Authors,Author(s) ID,Title,Year,Source title,Volume,Issue,Art. No.,Page start,Page end,Page count,Cited by,DOI,Link,Affiliations,Authors with affiliations,Abstract,Author Keywords,Index Keywords,Molecular Sequence Numbers,Chemicals/CAS,Tradenames,Manufacturers,Funding Details,Funding Text 1,Funding Text 2,Funding Text 3,Funding Text 4,Funding Text 5,Funding Text 6,Funding Text 7,Funding Text 8,Funding Text 9,Funding Text 10,References,Correspondence Address,Editors,Sponsors,Publisher,Conference name,Conference date,Conference location,Conference code,ISSN,ISBN,CODEN,PubMed ID,Language of Original Document,Abbreviated Source Title,Document Type,Publication Stage,Open Access,Source,EID
"Schleich B., Anwer N., Mathieu L., Wartzack S.","55308978800;7801563868;7005693283;6506007420;","Shaping the digital twin for design and production engineering",2017,"CIRP Annals - Manufacturing Technology","66","1",,"141","144",,644,"10.1016/j.cirp.2017.04.040","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018723536&doi=10.1016%2fj.cirp.2017.04.040&partnerID=40&md5=ba7049ec32c79b6298cc70581af3c133","Chair of Engineering Design, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstrasse 9, Erlangen, 91058, Germany; LURPA, ENS Cachan, University Paris-Sud, Universite Paris-Saclay, Cachan, 94235, France","Schleich, B., Chair of Engineering Design, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstrasse 9, Erlangen, 91058, Germany; Anwer, N., LURPA, ENS Cachan, University Paris-Sud, Universite Paris-Saclay, Cachan, 94235, France; Mathieu, L., LURPA, ENS Cachan, University Paris-Sud, Universite Paris-Saclay, Cachan, 94235, France; Wartzack, S., Chair of Engineering Design, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstrasse 9, Erlangen, 91058, Germany","The digitalization of manufacturing fuels the application of sophisticated virtual product models, which are referred to as digital twins, throughout all stages of product realization. Particularly, more realistic virtual models of manufactured products are essential to bridge the gap between design and manufacturing and to mirror the real and virtual worlds. In this paper, we propose a comprehensive reference model based on the concept of Skin Model Shapes, which serves as a digital twin of the physical product in design and manufacturing. In this regard, model conceptualization, representation, and implementation as well as applications along the product life-cycle are addressed. © 2017","Design; Digital twin; Tolerancing","Bridges; Design; Manufacture; Product design; Production; Virtual reality; Digital twin; Manufactured products; Physical products; Product life cycles; Product realization; Reference modeling; Tolerancing; Virtual products; Life cycle",,,,,,,,,,,,,,,,"Leu, M.C., ElMaraghy, H., Nee, A., Ong, S.K., Lanzetta, M., Putz, M., Zhu, W., Bernard, A., CAD Model Based Virtual Assembly Simulation, Planning and Training (2013) CIRP Annals – Manufacturing Technology, 62 (2), pp. 799-822; Roy, R., Stark, R., Tracht, K., Takata, S., Mori, M., Continuous Maintenance and the Future – Foundations and Technological Challenges (2016) CIRP Annals – Manufacturing Technology, 65 (2), pp. 667-688; Altintas, Y., Kersting, P., Biermann, D., Budak, E., Denkena, B., Lazoglu, I., Virtual Process Systems for Part Machining Operations (2014) CIRP Annals – Manufacturing Technology, 63 (2), pp. 585-605; Boschert, S., Rosen, R., Digital Twin – The Simulation Aspect (2016) Mechatronic Futures: Challenges and Solutions for Mechatronic Systems and their Designers, pp. 59-74. , P. Hehenberger D. Bradley Springer International Publishing, 2016; Rosen, R., von Wichert, G., Lo, G., Bettenhausen, K.D., About the Importance of Autonomy and Digital Twins for the Future of Manufacturing (2015) IFAC-Papers OnLine, 48 (3), pp. 567-572; Lu, S.C.-Y., Li, D., Cheng, J., Wu, C.L., A Model Fusion Approach to Support Negotiations during Complex Engineering System Design (1997) CIRP Annals – Manufacturing Technology, 46 (1), pp. 89-92; Maropoulos, P., Ceglarek, D., Design Verification and Validation in Product Lifecycle (2010) CIRP Annals – Manufacturing Technology, 59 (2), pp. 740-759; Quintana, V., Rivest, L., Pellerin, R., Venne, F., Kheddouci, F., Will Model-based Definition Replace Engineering Drawings Throughout the Product Lifecycle? A Global Perspective From Aerospace Industry (2010) Computers in Industry, 61 (5), pp. 497-508; Abramovici, M., Göbel, J.C., Dang, H.B., Semantic Data Management for the Development and Continuous Reconfiguration of Smart Products and Systems (2016) CIRP Annals – Manufacturing Technology, 65 (1), pp. 185-188; Glaessgen, E.H., Stargel, D.S., The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles (2012) Proceedings of the 53rd AIAA Structures, Structural Dynamics and Materials Conference 2012, Paper no. 1818; Grieves, M., Vickers, J., Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems (2017) Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, pp. 85-113. , F.-J. Kahlen S. Flumerfelt A. Alves Springer International Publishing, 2017; Mathieu, L., Ballu, A., GEOSPELLING: A Common Language for Geometrical Product Specification and Verification to Express Method Uncertainty (2003) Proc. of the 8th CIRP Int. Seminar on Computer Aided Tolerancing, pp. 70-79. , R. Wilhelm; Anwer, N., Ballu, A., Mathieu, L., The Skin Model, A Comprehensive Geometric Model for Engineering Design (2013) CIRP Annals – Manufacturing Technology, 62 (1), pp. 143-146; Nielsen, H.S., Recent Developments in International Organization for Standardization Geometrical Product Specification Standards and Strategic Plans for Future Work (2013) Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 227 (5), pp. 643-649; Schleich, B., Anwer, N., Mathieu, L., Wartzack, S., Skin Model Shapes: A New Paradigm Shift for Geometric Variations Modelling in Mechanical Engineering (2014) Computer-Aided Design, 50, pp. 1-15; ISO, 17450-1:2011, Geometrical Product Specifications (GPS) – General concepts – Part 1: Model for Geometrical Specification and Verification; Schleich, B., Anwer, N., Mathieu, L., Wartzack, S., Status and Prospects of Skin Model Shapes for Geometric Variations Management (2016) Procedia CIRP, 43, pp. 154-159","Schleich, B.; Chair of Engineering Design, Martensstrasse 9, Germany; email: schleich@mfk.fau.de",,,"Elsevier USA",,,,,00078506,,CIRAA,,"English","CIRP Ann Manuf Technol",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85018723536
"Zhang H., Liu Q., Chen X., Zhang D., Leng J.","57021448000;57222997697;57225125252;57212579186;57188970257;","A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line",2017,"IEEE Access","5",,"8082476","26901","26911",,266,"10.1109/ACCESS.2017.2766453","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032451877&doi=10.1109%2fACCESS.2017.2766453&partnerID=40&md5=dba7e7793ab1bbca95480ca355de87cc","Key Laboratory of Computer Integrated Manufacturing System, Guangdong University of Technology, Guangzhou, 510006, China","Zhang, H., Key Laboratory of Computer Integrated Manufacturing System, Guangdong University of Technology, Guangzhou, 510006, China; Liu, Q., Key Laboratory of Computer Integrated Manufacturing System, Guangdong University of Technology, Guangzhou, 510006, China; Chen, X., Key Laboratory of Computer Integrated Manufacturing System, Guangdong University of Technology, Guangzhou, 510006, China; Zhang, D., Key Laboratory of Computer Integrated Manufacturing System, Guangdong University of Technology, Guangzhou, 510006, China; Leng, J., Key Laboratory of Computer Integrated Manufacturing System, Guangdong University of Technology, Guangzhou, 510006, China","Various new national advanced manufacturing strategies, such as Industry 4.0, Industrial Internet, and Made in China 2025, are issued to achieve smart manufacturing, resulting in the increasing number of newly designed production lines in both developed and developing countries. Under the individualized designing demands, more realistic virtual models mirroring the real worlds of production lines are essential to bridge the gap between design and operation. This paper presents a digital twin-based approach for rapid individualized designing of the hollow glass production line. The digital twin merges physics-based system modeling and distributed real-time process data to generate an authoritative digital design of the system at pre-production phase. A digital twin-based analytical decoupling framework is also developed to provide engineering analysis capabilities and support the decision-making over the system designing and solution evaluation. Three key enabling techniques as well as a case study in hollow glass production line are addressed to validate the proposed approach. © 2013 IEEE.","Digital twin; Individualized designing; Mass individualization; Multi-view synchronization; Semi-physical simulation","Couplings; Data structures; Decision making; Developing countries; Glass; Manufacture; Optimization; Conferences; digital twin; individualized designing; mass individualization; Multi-views; Semi-physical simulations; Bridges",,,,,"National Natural Science Foundation of China, NSFC: 51675108, 51705091; Fundamental Research Funds for the Central Universities: 2015ZZ079; Science and Technology Planning Project of Guangdong Province: 2015B010128007, 2016A010106006","This work was supported in part by the National Natural Science Foundation of China under Grant 51675108 and Grant 51705091, in part by the Science and Technology Planning Project of Guangdong Province of China under Grant 2015B010128007 and Grant 2016A010106006, and in part by the Fundamental Research Funds for the Central Universities under Grant 2015ZZ079.",,,,,,,,,,"Gao, J., Yao, Y., Zhu, V.C.Y., Sun, L., Lin, L., Service-oriented manufacturing: A new product pattern and manufacturing paradigm (2011) J. 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Technol., 66 (1), pp. 141-144","Leng, J.; Key Laboratory of Computer Integrated Manufacturing System, China; email: jwleng@gdut.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85032451877
"Liu Q., Zhang H., Leng J., Chen X.","57222997697;57021448000;57188970257;57225125252;","Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system",2019,"International Journal of Production Research","57","12",,"3903","3919",,193,"10.1080/00207543.2018.1471243","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046619993&doi=10.1080%2f00207543.2018.1471243&partnerID=40&md5=72f1e191b90a651b4e3be23c7ff7c17e","Key Laboratory of Computer Integrated Manufacturing System, Guangdong University of Technology, Guangzhou, China; State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou, China","Liu, Q., Key Laboratory of Computer Integrated Manufacturing System, Guangdong University of Technology, Guangzhou, China, State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou, China; Zhang, H., Key Laboratory of Computer Integrated Manufacturing System, Guangdong University of Technology, Guangzhou, China; Leng, J., Key Laboratory of Computer Integrated Manufacturing System, Guangdong University of Technology, Guangzhou, China, State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou, China; Chen, X., State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou, China","Under a mass individualisation paradigm, the individualised design of manufacturing systems is difficult as it involves adaptive integrating both new and legacy machines for the formation of part families with uncertainty. A systematic virtual model mirroring the real world of manufacturing system is essential to bridge the gap between its design and operation. This paper presents a digital twin-driven methodology for rapid individualised designing of the automated flow-shop manufacturing system. The digital twin merges physics-based system modelling and distributed semi-physical simulation to provide engineering solution analysis capabilities and generates an authoritative digital design of the system at pre-production phase. An effective feedbacking of collected decision-support information from the intelligent multi-objective optimisation of the dynamic execution is presented to boost the applicability of the digital twin vision in the designing of AFMS. Finally, a bi-level iterative coordination mechanism is proposed to achieve optimal design performance for required functions of AFMS. A case study is conducted to prove the feasibility and effectiveness of the proposed methodology. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.","cyber-physical systems; digital twin; manufacturing system design; rapid individualised designing; semi-physical simulation","Bridges; Cyber Physical System; Decision support systems; Embedded systems; Iterative methods; Legacy systems; Machine shop practice; Multiobjective optimization; Coordination mechanisms; Design and operations; digital twin; Dynamic execution; Engineering solutions; Individualisation; rapid individualised designing; Semi-physical simulations; Manufacture",,,,,"National Natural Science Foundation of China, NSFC: 51675108, 51705091; Guangzhou Municipal Science and Technology Project: 201804020092; Fundamental Research Funds for the Central Universities: 2015ZZ079; Science and Technology Planning Project of Guangdong Province: 2015B010128007, 2016A010106006","This work was supported by the National Natural Science Foundation of China [grant numbers 51675108 and 51705091]; the Science and Technology Plan Project of Guangzhou [grant number 201804020092]; the Science and Technology Plan Project of Guangdong Province of China [grant numbers 2015B010128007 and 2016A010106006]; and the Fundamental Research Funds for the Central Universities [grant number 2015ZZ079].",,,,,,,,,,"Alam, K.M., El Saddik, A., C2PS: A Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems (2017) IEEE Access, 5, pp. 2050-2062; Amir, R., Grilo, I., Stackelberg versus Cournot Equilibrium (1999) Games and Economic Behavior, 26 (1), pp. 1-21; Atashpaz-Gargari, E., Lucas, C., Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition (2007) 2007 IEEE Congress on Evolutionary Computation, Vols 1-10, Proceedings, pp. 4661-4667; Baoding, L., Stackelberg-Nash Equilibrium for Multilevel Programming with Multiple Followers Using Genetic Algorithms (1998) Computers & Mathematics with Applications, 36 (7), pp. 79-89; Benayed, O., Blair, C.E., Computational Difficulties of Bilevel Linear Programming (1990) Operations Research, 38 (3), pp. 556-560; 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"Li L., Lei B., Mao C.","56094259800;56192494200;57201777643;","Digital twin in smart manufacturing",2022,"Journal of Industrial Information Integration","26",,"100289","","",,128,"10.1016/j.jii.2021.100289","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122625339&doi=10.1016%2fj.jii.2021.100289&partnerID=40&md5=23bf86e7e44e402c9ef122289fab710d","College of Mechatronics Engineering, North Minzu University, Yinchuan, 750021, China; School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Nanjing Automation Institute of Water Conservancy and Hydrology, Nanjing, 210012, China","Li, L., College of Mechatronics Engineering, North Minzu University, Yinchuan, 750021, China; Lei, B., School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Mao, C., Nanjing Automation Institute of Water Conservancy and Hydrology, Nanjing, 210012, China","Digital twin creates the virtual model of physical entity in digital way, promotes the interaction and integration of physical world and information world, and builds a reliable bridge for industrial information integration. With the rapid evolution of digital twin, the application of digital twin has found an increasingly wide utilization in smart manufacturing. In view of the practical problems encountered by the current smart manufacturing enterprises, this paper aims to carry out quantitative green performance evaluation of smart manufacturing (GPEoSM) driven by digital twin-based industrial information integration system. Based on the mapping between entity and model of smart manufacturing projects, the integration of digital twin information and the interaction of GPEoSM approach, a GPEoSM framework is constructed. According to the framework, a green performance evaluation case for smart manufacturing project of an air conditioning enterprise is carried out. The result shows that the digital twin driven GPEoSM framework is effective and enhances the green performance evaluation of smart manufacturing. © 2021","Complex networks; Digital twin; Green performance evaluation; PROMETHEE II; Set pair analysis; Smart manufacturing","Air conditioning; Complex networks; Information retrieval; Integration; Manufacture; Green performance evaluation; Information integration; Manufacturing projects; Performances evaluation; Physical information; Physical world; PROMETHEE-II; Set pair analysis; Smart manufacturing; Virtual models; Flow control",,,,,"National Natural Science Foundation of China, NSFC: 52165061; Natural Science Foundation of Ningxia Province: 2018HLZ07, 2020AAC03202, NZ17111, TGJC2018048","This research was funded by National Natural Science Foundation of China under grant number 52165061 Ningxia Natural Science Foundation under grant numbers 2020AAC03202 and NZ17111 , The Third Batch of Ningxia Youth Talents Supporting Program under grant number TGJC2018048 and University-enterprise Joint Project under grant number 2018HLZ07 . The author also thanks assistant editor and the anonymous reviewers for their useful suggestions which improve the quality of this research.","This research was funded by National Natural Science Foundation of China under grant number 52165061Ningxia Natural Science Foundation under grant numbers 2020AAC03202 and NZ17111, The Third Batch of Ningxia Youth Talents Supporting Program under grant number TGJC2018048 and University-enterprise Joint Project under grant number 2018HLZ07. The author also thanks assistant editor and the anonymous reviewers for their useful suggestions which improve the quality of this research.",,,,,,,,,"Frank, A., Dalenogare, L., Ayala, N., Industry 4.0 technologies: implementation patterns in manufacturing companies (2019) Int. J. Prod. Econ., 210, pp. 15-26; Cohen, Y., Naseraldin, H., Chaudhuri, A., P, F., Assembly systems in industry 4.0 era: a road map to understand assembly 4.0 (2019) Int. J. Adv. Manuf. 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Integr.",Article,"Final","",Scopus,2-s2.0-85122625339
"Lu Q., Parlikad A.K., Woodall P., Don Ranasinghe G., Xie X., Liang Z., Konstantinou E., Heaton J., Schooling J.","56717065200;9736080300;7003992161;57211138711;57210411313;55810419900;57190003646;57205622310;57189900369;","Developing a Digital Twin at Building and City Levels: Case Study of West Cambridge Campus",2020,"Journal of Management in Engineering","36","3","05020004","","",,97,"10.1061/(ASCE)ME.1943-5479.0000763","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081580153&doi=10.1061%2f%28ASCE%29ME.1943-5479.0000763&partnerID=40&md5=80fae89f7ab8ce83fa81ca1fb8ba1ed8","Bartlett School of Construction and Project Management, Univ. College London, 1-19 Torrington Place, London, WC1E 6BT, United Kingdom; Institute for Manufacturing, Univ. of Cambridge, 17 Charles Babbage Rd., Cambridge, CB3 0FS, United Kingdom; Centre for Smart Infrastructure and Construction, Univ. of Cambridge, Cambridge, CB2 1PZ, United Kingdom","Lu, Q., Bartlett School of Construction and Project Management, Univ. College London, 1-19 Torrington Place, London, WC1E 6BT, United Kingdom; Parlikad, A.K., Institute for Manufacturing, Univ. of Cambridge, 17 Charles Babbage Rd., Cambridge, CB3 0FS, United Kingdom; Woodall, P., Institute for Manufacturing, Univ. of Cambridge, 17 Charles Babbage Rd., Cambridge, CB3 0FS, United Kingdom; Don Ranasinghe, G., Institute for Manufacturing, Univ. of Cambridge, 17 Charles Babbage Rd., Cambridge, CB3 0FS, United Kingdom; Xie, X., Institute for Manufacturing, Univ. of Cambridge, 17 Charles Babbage Rd., Cambridge, CB3 0FS, United Kingdom; Liang, Z., Institute for Manufacturing, Univ. of Cambridge, 17 Charles Babbage Rd., Cambridge, CB3 0FS, United Kingdom; Konstantinou, E., Institute for Manufacturing, Univ. of Cambridge, 17 Charles Babbage Rd., Cambridge, CB3 0FS, United Kingdom; Heaton, J., Institute for Manufacturing, Univ. of Cambridge, 17 Charles Babbage Rd., Cambridge, CB3 0FS, United Kingdom; Schooling, J., Centre for Smart Infrastructure and Construction, Univ. of Cambridge, Cambridge, CB2 1PZ, United Kingdom","A digital twin (DT) refers to a digital replica of physical assets, processes, and systems. DTs integrate artificial intelligence, machine learning, and data analytics to create living digital simulation models that are able to learn and update from multiple sources as well as represent and predict the current and future conditions of physical counterparts. However, current activities related to DTs are still at an early stage with respect to buildings and other infrastructure assets from an architectural and engineering/construction point of view. Less attention has been paid to the operation and maintenance (O&M) phase, which is the longest time span in the asset life cycle. A systematic and clear architecture verified with practical use cases for constructing a DT would be the foremost step for effective operation and maintenance of buildings and cities. According to current research about multitier architectures, this paper presents a system architecture for DTs that is specifically designed at both the building and city levels. Based on this architecture, a DT demonstrator of the West Cambridge site of the University of Cambridge in the UK was developed that integrates heterogeneous data sources, supports effective data querying and analysis, supports decision-making processes in O&M management, and further bridges the gap between human relationships with buildings/cities. This paper aims at going through the whole process of developing DTs in building and city levels from the technical perspective and sharing lessons learned and challenges involved in developing DTs in real practices. Through developing this DT demonstrator, the results provide a clear roadmap and present particular DT research efforts for asset management practitioners, policymakers, and researchers to promote the implementation and development of DT at the building and city levels. © 2020 American Society of Civil Engineers.","Asset management; Building and city levels; Digital twin (DT); Operation and maintenance (O&M)","Artificial intelligence; Asset management; Buildings; Data Analytics; Decision making; Information management; Life cycle; Maintenance; Research and development management; Decision making process; Digital simulation models; Heterogeneous data sources; Infrastructure assets; Multi tier architecture; Operation and maintenance; System architectures; University of Cambridge; Digital twin",,,,,"Engineering and Physical Sciences Research Council, EPSRC; University of Cambridge","The research that contributed to this paper was funded by the EPSRC/Innovate UK Centre for Smart Infrastructure and Construction and the Centre for Digital Built Britain at the University of Cambridge.",,,,,,,,,,"Ahuja, V., Yang, J., Shankar, R., Benefits of collaborative ICT adoption for building project management (2009) Constr. 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"Lu Y., Huang X., Zhang K., Maharjan S., Zhang Y.","56166136300;8967036000;56404886100;36028399100;56602599100;","Low-Latency Federated Learning and Blockchain for Edge Association in Digital Twin Empowered 6G Networks",2021,"IEEE Transactions on Industrial Informatics","17","7","9170905","5098","5107",,82,"10.1109/TII.2020.3017668","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104195864&doi=10.1109%2fTII.2020.3017668&partnerID=40&md5=7c34e859fdad4bec7f2feceb454f2f36","Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China; School of Information and Communication Engineering, University of Electronic Science and Technology of China, Beijing, 610051, China; Department of Informatics, University of Oslo, Oslo, 0316, Norway; Simula Metropolitan Center for Digital Engineering, Oslo, 0167, Norway","Lu, Y., Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China; Huang, X., Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China; Zhang, K., School of Information and Communication Engineering, University of Electronic Science and Technology of China, Beijing, 610051, China; Maharjan, S., Department of Informatics, University of Oslo, Oslo, 0316, Norway, Simula Metropolitan Center for Digital Engineering, Oslo, 0167, Norway; Zhang, Y., Department of Informatics, University of Oslo, Oslo, 0316, Norway, Simula Metropolitan Center for Digital Engineering, Oslo, 0167, Norway","Emerging technologies, such as digital twins and 6th generation (6G) mobile networks, have accelerated the realization of edge intelligence in industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users hinder the effective application of federated learning in IIoT. In this article, we introduce the digital twin wireless networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multiagent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning methods. © 2005-2012 IEEE.","Blockchain; communication efficiency; digital twin; federated learning; wireless networks","Blockchain; Data privacy; Digital twin; E-learning; Learning systems; Numerical methods; Queueing networks; Reinforcement learning; Wireless networks; Distributed data processing; Emerging technologies; Learning frameworks; Multi-agent reinforcement learning; Optimization problems; Real-time data processing; Unreliable communication channels; Wireless connectivities; Industrial internet of things (IIoT)",,,,,"TICPSH202003016-ZC, TII-20-2614; National Natural Science Foundation of China, NSFC: 61941102","Manuscript received May 25, 2020; revised July 27, 2020 and August 10, 2020; accepted August 12, 2020. Date of publication August 18, 2020; date of current version April 2, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61941102, and in part by the Opening Project of Shanghai Trusted Industrial Control Platform under Grant TICPSH202003016-ZC. Paper no. TII-20-2614. (Corresponding author: Yan Zhang.) Yunlong Lu and Xiaohong Huang are with the Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China (e-mail: yunlong.lu@ieee.org; huangxh@bupt.edu.cn).",,,,,,,,,,"Sodhro, A.H., Pirbhulal, S., De Albuquerque, V.H.C., Artificial intelligence-driven mechanism for edge computing-based industrial applications (2019) Ieee Trans. Ind. Informat., 15 (7), pp. 4235-4243. , Jul; Kumar, N., Rahman, S.S., Dhakad, N., Fuzzy inference enabled deep reinforcement learning-based traffic light control for intelligent transportation system Ieee Trans. Intell. Transp. 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"Cheng Y., Zhang Y., Ji P., Xu W., Zhou Z., Tao F.","55330064800;57195409355;7005041399;57221159460;55538054700;12141248300;","Cyber-physical integration for moving digital factories forward towards smart manufacturing: a survey",2018,"International Journal of Advanced Manufacturing Technology","97","1-4",,"1209","1221",,75,"10.1007/s00170-018-2001-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045842061&doi=10.1007%2fs00170-018-2001-2&partnerID=40&md5=5a172289f8818e45b16ebfbb37afa27d","School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China; Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong; School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China; School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, 430070, China","Cheng, Y., School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong; Zhang, Y., School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China; Ji, P., Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong; Xu, W., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China; Zhou, Z., School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, 430070, China; Tao, F., School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China","The current study on digital factory (DF) meets some problems, such as disconnected manufacturing sites, independent digital models, isolated data, and non-self-controlled applications. In order to move current situation of DFs forward towards smart manufacturing, this paper attempts to present an overview of current digital situation of factories, and propose a systematical framework of cyber-physical integration in factories, with consideration of the concept of digital twin and the theory of manufacturing service. Particularly, the proposed framework includes four key issues, i.e., (a) fully interconnected physical elements integration, (b) faithful-mirrored virtual models integration, (c) all of elements/flows/businesses-covered data fusion, and (d) data-driven and application-oriented services integration. The corresponding implementable solutions of these four key issues are discussed in turn. As a reference, this paper is promising to bridge the gap in factories from current digital situation to smart manufacturing, so as to effectively facilitate their smart production. © 2018, Springer-Verlag London Ltd., part of Springer Nature.","Cyber-physical integration; Digital factory (DF); Digital twin; Manufacturing service; Smart manufacturing","Cyber Physical System; Data fusion; Factory automation; Flow control; Cyber physicals; Digital factories; Digital twin; Manufacturing service; Smart manufacturing; Manufacture",,,,,"National Natural Science Foundation of China, NSFC: 51475032, 51522501, G-YZ0K, XJ 2016004; Hong Kong Polytechnic University, PolyU","Funding information This work is partly supported by the National Natural Science Foundation of China (Grants 51522501 and 51475032) and Hong Kong Scholar Program (Project XJ 2016004 and G-YZ0K in The Hong Kong Polytechnic University).",,,,,,,,,,"Tao, F., Cheng, Y., Zhang, L., Nee, A.Y.C., Advanced manufacturing systems: socialization characteristics and trends (2017) J Intell Manuf, 28 (5), pp. 1079-1094; Davis, J., Edgar, T., Porter, J., Bernaden, J., Sarli, M., Smart manufacturing, manufacturing intelligence and demand-dynamic performance (2012) Comput Chem Eng, 47, pp. 145-156; Tao, F., Zhang, L., Nee, A.Y.C., Editorial for the special issue on big data and cloud technology for manufacturing (2016) Int J Adv Manuf Technol, 84 (1-4), pp. 1-3; Tao, F., Qi, Q.L., New IT driven service-oriented smart manufacturing: Framework and characteristics (2017) IEEE Trans Syst Man Cybern Syst, , https://doi.org/10.1109/TSMC.2017.2723764, Accepted on July.25 2017; Wang, L.H., Shih, A.J., Challenges in smart manufacturing (2016) J Manuf Syst, 40 (SI), p. 1; Bracht, U., Masurat, T., The digital factory between vision and reality (2005) Comput Ind, 56 (4), pp. 325-333; Americo, A., Almeida, A., Factory templates for digital factories framework (2011) Robot Comput Integr Manuf, 27 (4), pp. 755-771; Tao, F., Cheng, Y., Xu, L., Zhang, L., Li, B.H., CCIoT-CMfg: cloud computing and Internet of things based cloud manufacturing service system (2014) IEEE Trans Ind Inform, 10 (2), pp. 1435-1442; Xiang, F., Jiang, G.Z., Xu, L.L., Wang, N.X., The case-library method for service composition and optimal selection of big manufacturing data in cloud manufacturing system (2016) Int J Adv Manuf Technol, 84 (1-4), pp. 59-70; Tao, F., Cheng, J.F., Qi, Q.L., Zhang, M., Zhang, H., Sui, F.Y., Digital twin driven product design, manufacturing and service with big data (2017) Int J Adv Manuf Technol, 94 (9-12), pp. 3563-3576; Glaessagen, E., Stargel, D., (2012) The Digital Twin Paradigm for Future NASA and US Air Force Vehicles. 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Yoon, J.S., Shin, S.J., Suh, S.H., A conceptual framework for the ubiquitous factory (2012) Int J Prod Res, 50 (8), pp. 2174-2189; Zuehlke, D., SmartFactory-towards a factory-of-things (2010) Annu Rev Control, 34 (1), pp. 129-138; Radziwon, A., Bilberg, A., Bogers, M., The smart factory: exploring adaptive and flexible manufacturing solutions (2014) Procedia Eng, 69, pp. 1184-1190; Weyer, S., Meyer, T., Ohmer, M., Future modeling and simulation of CPS-based factories: an example from the automotive industry (2016) IFAC Papers Online, 49 (31), pp. 97-102; Tolio, T., Ceglarek, D., EIMaraghy, H.A., Fischer, A., Hu, S.J., Laperriere, L., Newman, S.T., Vancza, J., SPECIES-co-evolution of products, processes and production systems (2010) CIRP Ann Manuf Technol, 59 (2), pp. 672-693; Cheng, Y., Tao, F., Zhao, D., Zhang, L., Modeling of manufacturing service supply-demand matching hypernetwork in service-oriented manufacturing systems (2017) Robot Comput Integr Manuf, 45, pp. 59-72; Tao, F., Zhang, M., Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing (2017) IEEE Access, 5, pp. 20418-20427; Tao, F., Cheng, Y., Cheng, J.F., Zhang, M., Xu, W.J., Qi, Q.L., Theories and technologies for cyber-physical fusion in digital twin shop-floor (2017) Comput Integr Manuf Syst, 23 (8), pp. 1603-1611. , (in Chinese","Tao, F.; School of Automation Science and Electrical Engineering, China; email: ftao@buaa.edu.cn",,,"Springer London",,,,,02683768,,IJATE,,"English","Int J Adv Manuf Technol",Article,"Final","",Scopus,2-s2.0-85045842061
"Lu R., Brilakis I.","57194640091;8837673400;","Digital twinning of existing reinforced concrete bridges from labelled point clusters",2019,"Automation in Construction","105",,"102837","","",,72,"10.1016/j.autcon.2019.102837","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065641838&doi=10.1016%2fj.autcon.2019.102837&partnerID=40&md5=eb8619b2d5d7a3d5ac059918869928f6","School of Architecture, Building and Civil Engineering, Loughborough University, United Kingdom; Darwin College, University of Cambridge, United Kingdom; Laing O'Rourke Reader, Department of Engineering, University of Cambridge, United Kingdom","Lu, R., School of Architecture, Building and Civil Engineering, Loughborough University, United Kingdom, Darwin College, University of Cambridge, United Kingdom; Brilakis, I., Laing O'Rourke Reader, Department of Engineering, University of Cambridge, United Kingdom","The automation of digital twinning for existing reinforced concrete bridges from point clouds remains an unresolved problem. Whilst current methods can automatically detect bridge objects in point clouds in the form of labelled point clusters, the fitting of accurate 3D shapes to point clusters remains largely human dependent largely. 95% of the total manual modelling time is spent on customizing shapes and fitting them correctly. The challenges exhibited in the fitting step are due to the irregular geometries of existing bridges. Existing methods can fit geometric primitives such as cuboids and cylinders to point clusters, assuming bridges are comprised of generic shapes. However, the produced geometric digital twins are too ideal to depict the real geometry of bridges. In addition, none of the existing methods have explicitly demonstrated how to evaluate the resulting Industry Foundation Classes bridge data models in terms of spatial accuracy using quantitative measurements. In this article, we tackle these challenges by delivering a slicing-based object fitting method that can generate the geometric digital twin of an existing reinforced concrete bridge from four types of labelled point cluster. The quality of the generated models is gauged using cloud-to-cloud distance-based metrics. Experiments on ten bridge point cloud datasets indicate that the method achieves an average modelling distance of 7.05 cm (while the manual method achieves 7.69 cm), and an average modelling time of 37.8 s. This is a huge leap over the current practice of digital twinning performed manually. © 2019 Elsevier B.V.","BIM; BrIM; Digital twin; IFC; Point cloud data","Concrete bridges; Geometry; Object detection; Railroad bridges; BrIM; Digital twin; Existing reinforced concrete; Geometric primitives; Industry Foundation Classes - IFC; Irregular geometries; Point cloud data; Quantitative measurement; Reinforced concrete",,,,,"Engineering and Physical Sciences Research Council, EPSRC: 31109806.0007, EP/L010917/1","This research work is supported by EPSRC , Infravation SeeBridge project under Grant Number No. 31109806.0007 , and Cambridge Trimble Fund . We would like to thank for their supports. 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"Shim C.-S., Dang N.-S., Lon S., Jeon C.-H.","7103280900;57200211416;57208344286;57189060332;","Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model",2019,"Structure and Infrastructure Engineering","15","10",,"1319","1332",,71,"10.1080/15732479.2019.1620789","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066812168&doi=10.1080%2f15732479.2019.1620789&partnerID=40&md5=9ba1b50226ea35f615b5482a8a41562b","Chung-Ang University, Seoul, South Korea","Shim, C.-S., Chung-Ang University, Seoul, South Korea; Dang, N.-S., Chung-Ang University, Seoul, South Korea; Lon, S., Chung-Ang University, Seoul, South Korea; Jeon, C.-H., Chung-Ang University, Seoul, South Korea","Preventive maintenance is increasingly becoming an essential strategy in the bridge industry owing to its proactive advantage of maintaining the structural sustainability during its entire service life. Several in-use bridges lack an appropriate regular maintenance solution, leading to extra cost during the operation stage. This paper proposes a new generation of the bridge maintenance system by using a digital twin model concept for more reliable decision-making. A detailed solution is proposed in this work to enhance the bridge maintenance process using a parallel solution: a maintenance information management system based on a 3D information model in conjunction with a digital inspection system using image processing. Three-dimensional digital models are required to utilise information from the entire lifecycle of a project, including design and construction, operation, and maintenance, by continuously exchanging and updating data from each stakeholder. For the maintenance of prestressed concrete bridges, the twin models are defined and their uses are presented. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.","3D; 3D-scanning; bridge management system; deterioration; digital twin; image processing; information model; Maintenance; prestressed concrete bridges","3D modeling; Bridges; Concrete beams and girders; Concrete bridges; Decision making; Deterioration; Image enhancement; Image processing; Information management; Information theory; Life cycle; Maintenance; Prestressed concrete; 3D-scanning; Bridge maintenance systems; Bridge management system; Design and construction; digital twin; Information Modeling; Maintenance information; Three-dimensional digital models; Preventive maintenance",,,,,"Chung-Ang University, CAU; Ministry of Land, Infrastructure and Transport, MOLIT; Korea Agency for Infrastructure Technology Advancement, KAIA","This study was supported by a grant (18SCIP-B128570-02) from the Smart Civil Infrastructure Research Program funded by the Ministry of Land, Infrastructure and Transport (MOLIT) of the Korean government and the Korea Agency for Infrastructure Technology Advancement (KAIA) and was supported by the Chung-Ang University Research Scholarship Grants in 2012.",,,,,,,,,,"(2016), September)., The 2017 Infrastructure Report Card,. 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By the four founding organizations: The Canadian Construction Association (CCA) (2016) The Canadian Public Works Association (CPWA), the Canadian Society for Civil Engineering (CSCE), the Federation of Canadian Municipalities (FCM); Dang, N.S., Shim, C.S., BIM authoring for an image-based bridge maintenance system of existing cable-supported bridges (2018) IOP Conference Series: Earth and Environmental Science, 143, p. 012032; Glaessgen, E.H., Stargel, D.S., (2012) win, 23–26 April, , https://ntrs.nasa.gov/search.jsp?R=20120008178, April 16). The digital twin paradigm for future NASA and U.S. Air force vehicles. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Special Session on the Digital T, 2012, Honolulu, HI. Retrieved from; Hojlo, J., (2018), https://www.idc.com/getdoc.jsp?containerId=US43134418/, April)., IDC PlanScape: Digital twins for products, assets, and ecosystems,. Retrieved from; Jeon, C.H., Lee, J.B., Shim, C.S., Tensile test of corroded strand and maintenance of corroded prestressed concrete girders (2017) World Academy of Science, Engineering and Technology International Journal of Urban and Civil Engineering, 11 (10), pp. 1363-1367; Kellner, T., (2015), https://www.ge.com/reports/post/119300678660/wind-in-the-cloud-how-the-digital-wind-farm-will-2/, September 27)., GE Reports 2015: Wind the cloud? How the digital wind farm will make wind power 20 percent more efficient,. Retrieved from; Lee, G., Sacks, R., Eastman, C.M., Specifying parametric building object behavior (BOB) for a building information modeling system (2006) Automation in Construction, 15 (6), pp. 758-776; Matsumoto, M., Mitani, K., Sugimoto, M., Hashimoto, K., Miller, R., (2012), Paper presented at ‘Innovative InfrastructuresToward Human Urbanism, 18th Congress of IABSE’, Seoul, International Association for Bridge and Structural, &,). Innovative bridge assessment methods using image processing and infrared thermography technology., Engineering, 18(13), 1181–1188; Mohan, A., Poobal, S., Crack detection using image processing: A critical review and analysis (2017) Alexandria Engineering Journal, 57 (2), pp. 787-798; Nagrale, M.S.K., Bagde, M.S.T., Application of image processing for development of automated inspection system (2013) International Journal of Computing Engineering & Research, 3 (3), pp. 103-107; Oh, J., Lee, A., Oh, S., Choi, Y.J., Yi, B., Yang, H., Lee, J.H., Moon, Y.S., Bridge inspection robot system with novel image processing (2008) Proceedings of IABMAS, bridge maintenance, safety, management, health monitoring and informatics, , Koh and D. Frangopol (Eds.),., London: Taylor & Francis Group, &,. H.-M; Omar, T., Nehdi, M., (2016), p. 10. , Condition assessment and deterioration prediction tools for concrete bridges: A new look. Paper presented at ‘Resilient Infrastructure. London’, the Canadian Society of Civil Engineering, Ontario; Panetta, K., (2017), https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2018/, October 3)., Gartner top 10 strategic technology trends for 2018,. Retrieved from; Panetta, K., (2018), https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2019/, October 15)., Gartner top 10 strategic technology trends for 2019,. Retrieved from; Pereira, F.C., Pereira, C.E., Embedded image processing systems for automatic recognition of cracks using UAVs (2015) IFAC-PapersOnLine, 48 (10), pp. 16-21; Saydam, D., Frangopol, D.M., Time-dependent performance indicators of damaged bridge superstructures (2011) Engineering Structures, 33 (9), pp. 2458-2471; Shim, C.S., Kang, H.R., Dang, N.S., Lee, D.K., Development of BIM-based bridge maintenance system for cable-stayed bridges (2017) Smart Structures and Systems, 20 (6), pp. 697-708; Tolliver, D., Lu, P., Analysis of bridge deterioration rates: A case study of the northern plains region (2012) Journal of the Transportation Research Forum, 50 (2), pp. 87-100; Yang, S.I., Frangopol, D.M., Neves, L.C., Optimum maintenance strategy for deteriorating bridge structures based on lifetime functions (2006) Engineering Structures, 28 (2), pp. 196-206","Dang, N.-S.; Chung-Ang UniversitySouth Korea; email: dangngocson@cau.ac.kr",,,"Taylor and Francis Ltd.",,,,,15732479,,,,"English","Struct. Infrastructure Eng.",Article,"Final","",Scopus,2-s2.0-85066812168
"Yi Y., Yan Y., Liu X., Ni Z., Feng J., Liu J.","57203794125;55447489700;56113283800;7102668099;57205214478;57218350500;","Digital twin-based smart assembly process design and application framework for complex products and its case study",2021,"Journal of Manufacturing Systems","58",,,"94","107",,69,"10.1016/j.jmsy.2020.04.013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083854199&doi=10.1016%2fj.jmsy.2020.04.013&partnerID=40&md5=dc94bfc97fe693dbe83152f1c8f56367","School of Mechanical Engineering, Southeast University, Nanjing, 211198, China; Beijing Institute of Space Long March Vehicle, Beijing, 100076, China; Beijing Spacecrafts Limited Company, Beijing, 100094, China","Yi, Y., School of Mechanical Engineering, Southeast University, Nanjing, 211198, China; Yan, Y., Beijing Institute of Space Long March Vehicle, Beijing, 100076, China; Liu, X., School of Mechanical Engineering, Southeast University, Nanjing, 211198, China; Ni, Z., School of Mechanical Engineering, Southeast University, Nanjing, 211198, China; Feng, J., Beijing Spacecrafts Limited Company, Beijing, 100094, China; Liu, J., Beijing Spacecrafts Limited Company, Beijing, 100094, China","With rapid advances in new generation information technologies, digital twin (DT), and cyber-physical system, smart assembly has become a core focus for intelligent manufacturing in the fourth industrial evolution. Deep integration between information and physical worlds is a key phase to develop smart assembly process design that bridge the gap between product assembly design and manufacturing. This paper presents a digital twin reference model for smart assembly process design, and proposes an application framework for DT-based smart assembly with three layers. Product assembly station components are detailed in the physical space layer; two main modules, communication connection and data processing, are introduced in the interaction layer; and we discuss working mechanisms of assembly process planning, simulation, predication, and control management in the virtual space layer in detail. A case study shows the proposed approach application for an experimental simplified satellite assembly case using the DT-based assembly application system (DT-AAS) to verify the proposed application framework and method effectiveness. © 2020 The Society of Manufacturing Engineers","Application framework; Assembly process design; Complex product; Cyber-physical system; Digital twin; Smart assembly","Bridges; Data handling; Digital twin; Embedded systems; Manufacture; Process design; Product design; Application frameworks; Application systems; Assembly process planning; Communication connection; Design and application; Industrial evolution; Intelligent Manufacturing; Working mechanisms; Assembly",,,,,"41423010203, 41423010402, 61409230103; National Key Research and Development Program of China, NKRDPC: 2018YFB1701301","The authors gratefully acknowledge the financial support in part by the Preliminary Research Program of Equipment Development Department of China under Grant No. 41423010402 , Grant No. 41423010203 , Grant No. 61409230103 , and in part by the National Key Research and Development Program of China under Grant No. 2018YFB1701301 .",,,,,,,,,,"Yao, X., Jin, H., Zhang, J., Towards a wisdom manufacturing vision (2015) Int J Computer Integr Manuf, 28 (12), pp. 1291-1312; Zhou, J., Li, P., Zhou, Y., Wang, B., Zang, J., Meng, L., Toward new-generation intelligent manufacturing (2018) Engineering, 4 (1), pp. 11-20; Cohen, Y., Naseraldin, H., Chaudhuri, A., Pilati, F., Assembly systems in Industry 4.0 era: a road map to understand Assembly 4.0 (2019) Int J Adv Manuf Technol, 105 (9), pp. 4037-4054; ElMaraghy, H., ElMaraghy, W., Smart adaptable assembly systems (2016) Procedia CIRP, 44, pp. 4-13; Wang, L., Keshavarzmanesh, S., Feng, H., Buchal, R., Assembly process planning and its future in collaborative manufacturing: a review (2009) Int J Adv Manuf Technol, 41, pp. 132-144; Liu, J., Sun, Q., Cheng, H., Liu, X., Xiong, H., The state-of-the-art, connotation and developing trends of the products assembly technology (2018) J Mech Eng, 54 (11), pp. 2-28; Grieves, M., Virtually perfect: driving innovative and lean products through product lifecycle management (2011), Space Coast Press Cocoa Beach, Fla., USA ISBN:; Grieves, M., Vickers, J., Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems (2017) Transdisciplinary perspectives on complex systems: New findings and approaches, pp. 85-113. , F.-J. 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Gandomi, A., Haider, M., Beyond the hype: big data concepts, methods, and analytics (2015) Int J Inf Manage, 35 (2), pp. 137-144; Qi, Q., Tao, F., Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison (2018) IEEE Access, 6, pp. 3585-3593; Tao, F., Qi, Q., Liu, A., Kusiak, A., Data-driven smart manufacturing (2018) Int J Ind Manuf Syst Eng, 48, pp. 157-169; Liu, X., Ni, Z., Liu, J., Cheng, Y., Assembly process modeling mechanism based on the product hierarchy (2016) Int J Adv Manuf Technol, 82 (1-4), pp. 391-405; Yu, J., Xing, Y., Wang, C., Method for determination of geometric dismountability based on extended interference matrix (2011) J Mech Eng, 47 (21), pp. 146-156; Li, J., Yao, Y., Wang, P., Assembly accuracy prediction based on CAD model (2014) Int J Adv Manuf Technol, 75 (5-8), pp. 825-832; Tao, F., Zhang, M., Nee, A., Digital twin driven smart manufacturing (2019), Academic Press ISBN: 9780128176306","Yi, Y.; School of Mechanical Engineering, China; email: 230169307@seu.edu.cn",,,"Elsevier B.V.",,,,,02786125,,JMSYE,,"English","J Manuf Syst",Article,"Final","",Scopus,2-s2.0-85083854199
"Vrabič R., Erkoyuncu J.A., Butala P., Roy R.","35243856300;36124603700;7801584804;7402945388;","Digital twins: Understanding the added value of integrated models for through-life engineering services",2018,"Procedia Manufacturing","16",,,"139","146",,41,"10.1016/j.promfg.2018.10.167","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068447880&doi=10.1016%2fj.promfg.2018.10.167&partnerID=40&md5=a55ecb73ac25d15c9cb6cdc10058dd20","University of Ljubljana, Department of Manufacturing Systems and Control, Slovenia; Cranfield University, Through-life Engineering Services Centre, United Kingdom","Vrabič, R., University of Ljubljana, Department of Manufacturing Systems and Control, Slovenia; Erkoyuncu, J.A., Cranfield University, Through-life Engineering Services Centre, United Kingdom; Butala, P., University of Ljubljana, Department of Manufacturing Systems and Control, Slovenia; Roy, R., Cranfield University, Through-life Engineering Services Centre, United Kingdom","Digital twins are digital representations of physical products or systems that consist of multiple models from various domains describing them on multiple scales. By means of communication, digital twins change and evolve together with their physical counterparts throughout their lifecycle. Domain-specific partial models that make up the digital twin, such as the CAD model or the degradation model, are usually well known and provide accurate descriptions of certain parts of the physical asset. However, in complex systems, the value of integrating the partial models increases because it facilitates the study of their complex behaviours which only emerge from the interactions between various parts of the system. The paper proposes that the partial models of the digital twin share a common model space that integrates them through a definition of their interrelations and acts as a bridge between the digital twin and the physical asset. The approach is illustrated in a case of a mechatronic product - a differential drive mobile robot developed as a testbed for digital twin research. It is demonstrated how the integrated models add value to different stages of the lifecycle, allowing for evaluation of performance in the design stage and real-time reflection with the physical asset during its operation. © 2018 The Authors. Published by Elsevier B.V.","Digital twin; Modelling; Multi-domain model",,,,,,"Javna Agencija za Raziskovalno Dejavnost RS, ARRS: P2-0270; Ministrstvo za visoko šolstvo, znanost in tehnologijo: C3330-16-529000","This work was partially supported by the Ministry of Higher Education, Science and Technology of the Republic of Slovenia, grant no. C3330-16-529000, and by the Slovenian Research Agency, grant no. P2-0270.",,,,,,,,,,"Grieves, M., Digital Twin: Manufacturing Excellence through Virtual Factory Replication, , White paper; Porter, M.E., Heppelmann, J.E., How smart, connected products are transforming companies (2015) Harvard Business Review, 93 (10), pp. 96-114; Erkoyuncu, J., Birkin, G., Levy Carvalho do Lago, R., Williamson, A., Williams, H., Roy, R., Conceptualisation of Digital Twins in the Through-Life Engineering Services Environment, , unpublished book chapter; Grieves, M., Vickers, J., Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems (2017) Transdisciplinary Perspectives on Complex Systems, pp. 85-113. , Springer; Glaessgen, E., Stargel, D., The digital twin paradigm for future nasa and us air force vehicles 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, p. 1818; Tuegel, E.J., Ingraffea, A.R., Eason, T.G., Spottswood, S.M., Reengineering aircraft structural life prediction using a digital twin (2011) International Journal of Aerospace Engineering; Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F., Digital twin-driven product design, manufacturing and service with big data (2018) The International Journal of Advanced Manufacturing Technology, 94 (9-12), pp. 3563-3576; Quigley, M., Faust, J., Foote, T., Leibs, J., ROS: An open-source robot operating system (2009) ICRA Workshop on Open Source Software, 3, p. 5; Yamamoto, Y., Yun, X., Coordinating locomotion and manipulation of a mobile manipulator (1994) IEEE Transactions on Automatic Control, 39 (6), pp. 1326-1332; Dhaouadi, R., Hatab, A.A., Dynamic modelling of differential-drive mobile robots using lagrange and Newton-euler methodologies: A unified framework (2013) Advances in Robotics & Automation, 2 (2), pp. 1-7; Virgala, I., Kelemen, M., Experimental friction identification of a dc motor (2013) International Journal of Mechanics and Applications, 3 (1), pp. 26-30; Boschert, S., Rosen, R., (2016) Digital Twin-the Simulation Aspect, pp. 59-74. , Springer International Publishing; Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Nee, A.Y.C., Digital twin-driven product design framework (2018) International Journal of Production Research, pp. 1-19. , 0","Vrabič, R.; University of Ljubljana, Slovenia; email: rok.vrabic@fs.uni-lj.si","Castelluccio G.M.",,"Elsevier B.V.","7th International Conference on Through-life Engineering Services, TESconf 2018","6 November 2018 through 7 November 2018",,157772,23519789,,,,"English","Procedia Manuf.",Conference Paper,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85068447880
"Omer M., Margetts L., Hadi Mosleh M., Hewitt S., Parwaiz M.","57209220242;25636044400;57189458030;57193957118;57209221238;","Use of gaming technology to bring bridge inspection to the office",2019,"Structure and Infrastructure Engineering","15","10",,"1292","1307",,38,"10.1080/15732479.2019.1615962","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066905181&doi=10.1080%2f15732479.2019.1615962&partnerID=40&md5=97196c39075b67a4e01947cb85cc1c83","Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester, United Kingdom; ABM Engineers Multidimensional Consulting Firm, Karachi, Pakistan","Omer, M., Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester, United Kingdom; Margetts, L., Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester, United Kingdom; Hadi Mosleh, M., Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester, United Kingdom; Hewitt, S., Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester, United Kingdom; Parwaiz, M., ABM Engineers Multidimensional Consulting Firm, Karachi, Pakistan","This paper proposes a novel method for bridge inspection that essentially digitises bridges using Light Detection and Ranging (LIDAR) so that they can be later inspected in a virtual reality (VR) environment. The work uses conventional terrestrial LIDAR together with affordable VR hardware and freely available software development kits originally intended for authoring computer games. The resulting VR app is evaluated for a case study involving a typical masonry bridge, comparing the proposed technique with traditional inspection methods. The new approach promises to be highly effective in terms of interpretation of results, accessibility to critical areas and safety of inspectors. The work represents an important step towards the creation of digital twins of important assets in the built environment. Recent bridge collapse incidents have affected local economies, traffic congestion, and in some extreme cases led to a loss of life. The work is timely as law making agencies are paying greater attention to structural rehabilitation. This paper will be of particular interest to bridge engineers, construction professionals and law makers and could lead to future revisions of bridge inspection processes and standards. © 2019, © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","Bridge; digital twin; evaluation; inspection; LIDAR; maintenance; non-destructive methods; virtual reality","Computer games; Computer hardware; Inspection; Interactive computer graphics; Maintenance; Nondestructive examination; Optical radar; Software design; Traffic congestion; Virtual reality; Construction professionals; digital twin; evaluation; Freely available software; Inspection methods; Light detection and ranging; Nondestructive methods; Structural rehabilitation; Bridges",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/N026136/1; University of Manchester","This work was funded by a University of Manchester School of Engineering research scholarship and EPSRC through grant number EP/N026136/1.","This work was funded by a University of Manchester School of Engineering research scholarship and EPSRC through grant number EP/N026136/1. The authors would like to thank the School of Engineering at the University of Manchester for investing in the hardware and software required for this project.",,,,,,,,,"Adhikari, R., Moselhi, O., Bagchi, A., Image-based retrieval of concrete crack properties for bridge inspection (2014) Automation in Construction, 39, pp. 180-194; Agdas, D., Rice, J.A., Martinez, J.R., Lasa, I.R., Comparison of visual inspection and structural-health monitoring as bridge condition assessment method (2015) American Society of Civil Engineers, 26 (4), pp. 371-376; Bolourian, N., Soltania, M.M., Albahria, A.H., Hammad, A., High level framework for bridge inspection using LiDAR-equipped UAV (2017) The 34th International Symposium on Automation and Robotics in Construction (ISARC, , In,), Taipei, Taiwan, June 28th to July 1st, 2017; Branco, F.A., Brito, J., (2004) Handbook of concrete bridge management, , ASCE Press, Reston,Virginia, USA; Brown, M., (2017) Virtual reality engineering in business applications, wondershare, , https://filmora.wondershare.com/virtual-reality/virtual-reality-use-in-engineering.html, Retrieved from; Bryson, S., Virtual reality in scientific visualization (1993) Computers & Graphics, 17 (6), pp. 679-685; Byrne, M.O., Pakrashi, V., Schoefs, F., Ghosh, B., (2014) A comparison of image-based 3D recovery methods for underwater inspections, , 7th, European Workshop on Structural Health Monitoring, La Cité, Nantes, France:, &, July).,. 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"Hehr A., Norfolk M., Wenning J., Sheridan J., Leser P., Leser P., Newman J.A.","55211308300;35410180800;57192211552;57217713983;56226806900;57193888032;7401659694;","Integrating Fiber Optic Strain Sensors into Metal Using Ultrasonic Additive Manufacturing",2018,"JOM","70","3",,"315","320",,38,"10.1007/s11837-017-2709-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038631934&doi=10.1007%2fs11837-017-2709-8&partnerID=40&md5=cb8e9d19ff9ef51cb088c9ef7856a4cd","Fabrisonic LLC, Columbus, OH  43221, United States; Sheridan Solutions LLC, Saline, MI  48176, United States; NASA Langley Research Center, Hampton, VA  23681, United States","Hehr, A., Fabrisonic LLC, Columbus, OH  43221, United States; Norfolk, M., Fabrisonic LLC, Columbus, OH  43221, United States; Wenning, J., Fabrisonic LLC, Columbus, OH  43221, United States; Sheridan, J., Sheridan Solutions LLC, Saline, MI  48176, United States; Leser, P., NASA Langley Research Center, Hampton, VA  23681, United States; Leser, P., NASA Langley Research Center, Hampton, VA  23681, United States; Newman, J.A., NASA Langley Research Center, Hampton, VA  23681, United States","Ultrasonic additive manufacturing, a rather new three-dimensional (3D) printing technology, uses ultrasonic energy to produce metallurgical bonds between layers of metal foils near room temperature. This low temperature attribute of the process enables integration of temperature sensitive components, such as fiber optic strain sensors, directly into metal structures. This may be an enabling technology for Digital Twin applications, i.e., virtual model interaction and feedback with live load data. This study evaluates the consolidation quality, interface robustness, and load sensing limits of commercially available fiber optic strain sensors embedded into aluminum alloy 6061. Lastly, an outlook on the technology and its applications is described. © 2017, The Minerals, Metals & Materials Society.",,"3D printers; Aluminum alloys; Composite bridges; Fiber optics; Manufacture; Metals; Temperature; Aluminum alloy 6061; Enabling technologies; Fiber optic strain sensor; Metallurgical bonds; Near room temperature; Temperature sensitive; Three-dimensional (3D) printing; Ultrasonic additive manufacturing; Fiber optic sensors",,,,,"NNX16CL33C; National Aeronautics and Space Administration, NASA","The authors acknowledge financial support from NASA’s SBIR Office, NNX16CL33C. The authors are grateful for the support of NASA’s Convergent Aeronautics Solutions (CAS) Program Digital Twin Project.",,,,,,,,,,"Tuegel, E., Ingraffea, A., Eason, T., Spottswood, S., Int (2011) J. Aerosp. 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Technol, 211, p. 1650; Graff, K., Short, M., Norfolk (2010) Solid Freeform Fabrication Symposium Proceedings, , M, in; Hehr, A., Wenning, J., Terrani, K., Babu, S.S., Norfolk, M., (2017) JOM, 69, p. 485; (2017) Ormocer coating, , http://www.fbgs.com/technology/why-we-have-chosen-to-use-an-ormocer-coating/, FBGS, Accessed 23 Aug; (2017) Strain measurement with fiber Bragg grating sensors, , https://www.hbm.com/en/3189/tips-and-tricks-strain-measurement-with-fiber-bragg-grating-sensors/, M. Kreuzer, Accessed 2 Oct; Kong, C., Soar, R., (2005) Appl. Opt, 44, p. 6325; Li, D., Soar, R., (2008) J. Mater. Sci. Eng. A, 498, p. 421; Friel, R., Harris, R., (2010) Proc. Inst. Mech. Eng. Part L, 224, p. 31; Hehr, A., Dapino, M., (2015) Compos. Part B, 77, p. 199","Hehr, A.; Fabrisonic LLCUnited States; email: ahehr@fabrisonic.com",,,"Minerals, Metals and Materials Society",,,,,10474838,,JOMME,,"English","JOM",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85038631934
"Roy R.B., Mishra D., Pal S.K., Chakravarty T., Panda S., Chandra M.G., Pal A., Misra P., Chakravarty D., Misra S.","57219249946;7102272035;35570021100;55894496700;57220746256;57212083595;57203638167;36701722500;7004526342;7401768547;","Digital twin: current scenario and a case study on a manufacturing process",2020,"International Journal of Advanced Manufacturing Technology","107","9-10",,"3691","3714",,36,"10.1007/s00170-020-05306-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084031553&doi=10.1007%2fs00170-020-05306-w&partnerID=40&md5=9378449a6a3991d4a7cd18ffc58ae6e9","Department of Electronics and Communication Engineering, Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, India; Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur, India; Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India; TATA Consultancy Services Research & Innovation, Kolkata, India; Department of Mining Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India; Department of Computer Science & Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India","Roy, R.B., Department of Electronics and Communication Engineering, Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, India; Mishra, D., Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur, India; Pal, S.K., Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India; Chakravarty, T., TATA Consultancy Services Research & Innovation, Kolkata, India; Panda, S., TATA Consultancy Services Research & Innovation, Kolkata, India; Chandra, M.G., TATA Consultancy Services Research & Innovation, Kolkata, India; Pal, A., TATA Consultancy Services Research & Innovation, Kolkata, India; Misra, P., TATA Consultancy Services Research & Innovation, Kolkata, India; Chakravarty, D., Department of Mining Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India; Misra, S., Department of Computer Science & Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India","In the current scenario, industries need to have continuous improvement in their manufacturing processes. Digital twin (DT), a virtual representation of a physical entity, serves this purpose. It aims to bridge the prevailing gap between the design and manufacturing stages of a product by effective flow of information. This article aims to create a state-of-the-art review on various DTs with their application areas. The article also includes schematic representations of some of the DTs proposed in various fields. The concept is also represented by a case study based on a DT model developed for an advanced manufacturing process named friction stir welding. 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Iglesias, D., Bunting, P., Esquembri, S., Hollocombe, J., Silburn, S., Vitton-Mea, L., Balboa, I., Valcarcel, D., Digital twin applications for the JET divertor (2017) Fusion Eng Des, 125, pp. 71-76; Uhlemann, T.H.J., Schock, C., Lehmann, C., Freiberger, S., Steinhilper, R., The digital twin: demonstrating the potential of real time data acquisition in production systems (2017) Procedia Manuf, 9, pp. 113-120; Lynch, C., Big data: how do your data grow? 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IEEE Internet Comput, 19, pp. 7-11; Bandaru, S., Ng, A.H.C., Deb, K., Data mining methods for knowledge discovery in multi-objective optimization: part a - survey (2017) Expert Syst Appl, 70, pp. 139-159; Uhlemann, T.H.J., Lehmann, C., Steinhilper, R., The digital twin: realizing the cyber-physical production system for Industry 4.0 (2017) Procedia CIRP, 61, pp. 335-340; Thomas, W., Nicholas, E., Friction stir welding for the transportation industries (1997) Mater Des, 18, pp. 269-273; Mishra, R.S., Mahoney, M.W., Friction stir welding and processing (2007) ASM Int, 368. , https://doi.org/10.1361/fswp2007p001; Mehta, K.P., Sustainability in welding and processing (2019) Innovations in Manufacturing for Sustainability, pp. 125-145. , Springer International Publishing; Chen, C., Kovacevic, R., Jandgric, D., Wavelet transform analysis of acoustic emission in monitoring friction stir welding of 6061 aluminum (2003) Int J Mach Tools Manuf, 43, pp. 1383-1390; Yang, Y., Kalya, P., Landers, R.G., Krishnamurthy, K., Automatic gap detection in friction stir butt welding operations (2008) Int J Mach Tools Manuf, 48, pp. 1161-1169; Jene, T., Dobmann, G., Wagner, G., Eifler, D., Monitoring of the friction stir welding process to describe parameter effects on joint quality (2008) Mater Sci, 5454, pp. 1-11; Kumar, U., Yadav, I., Kumari, S., Kumari, K., Ranjan, N., Kesharwani, R.K., Jain, R., Pal, S.K., Defect identification in friction stir welding using discrete wavelet analysis (2015) Adv Eng Softw, 85, pp. 43-50; Kumari, S., Jain, R., Kumar, U., Yadav, I., Ranjan, N., Kumari, K., Kesharwani, R.K., Chakravarty, D., Defect identification in friction stir welding using continuous wavelet transform (2016) J Intell Manuf, 30, pp. 1-12; Fleming, P.A., Lammlein, D.H., Wilkes, D.M., Cook, G.E., Strauss, A.M., Delapp, D.R., Hartman, D.A., Misalignment detection and enabling of seam tracking for friction stir welding (2009) Sci Technol Weld Join, 14, pp. 93-96; Das Sukhomay Pal, B., Monitoring of friction stir welding process through signals acquired during the welding (2014) 5 Th International & 26Th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12th–14th, 2014, pp. 1-7. , IIT Guwahati, Assam, India; Das, B., Bag, S., Pal, S., Defect detection in friction stir welding process through characterization of signals by fractal dimension (2016) Manuf Lett, 7, pp. 6-10; Mishra, D., Roy, R.B., Dutta, S., Pal, S.K., Chakravarty, D., A review on sensor based monitoring and control of friction stir welding process and a roadmap to Industry 4.0 (2018) J Manuf Process, 36, pp. 373-397; Soundararajan, V., Atharifar, H., Kovacevic, R., Monitoring and processing the acoustic emission signals from the friction-stir-welding process (2006) Proc Inst Mech Eng Part B J Eng Manuf, 220, pp. 1673-1685; Roy, R.B., Ghosh, A., Bhattacharyya, S., Mahto, R.P., Kumari, K., Pal, S.K., Pal, S., Weld defect identification in friction stir welding through optimized wavelet transformation of signals and validation through X-ray micro-CT scan (2018) Int J Adv Manuf Technol, 99, pp. 623-633; Bhat, N.N., Kumari, K., Dutta, S., Pal, S.K., Pal, S., Friction stir weld classification by applying wavelet analysis and support vector machine on weld surface images (2015) J Manuf Process, 20, pp. 274-281","Pal, S.K.; Department of Mechanical Engineering, India; email: skpal@mech.iitkgp.ac.in",,,"Springer",,,,,02683768,,IJATE,,"English","Int J Adv Manuf Technol",Article,"Final","",Scopus,2-s2.0-85084031553
"Wu C., Zhou Y., Pereia Pessôa M.V., Peng Q., Tan R.","56085232400;57206268007;57218457993;7202851741;7201984456;","Conceptual digital twin modeling based on an integrated five-dimensional framework and TRIZ function model",2021,"Journal of Manufacturing Systems","58",,,"79","93",,34,"10.1016/j.jmsy.2020.07.006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089247209&doi=10.1016%2fj.jmsy.2020.07.006&partnerID=40&md5=be28e884ad68d855d5034d90bf0d9488","National Engineering Research Center for Technological Innovation Method and Tool/School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, China; Tianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles, Tianjin, 300130, China; China State Shipbuilding Corporation Limited 716th Research Institute, Lianyungang, 222061, China; Faculty of Engineering Technology, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, Netherlands; Department of Mechanical Engineering, University of Manitoba, Winnipeg, R3T 5V6, Canada","Wu, C., National Engineering Research Center for Technological Innovation Method and Tool/School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, China, Tianjin Key Laboratory of Power Transmission and Safety Technology for New Energy Vehicles, Tianjin, 300130, China; Zhou, Y., National Engineering Research Center for Technological Innovation Method and Tool/School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, China, China State Shipbuilding Corporation Limited 716th Research Institute, Lianyungang, 222061, China; Pereia Pessôa, M.V., Faculty of Engineering Technology, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, Netherlands; Peng, Q., Department of Mechanical Engineering, University of Manitoba, Winnipeg, R3T 5V6, Canada; Tan, R., National Engineering Research Center for Technological Innovation Method and Tool/School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, China","Digital twin represents a fusion of the informational and physical domains, to bridge the material and virtual worlds. Existing methods of digital twin modeling are mainly based on modular representation, which limits guidance of the modeling process. Such methods do not consider the components or operational rules of the digital twin in detail, thereby preventing designers from applying these methods in their fields. With the increasing application of digital twin to various engineering fields, an effective method of modeling a multi-dimensional digital twin at the conceptual level is required. To such an end, this paper presents a method for the conceptual modeling of a digital twin based on a five-dimensional digital twin framework to represent the complex relationship between digital twin objects and their attributes. The proposed method was used to model the digital twin of an intelligent vehicle at the concept level. © 2020 The Society of Manufacturing Engineers","Conceptual modeling; Digital twin; Function modeling; Intelligent vehicle; TRIZ","Digital integrated circuits; Complex relationships; Conceptual levels; Engineering fields; Function modeling; Method of modeling; Modeling process; Modular representations; Multi dimensional; Digital twin",,,,,"19JCTPJC57400; 18ZXZNGX00230; QN2018114; National Natural Science Foundation of China, NSFC: 51975181; Ministry of Science and Technology of the People's Republic of China, MOST: 2017IM040100","This work was supported by the Tianjin Science and Technology Major Project on New Generation Artificial Intelligence ( 18ZXZNGX00230 ), the Youth Fund for Science and Technology Research of Universities in Hebei Province, China ( QN2018114 ), Tianjin Science and Technology Commissioner Fund ( 19JCTPJC57400 ), the National Natural Science Foundation, China ( 51975181 ) and the Ministry of Science and Technology’s Methodology Program, China ( 2017IM040100 ).",,,,,,,,,,"Tao, F., Zhang, M., Nee, A., Digital twin driven smart manufacturing (2019), Elsevier Amsterdam, the Netherlands; Zhuang, C.B., Liu, J.H., Xiong, H., Ding, X.H., Liu, S.L., Wen, G., Connotation, architecture, and trends of product digital twin (2017) Comput Integr Manuf Syst, 23, pp. 753-768. , (in Chinese); Tao, F., Zhang, M., Cheng, J.F., Qi, Q.L., Digital twin workshop: a new paradigm for future workshop (2017) Comput Integr Manuf Syst, 23, pp. 1-9. , (in Chinese); Zhu, Z., Liu, C., Xu, X., Visualisation of the digital twin data in manufacturing by using augmented reality (2019) Procedia CIRP, 81, pp. 898-903; Tao, F., Liu, W.R., Zhang, M., Hu, T.L., Qi, Q.L., Zhang, H., Five- dimension digital twin model and its ten applications (2019) Comput Integr Manuf Syst, 25, pp. 1-18. , (in Chinese); Siemens, Industry 4.0 and Siemens’ way to industry digitalization (2018), https://max.book118.com/html/2018/0617/173123268.shtm, (Accessed 17 June 2018) (in Chinese); Apriso, Digital twin: manufacturing excellence through virtual factory replication (2014), http://www.apriso.com, (Accessed 6 May 2014); Huang, S.H., Wang, G.X., Yan, Y., Fang, X.B., Blockchain-based data management for digital twin of product (2020) J Manuf Syst, 54, pp. 361-371; Liu, Q., Liu, B., Wang, G., Zhang, C., Liang, Z.X., Zhang, P., Research on digital twin: model, problem, and progress (2019) J Hebei Univ Sci Technol, 40, pp. 68-78. , (in Chinese); Yang, L.Y., Chen, S.Y., Wang, X., Zhang, J., Wang, C.H., Digital twins and parallel systems: state of the art, comparisons and prospect (2019) Acta Autom Sin, 45, pp. 2001-2031. , (in Chinese); Schleich, B., Anwer, N., Mathieu, L., Wartzack, S., Shaping the digital twin for design and production engineering (2017) CIRP Ann, 66, pp. 141-144; Geng, J.G., Yao, L., Yan, H.J., A brief analysis of the digital twin's concept, model, and application (2019) Civ-Milit Integr Cyberspace, 2, pp. 60-63. , https://doi.org/CNKI:SUN:WXJM.0.2019-02-015, (in Chinese); Tuegel, E.J., Ingraffea, A.R., Eason, T., Spottswood, S.M., Reengineering aircraft structural life prediction using a digital twin (2011) Int J Aerosp Eng, p. 154798; Jean, L.G., Claire, L., François, T., Régis, L., A digital twin-based approach for the management of geometrical deviations during assembly processes (2020) J Manuf Syst, , (in press); Zhuang, C.B., Gong, J.C., Liu, J.H., Digital twin-based assembly data management and process traceability for complex products (2020) J Manuf Syst, , (in press); Tao, F., Zhang, M., Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing (2017) IEEE Access, 5, pp. 20418-20427; Tao, F., Zhang, M., Liu, Y.S., Nee, A.Y.C., Digital twin driven prognostics and health management for complex equipment (2018) CIRP Ann, 67, pp. 169-172; Tao, F., Cheng, J.F., Qi, Q.L., Zhang, M., Zhang, H., Sui, F.Y., Digital twin-driven product design, manufacturing, and service with big data (2018) Int J Adv Manuf Technol, 94, pp. 3563-3576; Tao, F., Sui, F.Y., Liu, A., Qi, Q.L., Zhang, M., Song, B.Y., Digital twin-driven product design framework (2018) Int J Prod Res, 1, pp. 1-19; Tao, F., Liu, W.R., Liu, J.H., Liu, X.J., Liu, Q., Qu, T., Digital twin and its potential application exploration (2018) Comput Integr Manuf Syst, 24, pp. 1-18. , (in Chinese); Kong, T.X., Hu, T.L., Zhou, T.T., Ye, Y.X., Data construction method for the applications of workshop digital twin system (2020) J Manuf Syst, , (in press); Schroeder, G.N., Steinmetz, C., Pereira, C.E., Espindola, D.B., Digital twin data modeling with automation ML and a communication methodology for data exchange (2016) IFAC-Pap Online, 49, pp. 12-17; Aivaliotisa, P., Georgouliasa, K., Arkoulia, Z., Makris, S., Methodology for enabling digital twin using advanced physics-based modelling in predictive maintenance (2019) Procedia CIRP, 81, pp. 417-422; Moreno, A., Velez, G., Ardanza, A., Barandiaran, I., Infante, A.R.D., Chopitea, R., Virtualisation process of a sheet metal punching machine within the Industry 4.0 vision (2017) Int J Interact Des Manuf, 11, pp. 365-373; Sun, H.B., Pan, J.L., Zhang, J.D., Mo, R., Digital twin model for cutting tools in machining process (2019) Comput Integr Manuf Syst, 25, pp. 1474-1480. , (in Chinese); Siemens, The connotation of the “digital twin” strategy and the construction of digital future (2017), http://www.bimcn.org/BIMguandian/201708258905.html, (Accessed 25 August 2017) (in Chinese); Ebrahimi, A., Challenges of developing a digital twin model of renewable energy generators (2019) 2019 IEEE 28th international symposium on industrial electronics (ISIE), IEEE, pp. 1059-1066; Liu, L.Y., Du, H.X., Wang, H.F., Liu, T.Y., The construction and application of digital twin systems for production processes in the workshop (2019) Comput Integr Manuf Syst, 25, pp. 1536-1545. , (in Chinese); Park, K.T., Nam, Y.W., Lee, H.S., Im, S.J., Noh, S.D., Son, J.Y., Design and implementation of a digital twin application for a connected micro smart factory (2019) Int J Comput Integr Manuf, pp. 1-19; Bao, J.S., Guo, D.S., Li, J., Zhang, J., The modelling and operations for the digital twin in the context of manufacturing (2019) Enterp Inf Syst, 13, pp. 534-556; Shangguan, D.S., Chen, L., Ding, J., A hierarchical digital twin model framework for dynamic cyber-physical system design (2019) Proceedings of the 5th international conference on mechatronics and robotics engineering, ACM, pp. 123-129; Uhlemann, T.H.J., Lehmann, C., Steinhilper, R., The digital twin: realizing the cyber-physical production system for industry 4.0 (2017) Procedia CIRP, 61, pp. 335-340; Borangiu, T., Oltean, E., Răileanu, S., Anton, F., Anton, S., Iacob, I., Embedded digital twin for ARTI-type control of semi-continuous production processes (2019) International workshop on service orientation in Holonic and multi-agent manufacturing, pp. 113-133; Redelinghuys, A.J.H., Basson, A.H., Kruger, K., A six-layer architecture for the digital twin: a manufacturing case study implementation (2019) J Intell Manuf, pp. 1-20; Glaessgen, E., Stargel, D., The digital twin paradigm for future NASA and U.S. Air Force vehicles (2012) Proceedings of the 53rd structures, structural dynamics, and materials conference, pp. 1-14; Tao, F., Ma, X., Hu, T.L., Huang, Z.G., Cheng, J.F., Qi, Q.L., Research on digital twin standard system (2019) Comput Integr Manuf Syst, 25, pp. 2406-2418; Zhang, M., Tao, F., Nee, A.Y.C., Digital twin enhanced dynamic job-shop scheduling (2020) J Manuf Syst, , (in press); Zhou, Y.C., Wu, C.L., Sun, J.G., Liu, F., Li, H., A function model construction method based on digital twin for intelligent products (2019) Comput Integr Manuf Syst, 25, pp. 1393-1404. , (in Chinese); Tan, R.H., TRIZ and its applications (2010), Higher Education Press Beijing (in Chinese); Pahl, G., Beitz, W., Feldhusen, J., Grote, K.H., Engineering design: a systematic approach (2007), Springer-Verlag Berlin; Pubs, F.P., Integration definition for function modelling (IDEF0) (1993), Federal Information Processing Standards Publication; Hubka, V., Eder, W., Theory of technical systems: a total concept theory for engineering design (1988), Springer-Verlag Berlin; Tan, R.H., Yuan, C.Y., Cao, G.Z., Zhang, R.H., Function model for products existed using reverse fishbone (2003) J Eng Des, 10, pp. 198-201. , (in Chinese); Yan, H.B., Cao, G.Z., Tan, R.H., Wang, K., Research and application of problem recognition based on triaxial analysis and functional model fusion (2019) Sci Technol Manag Res, 2, pp. 270-274; Zhang, H.G., Innovation design: systematic innovation based on TRIZ (2017), China Machine Press Beijing (in Chinese); Qi, Q.L., Tao, F., Hu, T.L., Anwerc, N., Liu, A., Wei, Y.L., Enabling technologies and tools for digital twin (2020) J Manuf Syst, , (in press); DebRoy, T., Zhang, W., Turner, J., Babud, S.S., Building digital twins of 3D printing machines (2017) Scr Mater, 35, pp. 119-124","Wu, C.; National Engineering Research Center for Technological Innovation Method and Tool/School of Mechanical Engineering, China; email: wuchunlong@hebut.edu.cn",,,"Elsevier B.V.",,,,,02786125,,JMSYE,,"English","J Manuf Syst",Article,"Final","",Scopus,2-s2.0-85089247209
"Autiosalo J.","57203088736;","Platform for industrial internet and digital twin focused education, research, and innovation: Ilmatar the overhead crane",2018,"IEEE World Forum on Internet of Things, WF-IoT 2018 - Proceedings","2018-January",,,"241","244",,34,"10.1109/WF-IoT.2018.8355217","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050503484&doi=10.1109%2fWF-IoT.2018.8355217&partnerID=40&md5=b8d8fea780c364dc536620351f870242","Department of Mechanical Engineering, Aalto University School of Engineering, Espoo, Finland","Autiosalo, J., Department of Mechanical Engineering, Aalto University School of Engineering, Espoo, Finland","The paper presents first experiences on an overhead crane platform targeted for university education, research, and innovation purposes. The main contributions feature a description of projects from the first year after the inauguration of the crane platform. To provide a basic perception on the potential of the platform, the paper presents the basic technical properties as well as opportunities and challenges of the crane platform. Digital Twin concept has been selected as a focus of the research activities on the platform. Hence, the paper reviews status of the term Digital Twin. Results describe experience-based observations on how university should manage an education, research, and innovation platform while collaborating with industry partners. © 2018 IEEE.","Cyber-Physical Systems; Digitalization; Internet of Things; laboratory; mechanical engineering; mechatronics","Bridge cranes; Cyber Physical System; E-learning; Embedded systems; Gantry cranes; Industrial research; Laboratories; Mechanical engineering; Mechatronics; Digitalization; First year; Industrial internets; Innovation platforms; Overhead crane; Research activities; Technical properties; University education; Internet of things",,,,,,,,,,,,,,,,"Bradley, D., Russell, D., Ferguson, I., Isaacs, J., Macleod, A., White, R., The Internet of Things - The future or the end of mechatronics (2015) Mechatronics, 27, pp. 57-74; Verner, I., Cuperman, D., Fang, A., Reitman, M., Romm, T., Balikin, G., Robot Online Learning Through Digital Twin Experiments: A Weightlifting Project (2018) Online Engineering & Internet of Things: Proceedings of the 14th International Conference on Remote Engineering and Virtual Instrumentation REV 2017, pp. 307-314. , held 15-17 March 2017, Columbia University, New York, USA, M. E. Auer and D. G. Zutin, Eds. Cham: Springer International Publishing; Lin, Y., Kämäräinen, T., Di Francesco, M., Ylä-Jääski, A., Performance evaluation of remote display access for mobile cloud computing (2015) Comput. Commun, 72, pp. 17-25; Shafto, M., DRAFT Modeling, Simulation, Information Technology & Processing Roadmap (2010) NASA, Technology Area 11; Grieves, M., Vickers, J., Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems (2017) Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, pp. 85-113. , F.-J. Kahlen, S. Flumerfelt, and A. Alves, Eds. Cham: Springer International Publishing; Främling, K., Holmström, J., Ala-Risku, T., Kärkkäinen, M., (2003) Product Agents for Handling Information about Physical Objects, , Lab. Inf. Process. Sci., Helsinki Univ. Technol., Finland, Rep. TKO-B; Ríos, J., Hernández, J.C., Oliva, M., Mas, F., Product avatar as digital counterpart of a physical individual product: Literature review and implications in an aircraft (2015) Adv. Transdiscipl. Eng, 2, pp. 657-666; Abramovici, M., Göbel, J.C., Savarino, P., Reconfiguration of smart products during their use phase based on virtual product twins (2017) CIRP Ann. - Manuf. Technol, 66 (1), pp. 165-168; Nitti, M., Pilloni, V., Colistra, G., Atzori, L., The Virtual Object as a Major Element of the Internet of Things: A Survey (2016) IEEE Commun. Surv. Tutorials, 18 (2), pp. 1228-1240; Främling, K., Ala-Risku, T., Kärkkäinen, M., Holmström, J., Agentbased model for managing composite product information (2006) Comput. Ind, 57 (1), pp. 72-81; El Kaed, C., Khan, I., Hossayni, H., Nappey, P., SQenloT: Semantic query engine for industrial Internet-of-Things gateways (2017) 2016 IEEE 3rd World Forum Internet Things, WF-IoT 2016, pp. 204-209; Schroeder, G.N., Steinmetz, C., Pereira, C.E., Espindola, D.B., Digital Twin Data Modeling with AutomationML and a Communication Methodology for Data Exchange (2016) IFACPapersOnLine, 49 (30), pp. 12-17; Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F., Digital twindriven product design, manufacturing and service with big data (2017) Int. J. Adv. Manuf. Technol, pp. 1-14; Boschert, S., Roland, R., Digital Twin-The Simulation Aspect (2016) Mechatronic Futures, pp. 59-74. , P. Hehenberger and D. Bradley, Eds. Springer, Cham; Kiviluoma, P., Kuosmanen, P., Mechatronics Education at Aalto University (2013) 13th International Symposium ""tOPICAL PROBLEMS in the FIELD of ELECTRICAL and POWER ENGINEERING"", Pärnu, Estonia, pp. 48-52","Autiosalo, J.; Department of Mechanical Engineering, Finland; email: juuso.autiosalo@aalto.fi",,"Credits;Darkmatter;et al.;Fuzhou IoT Lab;Nanyang Technological University, School of Electrical and Electronic Engineering;Rohde and Schwarz","Institute of Electrical and Electronics Engineers Inc.","4th IEEE World Forum on Internet of Things, WF-IoT 2018","5 February 2018 through 8 February 2018",,136296,,9781467399449,,,"English","IEEE World Forum Internet Things, WF-IoT - Proc.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85050503484
"Vivi Q.L., Parlikad A.K., Woodall P., Ranasinghe G.D., Heaton J.","56717065200;9736080300;7003992161;57211138711;57205622310;","Developing a dynamic digital twin at a building level: Using Cambridge campus as case study",2019,"International Conference on Smart Infrastructure and Construction 2019, ICSIC 2019: Driving Data-Informed Decision-Making",,,,"67","75",,32,"10.1680/icsic.64669.067","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085260931&doi=10.1680%2ficsic.64669.067&partnerID=40&md5=92fd111ffe7fab73dbecdf505f571d5c","Institute for Manufacturing (IfM), University of Cambridge, Cambridge, United Kingdom","Vivi, Q.L., Institute for Manufacturing (IfM), University of Cambridge, Cambridge, United Kingdom; Parlikad, A.K., Institute for Manufacturing (IfM), University of Cambridge, Cambridge, United Kingdom; Woodall, P., Institute for Manufacturing (IfM), University of Cambridge, Cambridge, United Kingdom; Ranasinghe, G.D., Institute for Manufacturing (IfM), University of Cambridge, Cambridge, United Kingdom; Heaton, J., Institute for Manufacturing (IfM), University of Cambridge, Cambridge, United Kingdom","A Digital Twin (DT) refers to a digital replica of physical assets, processes and systems. DTs integrate artificial intelligence, machine learning and data analytics to create dynamic digital models that are able to learn and update the status of the physical counterpart from multiple sources. A DT, if equipped with appropriate algorithms will represent and predict future condition and performance of their physical counterparts. Current developments related to DTs are still at an early stage with respect to buildings and other infrastructure assets. Most of these developments focus on the architectural and engineering/construction point of view. Less attention has been paid to the operation & maintenance (O&M) phase, where the value potential is immense. A systematic and clear architecture verified with practical use cases for constructing a DT is the foremost step for effective operation and maintenance of assets. This paper presents a system architecture for developing dynamic DTs in building levels for integrating heterogeneous data sources, support intelligent data query, and provide smarter decision-making processes. This will further bridge the gaps between human relationships with buildings/regions via a more intelligent, visual and sustainable channels. This architecture is brought to life through the development of a dynamic DT demonstrator of the West Cambridge site of the University of Cambridge. Specifically, this demonstrator integrates an as-is multi-layered IFC Building Information Model (BIM), building management system data, space management data, real-time Internet of Things (IoTj-based sensor data, asset registry data, and an asset tagging platform. The demonstrator also includes two applications: (1) improving asset maintenance and asset tracking using Augmented Reality (AR); and (2) equipment failure prediction. The long-term goals of this demonstrator are also discussed in this paper. © International Conference on Smart Infrastructure and Construction 2019, ICSIC 2019: Driving Data-Informed Decision-Making.",,"Architectural design; Artificial intelligence; Augmented reality; Data Analytics; Decision making; Digital twin; Dynamics; Intelligent buildings; Maintenance; Memory architecture; Search engines; Building Information Model - BIM; Building management system; Decision making process; Equipment failure predictions; Heterogeneous data sources; Infrastructure assets; Operation and maintenance; University of Cambridge; Information management",,,,,,,,,,,,,,,,"Arslan, M, Building Information Modeling (BIM) Enabled Facilities Management Using Hadoop Architecture (2017) Portland International Conference, Management of Engineering and Technology (PICMET), pp. 1-7. , IEEE, Portland; Beyer, M, (2017) Gartner Research Report: Magic Quadrant for Data Integration Tools, , Gartner research report G00314940; 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Hu, ZZ, BIM-based integrated delivery technologies for intelligent MEP management in the operation and maintenance phase (2018) Advances in engineering software, 115, pp. 1-16. , https://doi.org/10.1016/j.advengsoft.2017.08.007; Ko, CH, Web-based radio frequency identification facility management systems (2013) Structure and infrastructure engineering, 9 (5), pp. 465-480. , https://doi.org/10.1080/15732479.2010.546804; Lanzisera, S, Radio frequency time-of-flight distance measurement for low-cost wireless sensor localization (2011) IEEE Sensors Journal, 11 (3), pp. 837-845; Lee, J, An integrated approach to intelligent urban facilities management for real-time emergency response (2013) Automation in construction, 30, pp. 256-264. , https://doi.org/10.1016/j.autcon.2012.11.008; Lewis, FL, Wireless sensor networks (2004) Smart environments: technologies, protocols, and applications, pp. 11-46; Lin, YC, Developing mobile BIM/2D barcodebased automated facility management system (2014) The Scientific World Journal, 2014. , http://dx.doi.org/10.l155/2014/374735; 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(2018) Monnit Ethernet Gateway, , https://www.monnit.com/Products/Gatewavs/Ethernet-Gatewav/Wireless-Ethernet-Gatewavs, Monnit (c) (accessed 15/10/2018); Motamedi, A, Knowledge-assisted BIM-based visual analytics for failure root cause detection in facilities management (2014) Automation in construction, 43, pp. 73-83. , https://d0i.0rg/10.1016/i.autcon.2014.03.012; (2017) Data for the Public Good, , https://www.nic.org.uk/wpcontent/uploads/Data-for-the-Public-Good-NIC-Report.pdf, National Infrastructure Commission (NIC) (accessed 10/03/2018); (1998) Stewardship of federal facilities, A Proactive Strategy for Managing the nation's Public Assets National Research Council, , NRC National Academies Press, Washington, DC; Otto, B, How to design the master data architecture: Findings from a case study at Bosch (2012) International Journal of Information Management, 32 (4), pp. 337-346; Otto, B, Toward a functional reference model for master data quality management (2012) Information Systems and E-Business Management, 10 (3), pp. 395-425; 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Yick, J, Wireless sensor network survey (2008) Computer networks, 52 (12), pp. 2292-2330","Vivi, Q.L.; Institute for Manufacturing (IfM), United Kingdom","DeJong M.J.Schooling J.M.Viggiani G.M.B.",,"ICE Publishing","2nd International Conference on Smart Infrastructure and Construction: Driving Data-Informed Decision-Making, ICSIC 2019","1 July 2019 through 3 July 2019",,164083,,9780727764669,,,"English","Int. Conf. Smart Infrastruct. Constr., ICSIC : Driv. Data-Inf. Decis.-Mak.",Conference Paper,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85085260931
"Wang Q., Jiao W., Wang P., Zhang Y.","57209691307;57209686494;57220187855;56088829600;","Digital Twin for Human-Robot Interactive Welding and Welder Behavior Analysis",2021,"IEEE/CAA Journal of Automatica Sinica","8","2","9269521","334","343",,30,"10.1109/JAS.2020.1003518","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097130846&doi=10.1109%2fJAS.2020.1003518&partnerID=40&md5=049a99cdd207e65d3f931a60810308e4","Institute for Sustainable Manufacturing, University of Kentucky, Department of Electrical and Computer Engineering, Lexington, United States; Institute for Sustainable Manufacturing, Department of Electrical and Computer Engineering; Department of Mechanical Engineering, University of Kentucky, Lexington, United States","Wang, Q., Institute for Sustainable Manufacturing, University of Kentucky, Department of Electrical and Computer Engineering, Lexington, United States; Jiao, W., Institute for Sustainable Manufacturing, University of Kentucky, Department of Electrical and Computer Engineering, Lexington, United States; Wang, P., Institute for Sustainable Manufacturing, Department of Electrical and Computer Engineering, Department of Mechanical Engineering, University of Kentucky, Lexington, United States; Zhang, Y., Institute for Sustainable Manufacturing, University of Kentucky, Department of Electrical and Computer Engineering, Lexington, United States","This paper presents an innovative investigation on prototyping a digital twin (DT) as the platform for human-robot interactive welding and welder behavior analysis. This human-robot interaction (HRI) working style helps to enhance human users' operational productivity and comfort; while data-driven welder behavior analysis benefits to further novice welder training. This HRI system includes three modules: 1) a human user who demonstrates the welding operations offsite with her/his operations recorded by the motion-tracked handles; 2) a robot that executes the demonstrated welding operations to complete the physical welding tasks onsite; 3) a DT system that is developed based on virtual reality (VR) as a digital replica of the physical human-robot interactive welding environment. The DT system bridges a human user and robot through a bi-directional information flow: a) transmitting demonstrated welding operations in VR to the robot in the physical environment; b) displaying the physical welding scenes to human users in VR. Compared to existing DT systems reported in the literatures, the developed one provides better capability in engaging human users in interacting with welding scenes, through an augmented VR. To verify the effectiveness, six welders, skilled with certain manual welding training and unskilled without any training, tested the system by completing the same welding job; three skilled welders produce satisfied welded workpieces, while the other three unskilled do not. A data-driven approach as a combination of fast Fourier transform (FFT), principal component analysis (PCA), and support vector machine (SVM) is developed to analyze their behaviors. Given an operation sequence, i.e., motion speed sequence of the welding torch, frequency features are firstly extracted by FFT and then reduced in dimension through PCA, which are finally routed into SVM for classification. The trained model demonstrates a 94.44% classification accuracy in the testing dataset. The successful pattern recognition in skilled welder operations should benefit to accelerate novice welder training. © 2014 Chinese Association of Automation.","Digital twin (DT); human-robot interaction (HRI); machine learning; virtual reality (VR); welder behavior analysis","Classification (of information); Digital twin; Educational robots; Fast Fourier transforms; Pattern recognition; Statistical tests; Support vector machines; Welding; Behavior analysis; Classification accuracy; Data-driven approach; Frequency features; Operation sequences; Physical environments; Welder trainings; Welding operations; Social robots",,,,,,,,,,,,,,,,"Kang, H.S., Lee, J.Y., Choi, S., Kim, H., Park, J.H., Son, J.Y., Kim, B.H., Noh, S.D., Smart manufacturing: Past research, present findings, and future directions (2016) Int. J. Precis. Eng. 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Sci; Erden, M.S., Tomiyama, T., Identifying welding skills for training and assistance with robot (2009) Sci. Technol. Weld. Join., 14 (6), pp. 523-532; Liu, Y.K., Zhang, Y.M., Kvidahl, L., Skilled human welder intelligence modeling and control: Part I-Modeling (2014) Weld. J., 93 (2), pp. 46s-52s; Liu, Y.K., Zhang, Y.M., Kvidahl, L., Skilled human welder intelligence modeling and control: Part II-Analysis and control applications (2014) Weld. J., 93 (5), pp. 162s-170s; Smola, A.J., Scholkopf, B., A tutorial on support vector regression (2004) Stat. Comput., 14 (3), pp. 199-222. , Aug; Minka, T.P., Automatic choice of dimensionality for PCA (2000) Proc. 13th Int. Conf. Neural Information Processing Systems, pp. 577-583. , Cambridge USA; Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Duchesnay, E., Scikit-learn: Machine learning in Python (2011) J. Mach. Learn. Res., 12 (85), pp. 2825-2830","Zhang, Y.; Institute for Sustainable Manufacturing, United States; email: yuming.zhang@uky.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23299266,,,,"English","IEEE CAA J. Autom. Sin.",Article,"Final","",Scopus,2-s2.0-85097130846
"Wang D., Zhang Z., Zhang M., Fu M., Li J., Cai S., Zhang C., Chen X.","55819507100;57219094294;57221262062;57184859300;57191075261;56297299100;57199504177;56114476400;","The role of digital twin in optical communication: Fault management, hardware configuration, and transmission simulation",2021,"IEEE Communications Magazine","59","1","9356524","133","139",,30,"10.1109/MCOM.001.2000727","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101168854&doi=10.1109%2fMCOM.001.2000727&partnerID=40&md5=4d36e237e12d5d274bf33f0e9e4abe6c","Beijing University of Posts and Telecommunications; Purple Mountain Laboratories","Wang, D., Beijing University of Posts and Telecommunications; Zhang, Z., Beijing University of Posts and Telecommunications; Zhang, M., Beijing University of Posts and Telecommunications; Fu, M., Beijing University of Posts and Telecommunications; Li, J., Purple Mountain Laboratories; Cai, S., Beijing University of Posts and Telecommunications; Zhang, C., Beijing University of Posts and Telecommunications; Chen, X., Beijing University of Posts and Telecommunications","Optical communication is developing rapidly in the directions of hardware resource diversification, transmission system flexibility, and network function virtualization. Its proliferation poses a significant challenge to traditional optical communication management and control systems. Digital twin (DT), a technology that utilizes data, models, and algorithms and integrates multiple disciplines, acts as a bridge between the real and virtual worlds for comprehensive connectivity. In the digital space, virtual models are established dynamically to simulate and describe the states, behaviors, and rules of physical objects in the physical space. DT has been significantly developed and widely applied in the industrial and military fields. This study introduces the DT technology to optical communication through interdisciplinary crossing and proposes a DT framework suitable for optical communication. The intelligent fault management model, flexible hardware configuration model, and dynamic transmission simulation model are established in the digital space with the help of deep learning algorithms to ensure the high-reliability operation and high-ef-ficiency management of optical communication systems and networks. © 1979-2012 IEEE.",,"Deep learning; Digital twin; Failure analysis; Learning algorithms; Network function virtualization; Communication management; Fault management; Hardware configurations; Hardware resources; Multiple disciplines; Physical objects; Transmission simulations; Transmission systems; Optical communication",,,,,"6142104190207; National Natural Science Foundation of China, NSFC: 61671076, 61705016, 61871415, 61975020; Beijing University of Posts and Telecommunications, BUPT: IPOC2020ZT05; State Key Laboratory of Information Photonics and Optical Communications, SKLIPOC","Acknowledgements This work was supported in part by National Natural Science Foundation of China (No.61871415, 61671076, 61975020, 61705016), in part by the Key Laboratory Fund (No. 6142104190207), and Fund of State Key Laboratory of IPOC (BUPT) (No. IPOC2020ZT05), P. R. China.",,,,,,,,,,"Tao, F., Digital twin-driven product design, manufacturing and service with big data (2018) Int'l. J. Adv. Manufac. Tech., 21 (9), pp. 3563-3576; Negri, E., Fumagalli, L., Macchi, M., A review of the roles of digital twin in cps-based production systems (2017) Procedia Manufacturing, 11, pp. 939-948; Tuegel, E.J., Reengineering aircraft structural life prediction using a digital twin (2011) Int'l. J. Aero. Eng., pp. 1687-5966; Tao, F., Digital twin in industry: State-of-The-art (2019) Ieee Trans. Indus. Info., 15 (4), pp. 2405-2415; Zhu, Z., Dynamic service provisioning in elastic optical networks with hybrid single-/multi-path routing (2013) Ieee J. 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Processing, 3 (2), pp. 1-29. , Jan; Zhang, C., Temporal data-driven failure prognostics using bigru for optical networks (2020) IEEE/OSA J. Opt. Commun. And Net., 12 (8), pp. 227-287; Zhang, C., Interpretable learning algorithm based on xgboost for fault prediction in optical network Optical Fiber Commun. Conf., , Optical Society of America, 2020, paper Th1F. 3; Li, J., Digital twin-enabled self-evolved optical transceiver using deep reinforcement learning (2020) Opt. Lett., 45 (16), pp. 4654-4657; Wang, D., Data-driven optical fiber channel modeling: A deep learning approach (2020) Ieee J. Lightwave Tech., 38 (17), pp. 4730-4743",,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,01636804,,ICOMD,,"English","IEEE Commun Mag",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85101168854
"Stojanovic V., Trapp M., Richter R., Hagedorn B., Döllner J.","56421700900;24831175200;36195159400;56559901800;6602981892;","Towards the generation of digital twins for facility management based on 3D point clouds",2018,"Proceeding of the 34th Annual ARCOM Conference, ARCOM 2018",,,,"270","279",,29,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054529484&partnerID=40&md5=362878d63e18bee90afc8761c9203163","Computer Graphics Systems Group, Hasso Plattner Institute, Faculty of Digital Engineering, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, Potsdam, 14482, Germany","Stojanovic, V., Computer Graphics Systems Group, Hasso Plattner Institute, Faculty of Digital Engineering, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, Potsdam, 14482, Germany; Trapp, M., Computer Graphics Systems Group, Hasso Plattner Institute, Faculty of Digital Engineering, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, Potsdam, 14482, Germany; Richter, R., Computer Graphics Systems Group, Hasso Plattner Institute, Faculty of Digital Engineering, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, Potsdam, 14482, Germany; Hagedorn, B., Computer Graphics Systems Group, Hasso Plattner Institute, Faculty of Digital Engineering, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, Potsdam, 14482, Germany; Döllner, J., Computer Graphics Systems Group, Hasso Plattner Institute, Faculty of Digital Engineering, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, Potsdam, 14482, Germany","Advances versus adaptation of Industry 4.0 practices in Facility Management (FM) have created usage demand for up-to-date digitized building assets. The use of Building Information Modelling (BIM) for FM in the Operation and Maintenance (O&M) stages of the building lifecycle is intended to bridge the gap between operations and digital data, but lacks the functionality of assessing and forecasting the state of the built environment in real-time. To accommodate this, BIM data needs to be constantly updated with the current state of the built environment. However, generation of as-is BIM data for a digital representation of a building is a labor intensive process. While some software applications offer a degree of automation for the generation of as-is BIM data, they can be impractical to use for routinely updating digital FM documentation. Current approaches for capturing the built environment using remote sensing and photometry-based methods allow for the creation of 3D point clouds that can be used as basis data for a Digital Twin (DT), along with existing BIM and FM documentation. 3D point clouds themselves do not contain any semantics or specific information about the building components they represent physically, but using machine learning methods they can be enhanced with semantics that would allow for reconstruction of as-is BIM and basis DT data. This paper presents current research and development progress of a service-oriented platform for generation of semantically rich 3D point cloud representations of indoor environments. A specific focus is placed on the reconstruction and visualization of the captured state of the built environment for increasing FM stakeholder engagement and facilitating collaboration. The preliminary results of a prototypical web-based application demonstrate the feasibility of such a platform for FM using a service-oriented paradigm. © Proceeding of the 34th Annual ARCOM Conference, ARCOM 2018.","BIM; Digital Twins; Facility management; Point Clouds; Visualization","Application programs; Bridges; Flow visualization; Frequency modulation; Learning systems; Machine components; Office buildings; Project management; Remote sensing; Semantics; Visualization; Building Information Modelling; Digital Twins; Facility management; Labor intensive process; Machine learning methods; Operation and maintenance; Point cloud; Research and development; Architectural design",,,,,,,,,,,,,,,,"Becerik-Gerber, B., Jazizadeh, F., Li, N., Calis, G., Application areas and data requirements for BIM-enabled facilities management (2011) Journal of Construction Engineering and Management, 138 (3), pp. 431-442; Chen, K., Lai, Y.K., Hu, S.M., 3D indoor scene modeling from RGB-D data: A survey (2015) Computational Visual Media, 1 (4), pp. 267-278; Döllner, J., Hagedorn, B., Klimke, J., Server-based rendering of large 3D scenes for mobile devices using G-buffer cube maps (2012) Proceedings of the 17th International. 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Springer, Cham; Roper, K.O., Payant, R.P., (2014) The Facility Management Handbook, , New York, USA: AMACOM Division American Management Association; (2018) Leonardo Innovation Services Showroom, , https://discover.sap.com/innovation-showroom-demo/en-us/digital-twin.html, [Accessed 03/04/2018]; Singh, V., Gu, N., Wang, X., A theoretical framework of a BIM-based multi-disciplinary collaboration platform (2011) Automation in Construction, 20 (2), pp. 134-144; Stojanovic, V., Richter, R., Döllner, J., Trapp, M., Comparative visualization of BIM geometry and corresponding point clouds (2017) International Journal of Sustainable Development and Planning, 13 (1), pp. 12-23; Stojanovic, V., Trapp, M., Richter, R., Döllner, J., A service-oriented approach for classifying 3D points clouds by example of office furniture classification (2018) Web3D '18: The 23rd ACM International Conference on Web3D Technology, , 20-22 June 2018, Pozna?, Poland: ACM; Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E., Multi-view convolutional neural networks for 3D shape recognition (2015) Proceedings of the IEEE International Conference on Computer Vision, pp. 945-953; Tang, P., Huber, D., Akinci, B., Lipman, R., Lytle, A., Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques (2010) Automation in Construction, 19 (7), pp. 829-843; Teicholz, P., (2013) BIM for Facility Managers, , Chichester, UK: John Wiley and Sons; Volk, R., Stengel, J., Schultmann, F., Building information modeling (BIM) for existing buildings: Literature review and future needs (2014) Automation in Construction, 38 (1), pp. 109-127; Xiong, X., Adan, A., Akinci, B., Huber, D., Automatic creation of semantically rich 3D building models from laser scanner data (2013) Automation in Construction, 31, pp. 325-337; Xue, F., Lu, W., Chen, K., Automatic generation of semantically rich as-built building information models using 2D images: A derivative-free optimization approach (2018) Computer-Aided Civil and Infrastructure Engineering",,"Gorse C.Neilson C.J.",,"Association of Researchers in Construction Management","34th Annual Association of Researchers in Construction Management Conference, ARCOM 2018","3 September 2018 through 5 September 2018",,140045,,,,,"English","Proc. Annu. ARCOM Conf., ARCOM",Conference Paper,"Final","",Scopus,2-s2.0-85054529484
"Lin K., Xu Y.-L., Lu X., Guan Z., Li J.","56032694900;55695003100;15728275900;8987056300;56045991600;","Digital twin-based collapse fragility assessment of a long-span cable-stayed bridge under strong earthquakes",2021,"Automation in Construction","123",,"103547","","",,26,"10.1016/j.autcon.2020.103547","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099354526&doi=10.1016%2fj.autcon.2020.103547&partnerID=40&md5=be4d4f4523447d54d0052893874da210","Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong; College of Civil Engineering, Fuzhou University, Fuzhou, China; Key Laboratory of Civil Engineering Safety and Durability of China Education Ministry, Department of Civil Engineering, Tsinghua University, Beijing, China; Department of Bridge Engineering, Tongji University, Shanghai, China","Lin, K., Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, College of Civil Engineering, Fuzhou University, Fuzhou, China; Xu, Y.-L., Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong; Lu, X., Key Laboratory of Civil Engineering Safety and Durability of China Education Ministry, Department of Civil Engineering, Tsinghua University, Beijing, China; Guan, Z., Department of Bridge Engineering, Tongji University, Shanghai, China; Li, J., Department of Bridge Engineering, Tongji University, Shanghai, China","Fragility analysis is widely used in seismic performance assessment of bridges but uncertainties in seismic demand and bridge modelling affect the accuracy of assessment results. This study proposes a digital twin-based collapse fragility assessment method for long-span cable-stayed bridges under strong earthquakes. A scaled long-span cable-stayed bridge and its shake table tests are taken as an example. Three finite element (FE) models of the bridge, including a design document-based FE model, a linearly updated FE model, and a nonlinearly updated FE model, are established to demonstrate the necessity of the digital twin-based assessment. Incremental dynamic analyses (IDA) are conducted to calculate the collapse fragility curves of the FE models. The assessment results are compared with the test results in terms of collapse mechanisms, collapse ground motion intensities, and collapse probabilities. It is found that the proposed method is feasible and accurate for seismic collapse assessment of long-span cable-stayed bridges. © 2021 Elsevier B.V.","Digital twin; Fragility analysis; Long-span cable-stayed bridge; Modelling uncertainties; Seismic collapse assessment; Shake table tests","Buffeting; Cables; Digital twin; Earthquake effects; Earthquake engineering; Uncertainty analysis; Collapse fragilities; Collapse probabilities; Fragility analysis; Ground motion intensities; Incremental dynamic analysis; Long span cable stayed bridges; Seismic performance assessment; Strong earthquakes; Cable stayed bridges",,,,,"15269516; Curtin University of Technology; National Natural Science Foundation of China, NSFC: 51908133; Hong Kong Polytechnic University, PolyU: 1-ZVN3","The authors wish to acknowledge the financial support from the Research Grants Council of Hong Kong through a competitive GRF grant (Grant No. 15269516 ). The financial support from The Hong Kong Polytechnic University through a special grant (Grant No. 1-ZVN3 ) and the National Natural Science Foundation of China (No. 51908133 ) is also appreciated. The authors are grateful for the Information Technology Services of the Hong Kong Polytechnic University for providing the servers. The authors would also like to acknowledge Prof. Hong Hao from Curtin University, Australia, for his help in securing the first research grant for this work. Any opinions and conclusions presented in this paper are entirely those of the authors.",,,,,,,,,,"Vamvatsikos, D., Cornell, C.A., Applied incremental dynamic analysis (2004) Earthq. Spectra, 20 (2), pp. 523-553; Casciati, F., Cimellaro, G.P., Domaneschi, M., Seismic reliability of a cable-stayed bridge retrofitted with hysteretic devices (2008) Comput. 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"Jiang Y., Yin S., Li K., Luo H., Kaynak O.","57189689209;35346008700;57192990336;57213205900;7004469974;","Industrial applications of digital twins",2021,"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","379","2207","20200360","","",,25,"10.1098/rsta.2020.0360","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113295605&doi=10.1098%2frsta.2020.0360&partnerID=40&md5=a3f59fb1250cd7a2610c0a40cd81e867","Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China; Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim, 7033, Norway; Department of Electrical and Electronics Engineering, Bogazici University, Istanbul, 34342, Turkey; School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China","Jiang, Y., Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China; Yin, S., Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim, 7033, Norway; Li, K., Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China; Luo, H., Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China; Kaynak, O., Department of Electrical and Electronics Engineering, Bogazici University, Istanbul, 34342, Turkey, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China","A digital twin (DT) is classically defined as the virtual replica of a real-world product, system, being, communities, even cities that are continuously updated with data from its physical counterpart, as well as its environment. It bridges the virtual cyberspace with the physical entities and, as such, is considered to be the pillar of Industry 4.0 and the innovation backbone of the future. A DT is created and used throughout the whole life cycle of the entity it replicates, from cradle to grave, so to speak. This article focuses on the present state of the art of DTs, concentrating on the use of DTs in industry in the context of smart manufacturing, especially from the point of view of plantwide optimization. The main capabilities of DTs (mirroring, shadowing and threading) are discussed in this context. The article concludes with a perspective on the future. This article is part of the theme issue 'Towards symbiotic autonomous systems'. © 2021 The Author(s).","digital transformation; digital twin; industrial application; Industry 4.0","Life cycle; Autonomous systems; Cyberspaces; Plant-wide optimization; Real-world; Smart manufacturing; State of the art; Whole life cycles; Digital twin; adult; article; city; digital twin; life cycle",,,,,"Fok Ying Tung Education Foundation, FYTEF: 161056","Data accessibility. This article has no additional data. Authors’ contributions. Conceptualization: Y.J., S.Y. and O.K.; investigation: Y.J., K.L. and O.K.; resources: S.Y., O.K. and H.L.; writing and original draft preparation: Y.J. and K.L.; writing and review and editing: O.K., S.Y. and H.L.; supervision: S.Y., and O.K. All authors have read and agreed to the published version of the manuscript. Competing interests. We declare we have no competing interests. Funding. 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"Liu Z., Shi G., Zhang A., Huang C.","57191688199;57215775995;57213149153;56558885500;","Intelligent tensioning method for prestressed cables based on digital twins and artificial intelligence",2020,"Sensors (Switzerland)","20","24","7006","1","20",,20,"10.3390/s20247006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097567897&doi=10.3390%2fs20247006&partnerID=40&md5=3a06257a163a2f44ace9ebb0a86972d0","College of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing, 100124, China; The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing, 100124, China","Liu, Z., College of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing, 100124, China, The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing, 100124, China; Shi, G., College of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing, 100124, China, The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing, 100124, China; Zhang, A., College of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing, 100124, China, The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing, 100124, China; Huang, C., College of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing, 100124, China, The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing, 100124, China","In this study, to address the problems of multiple dimensions, large scales, complex tension resource scheduling, and strict quality control requirements in the tensioning process of cables in prestressed steel structures, the technical characteristics of digital twins (DTs) and artificial intelligence (AI) are analyzed. An intelligent tensioning of prestressed cables method driven by the integration of DTs and AI is proposed. Based on the current research status of cable tensioning and DTs, combined with the goal of intelligent tensioning, a fusion mechanism for DTs and AI is established and their integration to drive intelligent tensioning of prestressed cables technology is analyzed. In addition, the key issues involved in the construction of an intelligent control center driven by the integration of DTs and AI are discussed. By considering the construction elements of space and time dimensions, the tensioning process is controlled at multiple levels, thereby realizing the intelligent tensioning of prestressed cables. Driven by intelligent tensioning methods, the safety performance evaluation of the intelligent tensioning process is analyzed. Combined with sensing equipment and intelligent algorithms, a high-fidelity twin model and three-dimensional integrated data model are constructed to realize closed-loop control of the intelligent tensioning safety evaluation. Through the study of digital twins and artificial intelligence fusion to drive the intelligent tensioning method for prestressed cables, this study focuses on the analysis of the intelligent evaluation of safety performance. This study provides a reference for fusion applications with DTs and AI in intelligent tensioning of prestressed cables. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Artificial intelligence; Digital twin; Intelligent tensioning; Prestressed cable; Security assessment; Sensing equipment","Bridge cables; Closed loop control systems; Digital storage; Digital twin; Prestressing; Process control; Safety engineering; Three dimensional computer graphics; Closed-loop control; Construction elements; Control requirements; Current research status; Intelligent Algorithms; Intelligent evaluation; Prestressed steel structure; Safety performance evaluation; Artificial intelligence",,,,,"2018YFF0300300","Funding: The research was funded by the National Key R&D Program for the 13th-Five-Year Plan of China, grant number 2018YFF0300300.",,,,,,,,,,"Sharma, P.K., Park, J.H., Blockchain based hybrid network architecture for the smart city (2018) Future Gener. Comput. 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Liu, Z.; The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, China; email: lzs4216@163.com",,,"MDPI AG",,,,,14248220,,,"33302362","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85097567897
"Kang J.-S., Chung K., Hong E.J.","57211502119;25927027500;57212106477;","Multimedia knowledge‐based bridge health monitoring using digital twin",2021,"Multimedia Tools and Applications","80","26-27",,"34609","34624",,18,"10.1007/s11042-021-10649-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101092359&doi=10.1007%2fs11042-021-10649-x&partnerID=40&md5=3d26586c1ef82a8cbc8f6684db6673d3","Department of Computer Science, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do  16227, South Korea; Division of AI Computer Science and Engineering, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do  16227, South Korea; Department of Software, Yonsei University, Wonju-si, 26493, South Korea","Kang, J.-S., Department of Computer Science, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do  16227, South Korea; Chung, K., Division of AI Computer Science and Engineering, Kyonggi University, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do  16227, South Korea; Hong, E.J., Department of Software, Yonsei University, Wonju-si, 26493, South Korea","Digital twins are virtual replicas of real physical entities in computers. They can be considered as abstract digital models of data and behavior for objects of interest. Nevertheless, they are not perfectly consistent with conventional data or simulation models because they achieve prediction and optimization by simulating the abstract digital model of a particular system. To maintain the characteristics of digital twins in the virtual space, digital simulation models that continue to update, change, and evolve according to continuous changes of corresponding physical factors must be used. Owing to the various advantages of digital twin technology, digital twins have gained more attention. However, the method to create digital twins is still unclear. Additionally, the availability and sufficiency of information on physical entities to which digital twins will be applied must be considered, and a model suitable for their application must be designed. Therefore, multimedia knowledge-based bridge health monitoring using digital twins is proposed herein. It synchronizes real and virtual spaces to reflect the reality based on various data collected using sensors of real systems. In this study, various situations of virtual bridge twins in a facility management area are simulated to provide digital services to ensure bridge health. This digital bridge health service analyzes situations based on a small amount of data collected from a bridge, predicts the optimal time point for maintenance, and then applies it to the real world. Hence, maintenance costs can be reduced and the bridge’s lifespan extended. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.","Bridge health monitoring; Data model; Digital twin; Knowledge; Modeling and simulation","Bridges; Health; Information services; Knowledge based systems; Office buildings; Bridge health monitoring; Digital services; Digital simulation models; Facility management; Knowledge based; Maintenance cost; Physical factors; Virtual bridges; Digital twin",,,,,"Ministry of Land, Infrastructure and Transport, MOLIT: 21CTAP-C157011-02; Korea Agency for Infrastructure Technology Advancement, KAIA","This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 21CTAP-C157011-02).",,,,,,,,,,"Bondarenko, O., Fukuda, T., Development of a diesel engine’s digital twin for predicting propulsion system dynamics (2020) Energy, 196. , (,),., https://doi.org/10.1016/j.energy.2020.117126; City Brain Alibaba Cloud, , https://www.alibabacloud.com/solutions/intelligence-brain/city; Fan, C., Zhang, C., Yahja, A., Mostafavi, A., Disaster city digital twin: a vision for integrating artificial and human intelligence for disaster management (2019) Int J Inf Manag; Kabak, K., Hinkeldeyn, J., Dekkers, R., Analyses of outcomes that used simulation modelling towards building theory (2019) Procedia Manuf, 39, pp. 794-803; Kang, J., Shin, D., Baek, J., Chung, K., Activity recommendation model using rank correlation for chronic stress management (2019) Appl Sci, 9 (20), pp. 4284-4296; Kim, J.C., Chung, K., Multi-modal stacked denoising autoencoder for handling missing data in health big data (2020) IEEE Access, 8 (1), pp. 104933-104943; Kim, B.S., Kim, T.G., Modeling and simulation using artificial neural network-embedded cellular automata (2020) IEEE Access, 8 (1), pp. 24056-24061; Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W., Digital twin in manufacturing: a categorical literature review and classification (2018) IFAC-PapersOnLine, 51 (11), pp. 1016-1022; Lee, J., Bagheri, B., Kao, H., A cyber-physical systems architecture for industry 4.0-based manufacturing systems (2015) Manuf Lett, 3, pp. 18-23; Lee, K.H., Hong, J.H., Kim, T.G., System of systems approach to formal modeling of cps for simulation-based analysis (2015) ETRI J, 37 (1), pp. 175-185; Madni, A., Madni, C., Lucero, S., Leveraging digital twin technology in model-based systems engineering (2019) Systems, 7 (1), p. 7; Maria, A., Introduction to modeling and simulation (1997) Proceedings of the 29Th Conf. 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Conf. Process Control (PC), pp. 258-262; Velosa, A., Natis, Y., Lheureux, B., (2016) Use the Iot Platform Reference Model to Pan Your Iot Business Solutions, , Gartner Research, Stamford; Ye, C., Butler, L., Bartek, C., Iangurazov, M., Lu, Q., Gregory, A., Girolami, M., Middleton, C., A digital twin of bridges for structural health monitoring (2019) Proceedings of the 12Th International Workshop on Structural Health Monitoring. Stanford University, , https://doi.org/10.12783/shm2019/32287; https://www.nrf.gov.sg/programmes/virtual-singapore","Hong, E.J.; Department of Software, South Korea; email: ellenhong@yonsei.ac.kr",,,"Springer",,,,,13807501,,MTAPF,,"English","Multimedia Tools Appl",Article,"Final","",Scopus,2-s2.0-85101092359
"Jiang F., Ding Y., Song Y., Geng F., Wang Z.","57204694266;55768944900;55494118800;36637279300;36723167900;","Digital Twin-driven framework for fatigue life prediction of steel bridges using a probabilistic multiscale model: Application to segmental orthotropic steel deck specimen",2021,"Engineering Structures","241",,"112461","","",,18,"10.1016/j.engstruct.2021.112461","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105352890&doi=10.1016%2fj.engstruct.2021.112461&partnerID=40&md5=368175e567688c9bcbb3720458bb938c","Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing, 210096, China; School of Architecture Engineering, Jinling Institute of Technology, Nanjing, 211169, China; School of Architecture Engineering, Nanjing Institute of Technology, Nanjing, 211167, China; Shenzhen Express Engineering Consulting Co. Ltd, Shenzhen, 518000, China","Jiang, F., Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing, 210096, China; Ding, Y., Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing, 210096, China; Song, Y., School of Architecture Engineering, Jinling Institute of Technology, Nanjing, 211169, China; Geng, F., School of Architecture Engineering, Nanjing Institute of Technology, Nanjing, 211167, China; Wang, Z., Shenzhen Express Engineering Consulting Co. Ltd, Shenzhen, 518000, China","Accurate fatigue life prediction facilitates the fatigue maintenance of steel bridges. Since Digital Twin can simulate the lifecycle for physical objects at various scales, this study aims to provide a Digital Twin-driven framework for non-deterministic fatigue life prediction of steel bridges. A probabilistic multiscale model was developed to depict the fatigue evolution throughout the bridge lifecycle. The small crack initiation period was well described by the modified Fine and Bhat model considering microstructure uncertainties. After obtaining the critical model parameter via crystal plastic finite element simulation, the modified model was further calibrated using the assumed historical fatigue data in Digital Twin database. Based on the initiated half-penny-shaped small crack, the small crack initiation period was connected to the macrocrack extension period. Given the uncertainties of macrocrack propagation, the Paris’ law with random growth parameters was adopted. The Bayesian inference of the growth parameters realized the real-time calibration of the macrocrack growth model using Markov chain Monte Carlo simulation. The feasibility of the proposed framework was demonstrated through fatigue tests on a segmental steel deck specimen with mixed-mode deformed U-rib to diaphragm welded joints. The results show that the predicted fatigue initiation life and residual fatigue life are in good agreement with the experimentally observed life results. In summary, the proposed framework enhances our understanding of the fatigue evolution mechanism throughout the bridge lifecycle and provides an entirely new approach to accurately predict the fatigue life of steel bridges under various sources of uncertainties. © 2021 Elsevier Ltd","Crack initiation; Crack propagation; Digital Twin; Fatigue life prediction; Multiscale model; Steel bridges","Bayesian networks; Crack initiation; Crack propagation; Cracks; Fatigue of materials; Fatigue testing; Inference engines; Intelligent systems; Life cycle; Markov processes; Monte Carlo methods; Steel bridges; Welding; Welds; Cracks initiations; Cracks propagation; Fatigue life prediction; Growth parameters; Macro-cracks; Model application; Multiscale models; Probabilistics; Small crack; Uncertainty; Forecasting; bridge; crack propagation; fatigue; finite element method; growth modeling; microstructure; steel structure",,,,,"BK20190013; National Natural Science Foundation of China, NSFC: 51978154","This work was supported by the Distinguished Young Scientists of Jiangsu Province [grant number BK20190013] and the National Natural Science Foundation of China [grant number 51978154].",,,,,,,,,,"Lin, W., Yoda, T., Steel bridges (2017) Bridge engineering, pp. 111-136. , Elsevier Cambridge; Maier, P., Kuhlmann, U., Popa, N., Willms, R., Optimizing bridge design by improved deterioration models through fatigue tests (2012) Bridge maintenance, safety, management, resilience and sustainability, pp. 1816-1821. , F. Biondini D.M. Frangopol Taylor & Francis Group London; Lukić, M., Al-Emrani, M., Aygül, M., Bokesjö, M., Urushadze, S., Frýba, L., Bridge fatigue guidance: meeting sustainable design and assessment (2013), Publications Office of the European Union Luxembourg; Grieves, M., Vickers, J., Digital Twin: mitigating unpredictable, undesirable emergent behavior in complex systems (2017) Transdisciplinary perspectives on complex systems, pp. 85-113. , F. Kahlen S. Flumerfelt A. Alves Springer International Publishing Cham; Leser, P.E., Warner, J.E., Leser, W.P., Bomarito, G.F., Newman, J.A., Hochhalter, J.D., A digital twin feasibility study (Part II): non-deterministic predictions of fatigue life using in-situ diagnostics and prognostics (2020) Eng Fract Mech, 229; Božić, Ž., Schmauder, S., Mlikota, M., Hummel, M., Multiscale fatigue crack growth modelling for welded stiffened panels (2014) Fatigue Fract Eng Mater Struct, 37, pp. 1043-1054; Tanaka, K., Mura, T., A dislocation model for fatigue crack initiation (1981) J Appl Mech, 48, pp. 97-103; Mura, T., Nakasone, Y., A theory of fatigue crack initiation in solids (1990) J Appl Mech, 57, pp. 1-6; Lin, M.R., Fine, M.E., Mura, T., Fatigue crack initiation on slip bands: theory and experiment (1986) Acta Metall, 34, pp. 619-628; Fine, M.E., Bhat, S.P., A model of fatigue crack nucleation in single crystal iron and copper (2007) Mater Sci Eng, A, 468-470, pp. 64-69; Voothaluru, R., Richard, L.C., Determination of lattice level energy efficiency for fatigue crack initiation (2013) Fatigue Fract Eng Mater Struct, 36, pp. 670-678; Li, L., Shen, L., Proust, G., Fatigue crack initiation life prediction for aluminium alloy 7075 using crystal plasticity finite element simulations (2015) Mech Mater, 81, pp. 84-93; Yeratapally, S.R., Leser, P.E., Hochhalter, J.D., Leser, W.P., Ruggles, T.J., A digital twin feasibility study (Part I): non-deterministic predictions of fatigue life in aluminum alloy 7075–T651 using a microstructure-based multi-scale model (2020) Eng Fract Mech, 228; Zhao, Z., Haldar, A., Breen, F.L., Fatigue-reliability evaluation of steel bridges (1994) J Struct Eng, 120, pp. 1608-1623; Nagy, W., van Bogaert, P., de Backer, H., LEFM based fatigue design for welded connections in orthotropic steel bridge decks (2015) Procedia Eng, 133, pp. 758-769; Wang, B., Nagy, W., De Backer, H., Chen, A., Fatigue process of rib-to-deck welded joints of orthotropic steel decks (2019) Theor Appl Fract Mech, 101, pp. 113-126; Glaessgen, E., Stargel, D., The Digital Twin paradigm for future NASA and U.S. air force vehicles (2012), pp. 1-14. , American Institute of Aeronautics and Astronautics Honolulu (HI). Reston (VA); Frangopol, D.M., Kim, S., Life-cycle of structures under uncertainty: emphasis on fatigue-sensitive civil and marine structures (2019), CRC Press Boca Raton; Cheng, B., Cao, X., Ye, X., Cao, Y., Fatigue tests of welded connections between longitudinal stringer and deck plate in railway bridge orthotropic steel decks (2017) Eng Struct, 153, pp. 32-42; Luo, P., Zhang, Q., Bao, Y., Bu, Y., Fatigue performance of welded joint between thickened-edge U-rib and deck in orthotropic steel deck (2019) Eng Struct, 181, pp. 699-710; Huang, Y., Zhang, Q., Bao, Y., Bu, Y., Fatigue assessment of longitudinal rib-to-crossbeam welded joints in orthotropic steel bridge decks (2019) J Constr Steel Res, 159, pp. 53-66; Zhang, Q., Bu, Y., Li, Q., Review on fatigue problems of orthotropic steel bridge deck (2017) China J Highw Transp, 30, pp. 14-30. , (in Chinese); (2003), CEN. EN 1991-2: EUROCODE 1: Actions on structures - Part 2: Traffic loads on bridges; Bhat, S.P., Fine, M.E., Fatigue crack nucleation in iron and a high strength low alloy steel (2001) Mater Sci Eng, A, 314, pp. 90-96; Schönecker, S., Li, X., Johansson, B., Kwon, S.K., Vitos, L., Thermal surface free energy and stress of iron (2015) Sci Rep, 5, p. 14860; Aqra, F., Ayyad, A., Surface energies of metals in both liquid and solid states (2011) Appl Surf Sci, 257, pp. 6372-6379; Zhang, K.-S., Ju, J.W., Li, Z., Bai, Y.-L., Brocks, W., Micromechanics based fatigue life prediction of a polycrystalline metal applying crystal plasticity (2015) Mech Mater, 85, pp. 16-37; Smith, M., ABAQUS/standard user's manual, version 6.9. 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BS 7910:2005: Guide to methods for assessing the acceptability of flaws in metallic structures; Erdogan, F., Sih, G.C., On the crack extension in plates under plane loading and transverse shear (1963) J Basic Eng, 85, pp. 519-525; Atkinson, K.E., An introduction to numerical analysis (1989), 2nd ed. John Wiley & Sons New York","Ding, Y.; Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, China; email: civilding@seu.edu.cn",,,"Elsevier Ltd",,,,,01410296,,ENSTD,,"English","Eng. Struct.",Article,"Final","",Scopus,2-s2.0-85105352890
"Ye S., Lai X., Bartoli I., Aktan A.E.","56783517100;57196260436;8856150200;7006947953;","Technology for condition and performance evaluation of highway bridges",2020,"Journal of Civil Structural Health Monitoring","10","4",,"573","594",,18,"10.1007/s13349-020-00403-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084367794&doi=10.1007%2fs13349-020-00403-6&partnerID=40&md5=13a3541cb9b282f4c1a6a608f9ec9875","Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, PA, United States","Ye, S., Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, PA, United States; Lai, X., Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, PA, United States; Bartoli, I., Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, PA, United States; Aktan, A.E., Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, PA, United States","Today, bridge owners must consider increasing traffic demands (in both volume and weight) and also face concerns related to sustainability, resilience and liveability which were virtually unknown in the 1950s. Furthermore, legislators demand data-driven asset management decisions based on objective, quantitative and reliable bridge condition and performance evaluation. To explore the current state-of-the-art in objective performance and condition evaluation of constructed systems by leveraging technology, a 30-year old freeway bridge in New Jersey, exhibiting multiple complex performance deficiencies, was transformed into a field laboratory. To identify the root causes of performance concerns, Visual Inspection, Operational Monitoring, Forced Excitation Testing, Controlled Load Testing, Non-destructive Probes, Long-term Monitoring, Finite Element Modelling and Parameter Identification were conducted within a Structural Identification framework. The results showed that root causes of some performance deficiencies of the test bridge were identified definitively only through the application of field measurements and analyses integrated by following a scientific approach—i.e. Structural Identification. Controlled Proof-Load Testing was especially useful in demonstrating the location and impacts of damage and the remaining capacity although such an approach can only be considered for the most critical cases due to its high cost and disruption to operations. Operational monitoring was shown as a sufficient and much cheaper alternative for structural identification permitting the development of a 3D digital twin of the bridge, which proved critical in identifying the root causes of its deficiencies and formulating meaningful interventions. Without an a-priori model used for designing the experiments as well as a model (i.e. a digital twin) calibrated by parameter identification and used for simulations, it was not possible to offer options for corrective measures confidently. The study demonstrated the challenges in relying only on visual inspection when a multitude of interdependent mechanisms lead to damage and deterioration, and the information value of different experimental methods such as vibration testing, proof load testing, wide-area NDE scans and multi-year SHM in being able to understand the root causes of various damages. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.","Condition and performance evaluation; Controlled load testing; Finite element model updating; Non-destructive evaluation; Structural health monitoring; Structural identification; Vibration testing","Bridges; Deterioration; Digital twin; Load testing; Nondestructive examination; Structural analysis; Condition evaluation; Corrective measures; Experimental methods; Finite element modelling; Long term monitoring; Management decisions; Operational monitoring; Structural identification; Parameter estimation",,,,,"New Jersey Department of Transportation, NJDOT; Federal Highway Administration, FHWA; Princeton University","The structural system of the test bridge and its design is a common one throughout the US highway system. Various field experiments were conducted on the same span during 2012–2013 with the participation of experienced bridge engineers and bridge research experts from Japan, Korea, the United Kingdom, Austria, Switzerland and the US. The study was funded by the USDOT–FHWA and NJDOT as a part of the FHWA’s Long-Term Bridge Performance Study, and it was coordinated by Drexel University with participation from Rutgers and Princeton University.","This study was funded by the USDOT-FHWA and NJDOT as part of the Pilot Phase of FHWA’s Long-Term Bridge Performance Program. The authors are deeply grateful to the contributions of their US-based academic collaborators Professors Franklin Moon, Jeff Weidner [], Nenad Gucunski, Branko Glisic, Haluk Aktan, Marvin Halling as well as Yun Zhou, Jian Zhang and John Prader. FHWA researchers and officials Dr. Steven Chase and Dr. Hamid Ghasemi initiated the Long-Term Bridge Performance Program. Authors are especially grateful to International participants from Japan, Korea and Europe (Yozo Fujino, Tomonori Nagayama, Hyun-Moo Koh, Helmut Wenzel, James Brownjohn and Ian Smith as well as their teams) who generously supported and played very critical roles in this study. Finally, the senior authors deeply appreciate the current support and guidance by their FHWA colleagues Drs. Hoda Azari and David Kuehn. Additional information about this and other studies can be found on the “NDE Virtual Laboratory website” at: http://vlab.asklab.net/VirtualLab/index.html .","This study was funded by the USDOT-FHWA and NJDOT as part of the Pilot Phase of FHWA?s Long-Term Bridge Performance Program. The authors are deeply grateful to the contributions of their US-based academic collaborators Professors Franklin Moon, Jeff Weidner?[56], Nenad Gucunski, Branko Glisic, Haluk Aktan, Marvin Halling as well as Yun Zhou, Jian Zhang and John Prader. FHWA researchers and officials Dr. Steven Chase and Dr. Hamid Ghasemi initiated the Long-Term Bridge Performance Program. Authors are especially grateful to International participants from Japan, Korea and Europe (Yozo Fujino, Tomonori Nagayama, Hyun-Moo Koh, Helmut Wenzel, James Brownjohn and Ian Smith as well as their teams) who generously supported and played very critical roles in this study. Finally, the senior authors deeply appreciate the current support and guidance by their FHWA colleagues Drs. Hoda Azari and David Kuehn.?Additional information about this and other studies can be found on the ?NDE Virtual Laboratory website? at:?http://vlab.asklab.net/VirtualLab/index.html.",,,,,,,,"(2019) 2019 Bridge Report, , https://artbabridgereport.org/reports/2019-ARTBA-Bridge-Report.pdf, . 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Rutgers, The State University of New Jersey; Weidner, J., (2012) Structural Identification of a Complex Structure Using Both Conventional and Multiple Model Approaches, , http://hdl.handle.net/1860/3818, Doctoral Thesis. Drexel University","Ye, S.; Department of Civil, United States; email: shi.ye@drexel.edu",,,"Springer",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85084367794
"Yang X., Ran Y., Zhang G., Wang H., Mu Z., Zhi S.","57191643033;36993318800;56413201200;57196456900;57203981893;57226445534;","A digital twin-driven hybrid approach for the prediction of performance degradation in transmission unit of CNC machine tool",2022,"Robotics and Computer-Integrated Manufacturing","73",,"102230","","",,14,"10.1016/j.rcim.2021.102230","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111616270&doi=10.1016%2fj.rcim.2021.102230&partnerID=40&md5=f4ceec56d98a5ed872f13aeebbf26fb2","College of Mechanical Engineering, Chongqing University, Chongqing, 400044, China; State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China; Chongqing University of Arts and Sciences, Chongqing, 400044, China; Ammunition Packaging Products Factory, Xinhua Chemical Co., Ltd., Shanxi, 030000, China","Yang, X., College of Mechanical Engineering, Chongqing University, Chongqing, 400044, China; Ran, Y., State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China; Zhang, G., State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China, Chongqing University of Arts and Sciences, Chongqing, 400044, China; Wang, H., College of Mechanical Engineering, Chongqing University, Chongqing, 400044, China; Mu, Z., College of Mechanical Engineering, Chongqing University, Chongqing, 400044, China; Zhi, S., Ammunition Packaging Products Factory, Xinhua Chemical Co., Ltd., Shanxi, 030000, China","Precision performance prediction of transmission system is considered as a key technology to modern equipment health management. Given the importance of maintaining a transmission system's precision, this paper presents a hybrid approach framework driven by digital twin technology (DT), to predict performance degradation. Firstly, a DT model based on meta-action theory is established, and real-time monitoring and digital simulation, driven by DT data, is realized in order to analyze the precision of the transmission units in machine tools. Secondly, the wear of gear in transmission unit is studied through Achard wear theory, which considered the comprehensive influence of gear load and speed on surface wear of the gear pair tooth, based on the model driving method. The performance degradation of the transmission unit is obtained by using the RBF neural network algorithm based on the data-driven method to extrapolate the wear data to the field-measurable precision index value. In addition, the hybrid predictive approach of the performance degradation model through the particle filter algorithm is built, and the real-time data is used to update the current state estimation to improve the prediction accuracy. By combining the mechanism of the physical degradation processes with the real-time and historical data and turning them into a cooperative architecture, this prediction method uses the complementary advantages offered by the fusion of these methods to bridge the link between data-driven prediction and model-based prediction. Finally, the method has been successfully applied to the precision prediction of the transmission unit in CNCMT turntable, and it is compared with the single prediction method to verify the effectiveness and feasibility. © 2021","CNCMT; Data-driven; Digital twin; Performance degradation; Simulation; Wear","Agricultural robots; Computer control systems; Digital twin; Forecasting; Machine tools; Radial basis function networks; Wear of materials; Cooperative architectures; Equipment health managements; Model-based prediction; Particle filter algorithms; Performance degradation; Performance prediction; Precision prediction; Prediction of performance; Predictive analytics",,,,,"2018ZX04032-001, 2019ZX04005-001; National Natural Science Foundation of China, NSFC: 51705048, 51835001; Nature Conservancy of Canada, NCC; National Major Science and Technology Projects of China","This work was supported in part by the National Natural Science Foundation, China (No. 51835001 ; 51705048 ) and the National Major Scientific and Technological Special Project for “High-grade CNC and Basic Manufacturing Equipment”, China ( 2018ZX04032-001 ; 2019ZX04005-001 ) .",,,,,,,,,,"Chehade, A., Song, C., Liu, K., Saxena, A., Zhang, X., A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes (2018) J. Qual. Technol., 50, pp. 150-165; Dong, M., Peng, Y., Equipment PHM using non-stationary segmental hidden semi-Markov model (2011) Robot. Comp.Integr. Manuf., 27, pp. 581-590; Sun, X., Bao, J., Li, J., Zhang, Y., Liu, S., Zhou, B., A digital twin-driven approach for the assembly-commissioning of high precision products (2020) Robot. Comp.Integr. Manuf., 61, p. 839; Yu, J., Song, Y., Tang, D., Dai, J., A digital twin approach based on nonparametric Bayesian network for complex system health monitoring (2020) J. Manuf. Syst., 58, pp. 293-304; Si, X., Wang, W., Hu, C., Zhou, D., Remaining useful life estimation - a review on the statistical data driven approaches (2011) Eur. J. Oper. Res., 213, pp. 1-14; Zhang, H., Kang, R., Pecht, M., A hybrid prognostics and health management approach for condition-based maintenance (2009) Int. Conf. Ind. Eng. Eng. Manage. IEEM, pp. 1165-1169; Tao, F., Qi, Q., Liu, A., Kusiak, A., Data-driven smart manufacturing (2018) J. Manuf. 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Syst., 23, pp. 753-768; Schleich, B., Anwer, N., Mathieu, L., Wartzack, S., Shaping the digital twin for design and production engineering (2017) CIRP Annals, 66, pp. 141-144; Fei, T., Meng, Z., Jiangfeng, C., Qinglin, Q.I., Digital twin workshop: a new paradigm for future workshop (2017) Comput. Integr. Manuf. Syst., 23, pp. 1-9; Zheng, P., Sivabalan, A.S., A generic tri-model-based approach for product-level digital twin development in a smart manufacturing environment (2020) Robot. Comp. Integr. Manuf., 64, p. 101958; Sun, H., Zhang, J., Mo, R., Zhang, X., In-process tool condition forecasting based on a deep learning method (2020) Robot. Comp. Integr. Manuf., 64, p. 101924; Lu, Y., Liu, C., Wang, K.I., Huang, H., Xu, X., Digital twin-driven smart manufacturing: connotation, reference model, applications and research issues (2020) Robot. Comp. Integr. Manuf., 61, p. 101837; Yu, H., Zhang, G., Ran, Y., Li, M., Jiang, D., Chen, Y., A reliability allocation method for mechanical product based on meta-action (2019) IEEE T. Reliab., 69 (1), pp. 373-381; Wu, D., Coatanea, E., Wang, G.G., Employing knowledge on causal relationship to assist multidisciplinary design optimization (2019) J. Mech. Des., 141 (4), pp. 041402-1-04140211; Zhang, G., Zhang, H., Fan, X., Tu, L., Function decomposition and reliability analysis of CNC machine using function- motion-action (2012) Mech. Sci. Technol. Aerospace Eng., 4 (31), pp. 528-533; Ran, Y., Research on Meta-Action Unit Modelling and Key QCs Predictive Control Technology of Electromechanical Products (2016), Chongqing Univ. Chongqing, China; (2014), G. M, Digital twin: manufacturing excellence through virtual factory replication; Tao, F., Zhang, M., Liu, Y., Nee, A.Y.C., Digital twin driven prognostics and health management for complex equipment (2018) CIRP Ann.-Manuf. Technol., 67, pp. 169-172; Liang, H., Wang, J., Sun, Z., Robust decentralized coordinated attitude control of spacecraft formation (2011) Acta Astronaut, 69, pp. 280-288; Guo, P., Fang, L., Deng, H., Zhang, N., Li, N., Research on wearing life calculation of worm gear of elevating mechanism (2014) J. Ordnance Eng. Coll., 26, pp. 30-33; Pan, D., Zhao, Y., Li, N., Wang, X., The wear life prediction method of gear system (2012) J. Harbin Inst. Technol., 44, pp. 29-33","Ran, Y.; State Key Laboratory of Mechanical Transmissions, China; email: ranyan@cqu.edu.cn",,,"Elsevier Ltd",,,,,07365845,,RCIME,,"English","Rob Comput Integr Manuf",Article,"Final","",Scopus,2-s2.0-85111616270
"Zhao L., Fang Y., Lou P., Yan J., Xiao A.","57222541383;55469295000;36899486300;7403729116;57532758800;","Cutting Parameter Optimization for Reducing Carbon Emissions Using Digital Twin",2021,"International Journal of Precision Engineering and Manufacturing","22","5",,"933","949",,14,"10.1007/s12541-021-00486-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103132627&doi=10.1007%2fs12541-021-00486-1&partnerID=40&md5=984d8b32c6dbc6a920f4aa22eab666ae","School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China; Mechanical Engineering Technology Department, New York City College of Technology, Brooklyn, NY  11201, United States","Zhao, L., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China; Fang, Y., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China; Lou, P., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China; Yan, J., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China; Xiao, A., Mechanical Engineering Technology Department, New York City College of Technology, Brooklyn, NY  11201, United States","With the exacerbation of global environmental concerns, manufacturing industries need to consider the impact of carbon emissions from manufacturing processes. The selection of the parameters in the machining process greatly influences on carbon emissions and machining efficiency. Hence dynamically optimizing the machining process parameters is a significant means to reduce carbon emissions according to the real-time perception of the machining conditions. In the paper, a method of cutting parameter optimization is presented on basis of the construction the digital twin of a CNC machine tool. In this method, an ontology on CNC machining process is established to be used as a communication bridge for understanding the semantic of the real-time interaction between the physical machine and the virtual twin. And a dynamic optimization method on cutting parameters is presented according to the simulation and optimization of the virtual twin with the dynamic perception of the machining conditions of the physical machine. At last, a case study is presented to validate this method for effectively optimizing the cutting parameters and decreasing carbon emissions. © 2021, Korean Society for Precision Engineering.","Carbon emissions; Cutting parameter optimization; Digital twin; Machining efficiency; Virtual-physical interaction","Computer control systems; Digital twin; Dynamics; Machining centers; Manufacture; Semantics; Turning; Cutting parameter optimization; Dynamic optimization; Environmental concerns; Machining process parameters; Manufacturing industries; Manufacturing process; Real time interactions; Simulation and optimization; Carbon",,,,,"WUT:2019III071GX; 2020010601012176; International Science and Technology Cooperation Programme, ISTCP: 2015DFA70340","The authors would like to acknowledge funding support from application foundation frontier special project of Wuhan Science and Technology Bureau(No.2020010601012176), the International Science & Technology Cooperation Program of China (Grant No.2015DFA70340), and the Fundamental Research Funds for the Central Universities of Wuhan University of Technology (WUT:2019III071GX), as well as the contributions from all collaborators within the projects mentioned.","The authors would like to acknowledge funding support from application foundation frontier special project of Wuhan Science and Technology Bureau(No.2020010601012176), the International Science & Technology Cooperation Program of China (Grant No.2015DFA70340), and the Fundamental Research Funds for the Central Universities of Wuhan University of Technology (WUT:2019III071GX), as well as the contributions from all collaborators within the projects mentioned.",,,,,,,,,"Hosseinabad, E.R., Moraga, R.J., A system dynamics approach in air pollution mitigation of metropolitan areas with sustainable development perspective: A case study of mexico city (2017) Journal of Applied Environmental and Biological Sciences, 7 (12), pp. 164-174; 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"Shao S., Zhou Z., Deng G., Du P., Jian C., Yu Z.","57215084079;15830628600;57201408119;57215083266;57215089050;57215088172;","Experiment of structural geometric morphology monitoring for bridges using holographic visual sensor",2020,"Sensors (Switzerland)","20","4","1187","","",,13,"10.3390/s20041187","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079884461&doi=10.3390%2fs20041187&partnerID=40&md5=b5ade0d6eee3b69933e4f456ab6faf21","School of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, China; College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518061, China","Shao, S., School of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, China, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518061, China; Zhou, Z., College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, 518061, China; Deng, G., School of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, China; Du, P., School of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, China; Jian, C., School of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, China; Yu, Z., School of Civil Engineering, Chongqing Jiaotong University, Chongqing, 400074, China","To further improve the precision and efficiency of structural health monitoring technology and the theory of large‐scale structures, full‐field non‐contact structural geometry morphology monitoring is expected to be a breakthrough technology in structural safety state monitoring and digital twins, owing to its economic, credible, high frequency, and holographic advantages. This study validates a proposed holographic visual sensor and algorithms in a computer‐vision‐based full‐field non‐contact displacement and vibration measurement. Using an automatic camera patrol experimental device, original segmental dynamic and static video monitoring data of a model bridge under various damage/activities were collected. According to the temporal and spatial characteristics of the series data, the holographic geometric morphology tracking algorithm was introduced. Additionally, the feature points set of the structural holography geometry and the holography feature contours were established. Experimental results show that the holographic visual sensor and the proposed algorithms can extract an accurate holographic full‐field displacement signal, and factually and sensitively accomplish vibration measurement, while accurately reflecting the real change in structural properties under various damage/action conditions. The proposed method can serve as a foundation for further research on digital twins for large‐scale structures, structural condition assessment, and intelligent damage identification. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Bridge safety; Computer‐vision‐based measurement technology; Dense full‐field measurement; Digital twins; Holographic visual sensor; Structural geometry monitoring","Damage detection; Digital twin; Displacement measurement; Electric measuring bridges; Geometry; Holography; Monitoring; Morphology; Vibration measurement; Bridge safety; Field measurement; Measurement technologies; Structural geometry; Visual sensor; Structural health monitoring",,,,,"National Natural Science Foundation of China, NSFC: 51778094; China National Funds for Distinguished Young Scientists: 51608080, 51708068; Chongqing Jiaotong University, CQJTU: 2019S0141","Funding: This research was funded by the National Natural Science Foundation of China (Grant No. 51778094), the National Science Foundation for Distinguished Young Scholars of China (Grant No. 51608080), and the National Science Foundation for Distinguished Young Scholars of China (Grant No. 51708068), and the Science and Technology Innovation Project of Chongqing Jiaotong University (Grant No. 2019S0141).",,,,,,,,,,"Feng, D.M., Feng, M.Q., Computer Vision for SHM of Civil Infrastructure: From Dynamic Response Measurement to Damage Detection‐A review (2018) Eng. 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Graph., 34, pp. 1-7","Zhou, Z.; College of Civil and Transportation Engineering, China; email: zhixiangzhou@szu.edu.cn",,,"MDPI AG",,,,,14248220,,,"32098079","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85079884461
"Zhang Z., Guan Z., Gong Y., Luo D., Yue L.","57220127842;7202542276;24767308200;57201590750;36718453300;","Improved multi-fidelity simulation-based optimisation: application in a digital twin shop floor",2022,"International Journal of Production Research","60","3",,"1016","1035",,11,"10.1080/00207543.2020.1849846","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097015794&doi=10.1080%2f00207543.2020.1849846&partnerID=40&md5=e1b54ca55a9eb62265c6e8c05a47b7a8","School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China; EMLYON Business School, Ecully, France","Zhang, Z., School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China; Guan, Z., School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China; Gong, Y., EMLYON Business School, Ecully, France; Luo, D., School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China; Yue, L., School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China","In recent years, the literature has paid considerable attention to digital twin technology for the implementation of Industry 4.0 and intelligent manufacturing. Most of the literature argues that simulation models are a key platform for digital twins and considers discrete-event simulation to be a suitable method to model real dynamic manufacturing systems. However, the discrete-event simulation of complex manufacturing systems is a time-consuming process. Therefore, it is difficult to deal with the large-scale discrete optimisation problems in digital twin shop floors. To bridge this research gap, we propose an improved multi-fidelity simulation-based optimisation method based on multi-fidelity optimisation with ordinal transformation and optimal sampling (MO2TOS) in the current research. The proposed method embeds heuristic algorithms to accelerate the solution space search efficiency in MO2TOS. Moreover, we develop an improved multi-fidelity simulation-based optimisation system by integrating the proposed method with discrete-event simulation tools and apply this system to a digital twin-based aircraft parts production workshop. Based on this digital twin shop floor, we conduct different production planning experiments to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed improved multi-fidelity simulation-based optimisation method is well-applied in solving large-scale problems and outperforms other simulation-based optimisation methods. © 2020 Informa UK Limited, trading as Taylor & Francis Group.","digital twin shop floor; heuristic algorithms; large-scale problem optimisation; Multi-fidelity simulation-based optimisation; simulation","Discrete event simulation; Floors; Heuristic algorithms; Heuristic methods; Manufacture; Optimization; Production control; Complex manufacturing systems; Discrete optimisation; Intelligent Manufacturing; Large-scale problem; Multi-fidelity simulation; Production Planning; Production workshops; Simulation based optimisation; Digital twin",,,,,"National Natural Science Foundation of China, NSFC: 51561125002, 51705379, 71620107002; China Postdoctoral Science Foundation: 2019M652665; Research Grants Council, University Grants Committee, RGC, UGC; National Key Research and Development Program of China, NKRDPC: 2018YFB1702700, 51905196","This work was supported by China Postdoctoral Science Foundation [grant number 2019M652665]; National Natural Science Foundation of China: [grant number 51561125002, 71620107002 & 51705379]; National Key Research and Development Program of China [grant number 2018YFB1702700]; Youth Program of National Natural Science Foundation of China [grant number 51905196]. Yeming (Yale) Gong is supported by Business Intelligence Center and EMLYON Shanghai Campus.","Zailin Guan graduated from Huazhong University of Science and Technology, Wuhan, China in 1997. He did postdoctoral research at Hong Kong University of Science and Technology in 1999. Currently he is a Professor in department of industrial engineering, school of mechanical science and engineering, Huazhong University of Science and Technology, Wuhan, China. He received modern industrial production management training in 2001 at Rheinisch-Westfaelische Technische Hochschule Aachen. He has long been engaged in the research of new model and mechanism of multi-species small batch production operation control as well as the research and application of Advanced Planning and Scheduling. He has presided over two projects of National Natural Science Foundation and one project of 863 program. He has participated in a program jointly funded by National Natural Science Foundation and Hong Kong Research Grants Council and a program of EU FP6 cooperation. Prof. Guan is a member of State Key Lab of Digital Manufacturing Equipment and Technology, HUST-SANY Joint Lab of Advanced Manufacturing, Huazhong University of Science and Technology (HUST), Wuhan, China. 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"Shim C.S., Kang H.R., Dang N.S.","7103280900;57200211808;57200211416;","Digital twin models for maintenance of cable-supported bridges",2019,"International Conference on Smart Infrastructure and Construction 2019, ICSIC 2019: Driving Data-Informed Decision-Making",,,,"737","742",,11,"10.1680/icsic.64669.737","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087977463&doi=10.1680%2ficsic.64669.737&partnerID=40&md5=de14960b085db97d9142a354ce463283","Department of Civil Engineering, Chung-Ang University, Seoul, South Korea","Shim, C.S., Department of Civil Engineering, Chung-Ang University, Seoul, South Korea; Kang, H.R., Department of Civil Engineering, Chung-Ang University, Seoul, South Korea; Dang, N.S., Department of Civil Engineering, Chung-Ang University, Seoul, South Korea","Recently, long-span bridges have more than 100 years of service life. Since cable-supported bridges are key links in transportation networks, it is essential to manage the structures in a good service state. Most cable-supported bridges have bridge maintenance systems with monitoring sensors. However, records of damage and their repairs are not well managed. In this paper, a new bridge maintenance system was designed using a digital twin model concept. According to different maintenance tasks, inventory and information requirements were investigated. The digital model is a platform to collect, curate and share the maintenance history. For certain events, the model can be utilized for the analysis to consider any change of structural parameters. Recorded responses from sensors are used to update the digital twin model. Based on the design of the maintenance system, a pilot application on an existing cable-stayed bridge was conducted for a year and feedback was discussed. Extension of the application for a suspension bridge was also presented to generalize the methodology. © International Conference on Smart Infrastructure and Construction 2019, ICSIC 2019: Driving Data-Informed Decision-Making.",,"Cables; Decision making; Digital twin; Maintenance; Bridge maintenance systems; Cable-supported bridges; Information requirement; Maintenance systems; Monitoring sensors; Pilot applications; Structural parameter; Transportation network; Cable stayed bridges",,,,,"Kala Art Institute; Ministry of Land, Infrastructure and Transport, MOLIT","This research was supported by a grant ""Development of lifecycle engineering technique and construction method for global competitiveness upgrade of cable bridges (16SCIP-B119960-01)"" from Smart Civil Infrastructure Research Program funded by MOLIT and KALA.",,,,,,,,,,"Aron, P, Lane, W, Industry 4.0 and the Digital Twin (2017) Deloitte Anjin Review, (9), pp. 64-72; Atorod, A, (2013) Design Guide for Bridges for Service Life, , Strategic Highway Research Program, Transportation Research Board, SHRP 2 Renewal Project R19A; Chang, SP, Health monitoring system of a selfanchored suspension bridge (planning, design and installation/operation) (2008) Structure and Infrastructure Engineering, 4 (3), pp. 193-205; Dang, NS, 3D digital twin models for bridge maintenance (2018) Proceedings of 10th International Conference on Short and Medium Span Bridges, , Quebec city, Quebec, Canada; Dang, NS, Shim, CS, BIM authoring for an imagebased bridge maintenance system of existing cable-supported bridges (2018) IOP Conference Series: Earth and Environmental Science, 143 (1), p. 012032; (2012) Korean Bridge Technology, 18th IABSE Congress, , Korean Group of IABSE Seoul, Korea; Lee, KM, Bridge information models for construction of a concrete box-girder bridge (2010) Structure and Infrastructure Engineering, 8 (7), pp. 687-703; Mahmoud, KM, (2011) BTC Method for Evaluation of Remaining Strength and Service Life of Bridge Cables, , New York State Bridge Authority, New York, NY, USA, NYSDOT Report C-07-11; Maybaurl, RM, Camo, S, (2004) Guidelines for Inspection and Strength Evaluation of Suspension Bridge Parallel Wire Cables, , Transportation Research Board, Washington DC, USA, Report NCHRP 534; Petersen, ØW, Oiseth, Ø, Finite element model updating of a long span suspension bridge (2017) Proceedings of International Conference on Earthquake engineering and Structural dynamics, , Reykjavik, Iceland; Shim, CS, Development of BIM for a Maintenance System of Subway Infrastructure (2011) Journal of KIBIM, 1 (1), pp. 6-12. , (in Korean); Shim, CS, Application of 3D Bridge Information Modeling to Design and Construction of Bridges (2011) Procedía Engineering, 14, pp. 95-99; Shim, CS, Three-Dimensional Information Modelbased Bridge Engineering in Korea (2012) Structural Engineering International, 22 (1), pp. 8-13; Shim, CS, 3D Information Model Based Bridge maintenance (2016) Proceedings of the 5th International Technical Conference, p. 449455. , Kota Kinabalu, Malaysia, Nov; Shim, CS, Development of BIM-based bridge maintenance system for cable-stayed bridges (2017) Smart Structures System, 20 (6), pp. 697-708; Shim, CS, Development of a bridge maintenance system for PSC bridges using 3D digital twin model (2019) Structure and Infrastructure Engineering, , Under review","Shim, C.S.; Department of Civil Engineering, South Korea","DeJong M.J.Schooling J.M.Viggiani G.M.B.",,"ICE Publishing","2nd International Conference on Smart Infrastructure and Construction: Driving Data-Informed Decision-Making, ICSIC 2019","1 July 2019 through 3 July 2019",,164083,,9780727764669,,,"English","Int. Conf. Smart Infrastruct. Constr., ICSIC : Driv. Data-Inf. Decis.-Mak.",Conference Paper,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85087977463
"Meixedo A., Santos J., Ribeiro D., Calçada R., Todd M.D.","56940709200;36810314200;24476782300;7801603531;7202805915;","Online unsupervised detection of structural changes using train–induced dynamic responses",2022,"Mechanical Systems and Signal Processing","165",,"108268","","",,10,"10.1016/j.ymssp.2021.108268","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112019043&doi=10.1016%2fj.ymssp.2021.108268&partnerID=40&md5=9563683e0a15f5f35befc0cd28a1d8e1","CONSTRUCT-LESE, Faculty of Engineering, University of Porto, Portugal; LNEC, Laboratório Nacional de Engenharia Civil, Portugal; CONSTRUCT-LESE, School of Engineering, Polytechnic of Porto, Portugal; Department of Structural Engineering, University California San Diego, United States","Meixedo, A., CONSTRUCT-LESE, Faculty of Engineering, University of Porto, Portugal; Santos, J., LNEC, Laboratório Nacional de Engenharia Civil, Portugal; Ribeiro, D., CONSTRUCT-LESE, School of Engineering, Polytechnic of Porto, Portugal; Calçada, R., CONSTRUCT-LESE, Faculty of Engineering, University of Porto, Portugal; Todd, M.D., Department of Structural Engineering, University California San Diego, United States","This paper exploits unsupervised data-driven structural health monitoring (SHM) in order to propose a continuous online procedure for damage detection based on train-induced dynamic bridge responses, taking advantage of the large-magnitude loading for enhancing sensitivity to small-scale structural changes. While such large responses induced by trains might create more damage-sensitive information in the measured response, it also amplifies the effects on those measurements from the environment. Thus, one of the biggest contributions herein is a methodology that exploits the large bridge responses induced by train passage while rejecting the confounding influences of the environment in such a way that false positive detections are mitigated. Furthermore, this research work introduces an adaptable confidence decision threshold that further improves damage detection over time. To ensure an online continuous assessment, a hybrid combination of autoregressive exogenous input (ARX) models, principal components analysis (PCA), and clustering algorithms was sequentially applied to the monitoring data, in a moving window process. A comparison between the performance obtained from autoregressive (AR) and ARX models as feature extractors was conducted, and it was concluded that ARX models lead to increased sensitivity to damage due to their ability to capture cross information between the sensors. The PCA proved its importance and effectiveness in removing observable changes induced by variations in train speed or temperature without the need to measure them, and the clustering methods allowed for an automatic classification of the damage-sensitive features. Since it was not possible to introduce damage to the bridge, several structural conditions were simulated with a highly reliable digital twin of the Sado Bridge, tuned with experimental data acquired from a SHM system installed on site, in order to test and validate the efficiency of the proposed procedure. The strategy proved to be robust when detecting a comprehensive set of damage scenarios with a false detection incidence of 2%. Moreover, it showed sensitivity to smaller damage levels (earlier in life), even when it consists of small stiffness reductions that do not impair structural safety and are imperceptible in the original signals. © 2021 Elsevier Ltd","ARX model; Cluster analysis; Damage detection; Online assessment; PCA; Structural health monitoring; Traffic-induced dynamic responses; Unsupervised learning","Bridges; Cluster analysis; Clustering algorithms; Damage detection; E-learning; Principal component analysis; Structural health monitoring; Unsupervised learning; ARX model; Bridge response; Data driven; Health monitoring; Online assessments; Principal-component analysis; Structural health; Traffic-induced dynamic response; Unsupervised data; Unsupervised detection; Dynamic response",,,,,"POCI-01-0145-FEDER-031054; Fundação para a Ciência e a Tecnologia, FCT: SFRH/BD/93201/2013; Ministério da Ciência, Tecnologia e Ensino Superior, MCTES; Institute of Research and Development in Structures and Construction","This work was financially supported by the Portuguese Foundation for Science and Technology (FCT) through the PhD scholarship SFRH/BD/93201/2013. The authors would like to acknowledge the support of the Portuguese Road and Railway Infrastructure Manager (Infraestruturas de Portugal, I.P), the Portuguese National Laboratory for Civil Engineering (LNEC), the SAFESUSPENSE project - POCI-01-0145-FEDER-031054 (funded by COMPETE2020, POR Lisboa and FCT) and the Base Funding - UIDB/04708/2020 of the CONSTRUCT - Instituto de I&D em Estruturas e Construções - financed by national funds through the FCT/MCTES (PIDDAC).",,,,,,,,,,"Huynh, C.P., Mustapha, S., Runcie, P., Porikli, F., Multi-class support vector machines for paint condition assessment on the Sydney Harbour Bridge using hyperspectral imaging (2015) Struct. Monitor. Maintenance, 2 (3), pp. 181-197; C.R. Farrar K. Worden Structural Health Monitoring: a machine learning perspective. Wiley 2013 1 45; Mustapha, S., Braytee, A., Ye, L., Multisource data fusion for classification of surface cracks in steel pipes (2018) J. Nondestructive Eval. Diagnost. Prognost. Eng. 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Process., 19, pp. 847-864; Alvandi, A., Cremona, C., Assessment of vibration-based damage identification techniques (2006) J. Sound Vib., 292 (1-2), pp. 179-202; , pp. 998-1005. , A. Meixedo V. Alves D. Ribeiro A. Cury R. Calçada. Damage identification of a railway bridge based on genetic algorithms. In: Maintenance, Monitoring, Safety, Risk and Resilience of Bridges and Bridge Networks - Proceedings of the 8th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2016; A.C. Neves I. González R. Karoumi J. Leander The influence of frequency content on the performance of artificial neural network – based damage detection systems tested on numerical and experimental bridge data Structural Health Monitoring 20 3 2021 1331 1347 10.1177/1475921720924320; Santos, J.P., Crémona, C., Calado, L., Silveira, P., Orcesi, A.D., On-line unsupervised detection of early damage (2015) Struct. 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"Anyfantis K.N.","35084921800;","An abstract approach toward the structural digital twin of ship hulls: A numerical study applied to a box girder geometry",2021,"Proceedings of the Institution of Mechanical Engineers Part M: Journal of Engineering for the Maritime Environment","235","3",,"718","736",,10,"10.1177/1475090221989188","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100049675&doi=10.1177%2f1475090221989188&partnerID=40&md5=5e6d99b564cd733cec76bb8db2445a36","Shipbuilding Technology Laboratory, School of Naval Architecture and Marine Engineering, National Technical University of Athens, Zografos, Athens, Greece","Anyfantis, K.N., Shipbuilding Technology Laboratory, School of Naval Architecture and Marine Engineering, National Technical University of Athens, Zografos, Athens, Greece","Condition monitoring (CM) of ship hull structures is a promising field that has recently attracted the interested of researches. The main challenge behind CM is to develop a system that gets as input sensor readings from the structure and provide the damage locus as an output. In this regard, the current study proposes two alternative CM digital twin schemes for solving this inverse engineering problem. The first one is based on a Finite Element (FE) – Optimization cooperative framework that solves several times the model until the predicted strains match the measured ones and as such the damage location has been found. The other scheme is based on a cooperative framework of Artificial Neural Networks (ANNs) used for classification and fitting, that may be regarded as surrogated models which provide solutions instantaneously. The ANNs are trained through the numerical solutions provided by the FE model. A thin-walled hollow cantilever beam, that resembles a hull-girder subjected to principal stresses under vertical bending, has been adopted. The performed work has allowed for the selection and evaluation of the locations for sensor placement and the estimation of the damage sensitive area for monitoring. Both CM digital twin schemes have proven to be promising for the theoretical simplified examined case. © IMechE 2021.","artificial neural networks; condition monitoring; optimization; ship structure; Structural digital twin","Box girder bridges; Condition monitoring; Digital twin; Inverse problems; Neural networks; Thin walled structures; Cooperative frameworks; Inverse engineering; Numerical solution; Principal stress; Selection and evaluations; Sensor placement; Ship hull structure; Surrogated models; Hulls (ship)",,,,,,"The author received no financial support for the research, authorship, and/or publication of this article.",,,,,,,,,,"Cui, Y., Kara, S., Chana, K.C., Manufacturing big data ecosystem: a systematic literature review (2020) Robot Comput Integr Manuf, 62, p. 101861; de Treville, S., Ketokivi, M., Singhal, V., Competitive manufacturing in a high-cost environment: introduction to the special issue (2017) J Oper Manage, 49-51, pp. 1-5; Stanić, V., Hadjina, M., Fafandjel, N., Toward Shipbuilding 4.0 - An Industry 4.0 changing the face of the Shipbuilding industry (2018) Brodogradnja, 69, pp. 111-128; Alfredo, A., Li, Y., Chen, W., Industry 4.0 with cyber-physical integration: a design and manufacture perspective, , 21st International Conference on Automation and Computing (ICAC), Glasgow, UK, 11–12 September 2015, In; Lu, Y., Liu, C., Kevin, I., Digital Twin-driven smart manufacturing: connotation, reference model, applications and research issues (2020) Robot Comput Integr Manuf, 61, p. 101837; Worden, K., Cross, E.J., Gardner, P., On digital twins, mirrors and virtualisations (2020) Model validation and uncertainty quantification, 3, pp. 285-295. , Barthorpe R., (ed), Conference proceedings of the society for experimental mechanics series, Springer, Cham, In:, (eds; Boschert, S., Rosen, R., (2016) Digital twin—the simulation aspect, pp. 59-74. , Cham, Springer International Publishing; (2018) Digital twin report for DMA, Digital Twins for Blue Denmark, Danish Maritime Authority, , https://www.dma.dk/Documents/Publikationer/Digital%20Twin%20report%20for%20DMA.PDF, accessed August 2020; (2019) Pioneering ABS and MSC condition-based class program showcased at mega rust, , https://ww2.eagle.org/en/news/press-room/abs-msc-condition-based-class.html, accessed August 2020, 2019; Farrar, C., Worden, K., An introduction to structural health monitoring (2007) Philos Trans A Math Phys Eng Sci, 365, pp. 303-315; (2007) No.76. 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Neuilly-sur-Seine, France, RTO, :,.22-1–22-14; Fujikubo, M., Digital twin for ship structures – research project in Japan - Proceedings of the 14th international symposium on practical design of ships and other floating structures (PRADS 2019), , Yokohama, Japan, 22–26 September 2019, In; Anyfantis, K., Evaluating the influence of geometric distortions to the buckling capacity of stiffened panels (2019) Thin-Walled Struct, 140, pp. 450-465; Gupta, D.K., (2013) Inverse methods for load identification augmented by optimal sensor placement and model order reduction, in engineering, , University of Wisconsin Milwaukee, PhD Thesis; Kammer, D.C., Sensor placement for on-orbit modal identification and correlation of large space structures (1991) J Guid Control Dyn, 14, pp. 251-259; Yusoff, Y., Ngadiman, M.S., Zain, A.M., Overview of NSGA-II for optimizing machining process parameters (2011) Procedia Eng, 15, pp. 3978-3983; Tian, Y., Wang, H., Zhang, X., Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization (2017) Complex Intell Syst, 3, pp. 247-263; Garrett, J.H., Where and why artificial neural networks are applicable in civil engineering (1994) J Comput Civ Eng, 8, pp. 129-130; Papadrakakis, M., Lagaros, N., Tsompanakis, Y., Optimization of large-scale 3-D trusses using evolution strategies and neural networks (1999) Int J Space Struct, 14, pp. 211-223; Tahir, Z., Mandal, P., Artificial neural network prediction of buckling load of thin cylindrical shells under axial compression (2017) Eng Struct, 152, pp. 843-855; Toktas, I., Murat, Ö., Fulya, E., Stress concentration factor Kt determination for A Crankshaft İn bending loading: an artificial neural networks approach (2020) J Polytech, 23, pp. 813-819","Anyfantis, K.N.; Shipbuilding Technology Laboratory, Greece; email: kanyf@naval.ntua.gr",,,"SAGE Publications Ltd",,,,,14750902,,,,"English","Proc. Inst. Mech. Eng. Part M J. Eng. Marit. Environ.",Article,"Final","",Scopus,2-s2.0-85100049675
"Dang N.S., Shim C.S.","57200211416;7103280900;","Bridge assessment for PSC girder bridge using digital twins model",2020,"Lecture Notes in Civil Engineering","54",,,"1241","1246",,10,"10.1007/978-981-15-0802-8_199","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073693163&doi=10.1007%2f978-981-15-0802-8_199&partnerID=40&md5=8835ece46d84c35e887585d64d06fef7","Chung-Ang University, Seoul, South Korea","Dang, N.S., Chung-Ang University, Seoul, South Korea; Shim, C.S., Chung-Ang University, Seoul, South Korea","Bridge maintenance nowadays is no longer a “reactive activity” to the severe degradation or unexpected disaster. Aging and deterioration are inevitable, sometimes arises exponentially if maintenance strategy is neglected. Leading to structural failure, or worse case is the functional failure and seriously threaten the safety, interrupt the public transportation system. This paper proposes a new concept for preventive maintenance strategy for existing aged PSC Girder Bridge using a digital twin model. Digital Twins concept is mainly based on the use of parallel models: digital twin model (DTM), reality twin model (RTM) and mechanical twin model (MTM). DTM authoring is introduced using an integrated BIM mod-el, while RTM is verified by a reversed engineering surface model based on 3D scanning data. The RTM is generated continuously during bridge lifecycle in order to make a field-verified replica of the structure, aims to records all the bridge damage time by time. The mechanical twin model is derived directly from DTM through the interoperability of BIM model and adjusted by damage information from RTM. Discussion on bridge assessment model is induced based on the supposed analysis model and deterioration history, significant support for the decision making team in order to make a long-term strategy for bridge maintenance task. © Springer Nature Singapore Pte Ltd. 2020.","3D Scanning; BIM; DTM; Preventive Maintenance; Reality Model","3D modeling; Bridges; Decision making; Deterioration; Failure (mechanical); Fracture mechanics; Life cycle; Preventive maintenance; 3D-scanning; Bridge maintenance; Engineering surfaces; Functional failure; Long-term strategy; Maintenance strategies; Public transportation systems; Structural failure; Digital twin",,,,,"Ministry of Land, Infrastructure and Transport, MOLIT; Korea Agency for Infrastructure Technology Advancement, KAIA","This research was supported by a grant “Development of life-cycle engineering technique and construction method for global competitiveness upgrade of cable bridges (16SCIP-B119960-01)” from Smart Civil Infrastructure Research Program funded by MOLIT and KAIA.",,,,,,,,,,"Shim, C.S., Kang, H.R., Dang, N.S., Lee, D., K: Development of BIM-based bridge maintenance system for cable-stayed bridges (2017) Smart Structures and Systems, 20 (6), pp. 697-708; Dang, N.S., Shim, C.S., BIM authoring for an image-based bridge maintenance system of existing cable-supported bridges (2018) IOP Conference Series: Earth and Environmental Science, 143 (1); Dang, N.S., Kang, H.R., Lon, S., Shim, C.S., 3D digital twin models for bridge maintenance (2018) Proceedings of 10Th International Conference on Short and Medium Span Bridges, , Quebec city, Quebec, Canada; Saydam, D., Frangopol, D.M., Time-dependent performance indicators of damaged bridge superstructures (2011) Engineering Structures, 33 (9), pp. 2458-2471; Yang, S.I., Frangopol, D.M., Neves, L.C., Optimum maintenance strategy for deteriorating bridge structures based on lifetime functions (2006) Engineering Structures, 28 (2), pp. 196-206; Shim, C.S., Dang, N.S., Lon, S., Jeon, C.H., Development of a Bridge Maintenance System for PSC Bridges Using 3D Digital Twin Model, Structure and Infrastructure Engineering, , (Accept on 26-Feb-2019)","Dang, N.S.; Chung-Ang UniversitySouth Korea; email: dangngocson@cau.ac.kr",,,"Springer",,,,,23662557,,,,"English","Lect. Notes Civ. Eng.",Book Chapter,"Final","",Scopus,2-s2.0-85073693163
"Xi T., Benincá I.M., Kehne S., Fey M., Brecher C.","57216590065;57222329938;57194145500;36521055200;55947597300;","Tool wear monitoring in roughing and finishing processes based on machine internal data",2021,"International Journal of Advanced Manufacturing Technology","113","11-12",,"3543","3554",,9,"10.1007/s00170-021-06748-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102240265&doi=10.1007%2fs00170-021-06748-6&partnerID=40&md5=211c5562f76fa8d4dfd637d8c3f8ab9f","Laboratory for Machine Tools and Production Engineering, (WZL) of RWTH Aachen University, Steinbachstr. 19, Aachen, 52074, Germany","Xi, T., Laboratory for Machine Tools and Production Engineering, (WZL) of RWTH Aachen University, Steinbachstr. 19, Aachen, 52074, Germany; Benincá, I.M., Laboratory for Machine Tools and Production Engineering, (WZL) of RWTH Aachen University, Steinbachstr. 19, Aachen, 52074, Germany; Kehne, S., Laboratory for Machine Tools and Production Engineering, (WZL) of RWTH Aachen University, Steinbachstr. 19, Aachen, 52074, Germany; Fey, M., Laboratory for Machine Tools and Production Engineering, (WZL) of RWTH Aachen University, Steinbachstr. 19, Aachen, 52074, Germany; Brecher, C., Laboratory for Machine Tools and Production Engineering, (WZL) of RWTH Aachen University, Steinbachstr. 19, Aachen, 52074, Germany","Data analytics plays a significant role in the realization of Industry 4.0. By generating context-related persistent datasets, every manufacturing process in real production becomes an experiment. The vision of Internet of Production (IoP) is to enable real-time diagnosis and prediction in smart productions by acquiring datasets seamlessly from different data silos. This requires interdisciplinary collaboration and domain-specific expertise. In this paper, we present a novel tool wear monitoring system for milling process developed in the context of IoP. This system is based on high-frequency data from the numerical control of the production machine without additional sensors. The novelty of this paper lies in the introduction of virtual workpiece quality and fusion of multiple build-in sensor signals and a force model as decision support. This bridges the time gap between quality inspection and production at the shop floor level, establishes an automated statistical process control system, and provides a more plausible prediction of tool lifetime. The monitoring of two different milling processes in a real production environment is exemplary demonstrated in this paper. The first case is a face roughing process with the aim of rapidly removing large amounts of material. The second case is a face finishing operation that follows roughing and aims to achieve the desired surface quality. © 2021, The Author(s).","Condition monitoring; Data analytics; Digital twin; Quality inspection; Tool wear","Cutting tools; Data Analytics; Decision support systems; Milling (machining); Wear of materials; High frequency data; Interdisciplinary collaborations; Manufacturing process; Production environments; Production machines; Real-time diagnosis; Roughing and finishing; Tool wear monitoring; Statistical process control",,,,,,"The authors would like to thank Heidelberger Druckmaschinen AG for supporting this study.",,,,,,,,,,"Schuh, G., Prote, J., Gützlaff, A., Thomas, K., Sauermann, F., Rodemann, N., (2019), Wulfsberg J, Hintze W, Behrens BA; Pennekamp, J., Towards an infrastructure enabling the internet of production (2019) ICPS, 2019, pp. 31-37. , https://doi.org/10.1109/ICPHYS.2019.8780276; Brecher, C., Epple, A., Knape, S., Schmidt, S., Network architecture components for shop floor level (Translated form German) (2018) Zeitschrift für Wirtschaftlichen Fabrikbetrieb (ZWF), 113 (5), pp. 342-345; Königs, M., Brecher, C., Process-parallel virtual quality evaluation for metal cutting in series production (2018) Procedia Manuf, 26, pp. 1087-1093; Brecher, C., Wiesch, M., Wellmann, F., Productivity Increase – Model-based optimisation of NC-controlled milling processes to reduce machining time and improve process quality (2019) IFAC Papersonline, 52 (13), pp. 1803-1807. , DOI: 10.1016/j.ifacol.2019.11.463; Motorcu, A.R., Güllü, A., Statistical process control in machining, a case study for machine tool capability and process capability (2006) Mater Des, 27, pp. 364-372; Macgregor, J.F., Kourti, T., Statistical process control of multivariate processes (1995) Control Eng Pract, 3, pp. 403-414; Zhou, Y., Xue, W., Review of tool condition monitoring methods in milling processes (2018) Int J Adv Manuf Technol, 96, pp. 2509-2523. , DOI: 10.1007/s00170-018-1768-5; Jurkovic, J., Korosec, M., Kopac, J., New approach in tool wear measuring technique using CCD vision system (2005) Int J Mach Tool Manuf, 45, pp. 1023-1030; Wang, M., Wang, J., CHMM For tool condition monitoring and remaining useful life prediction (2012) Int J Adv Manuf Technol, 59, pp. 463-471; Bhattacharyya, P., Sengupta, D., Mukhopadhyay, S., Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques (2007) Mech Syst Signal Process, 21, pp. 2665-2683; Cheng, K., Niu, Z.C., Wang, R.C., Smart cutting tools and smart machining: Development approaches, and their implementation and application perspectives (2017) Chin J Mech Eng, 30, pp. 1162-1176; Wang, C., Cheng, K., Minton, T., Rakowski, R., Development of a novel surface acoustic wave (SAW) based smart cutting tool in machining hybrid dissimilar material (2014) Manufacturing Letters, 2, pp. 21-25. , (,),., (,)., https://doi.org/10.1016/j.mfglet.2013.12.003; Nouri, M., Fussell, B., Ziniti, B., Linder, E., Real-time tool wear monitoring in milling using a cutting condition independent method (2015) Int J Mach Tool Manuf, 89, pp. 1-13. , DOI: 10.1016/j.ijmachtools.2014.10.011; Choudhury, S.K., Rath, S., In-process tool wear estimation in milling using cutting force model (2000) J Mater Process Technol, 99 (1), pp. 113-119; Ammouri, A.H., Hamade, R.F., Current rise criterion: A process independent method for tool-condition monitoring and prognostics (2014) Int J Adv Manuf Technol, 72, pp. 509-519; Rizal, M., Ghani, J.A., Nuawi, M., Che, H., A review of sensor system and application in milling process for tool condition monitoring (2014) Res J Appl Sci Technol, 7 (10), pp. 2083-2097; Brecher, C., Epple, A., Fey, M., Königs, M., Neus, S., Wellmann, F., Self-Learning Production Systems (Translated from german: Lernende Produktionssysteme) (2017) In: Internet of Production für Agile Unternehmen: AWK Aachener Werkzeugmaschinen-Kolloquium 2017, Apprimus Verlag, pp. 135-161. , ISBN 978-3-86359-512-8; Wang, G., Yang, Y., Zhang, Y., Xie, Q., Vibration sensor based tool condition monitoring using ν support vector machine and locality preserving projection (2014) Sens Actuators a Phys, 209, pp. 24-32; Zhang, C., Yao, X., Zhang, J., Jin, H., Tool condition monitoring and remaining useful life prognostic based on a wireless sensor in dry milling operations (2016) Sensors, 16 (795), pp. 1-20. , 10.3390/s16060795; Zhang, X., Lu, X., Wang, S., Wang, W., Li, W., A multi-sensor based online tool condition monitoring system for milling process (2018) Procedia CIRP, 72, pp. 1136-1141; Huang, Z., Zhu, J., Lei, J., Li, X., Tian, F., Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations (2020) J Intell Manuf, 31, pp. 953-966. , DOI: 10.1007/s10845-019-01488-7; de Farias, A., de Almeida, S.L.R., Delijaicov, S., Seriacopi, V., Bordinassi, E., Simple machine learning allied with data-driven methods for monitoring tool wear in machining processes (2020) Int J Adv Manuf Technol, 109, pp. 2491-2501; Aslan, D., Altintas, Y., Prediction of cutting forces in 5-axis milling using feed drive current measurements (2018) IEEE/ASME Trans Mechatron, 23 (2), pp. 833-844; Fey, M., Epple, A., Kehne, S., Brecher, C., Method for determining the axle load on linear and rotary axes (2016) Patent DE102016013890A1; Brecher, C., Eckel, H., Motschke, T., Fey, M., Epple, A., Estimation of the virtual workpiece quality by the use of a spindle-integrated process force measurement (2019) CIRP Ann, 68, pp. 381-384","Xi, T.; Laboratory for Machine Tools and Production Engineering, Steinbachstr. 19, Germany; email: t.xi@wzl.rwth-aachen.de",,,"Springer Science and Business Media Deutschland GmbH",,,,,02683768,,IJATE,,"English","Int J Adv Manuf Technol",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85102240265
"Broo D.G., Schooling J.","55648593700;57189900369;","A Framework for Using Data as an Engineering Tool for Sustainable Cyber-Physical Systems",2021,"IEEE Access","9",,"9340179","22876","22882",,9,"10.1109/ACCESS.2021.3055652","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100461393&doi=10.1109%2fACCESS.2021.3055652&partnerID=40&md5=b92cc13246a1d6a17d92a74392764ef0","Department of Engineering, Centre for Smart Infrastructure and Construction, University of Cambridge, Cambridge, CB3 0FA, United Kingdom; Laing o'Rourke Centre for Construction Engineering and Technology, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, United Kingdom","Broo, D.G., Department of Engineering, Centre for Smart Infrastructure and Construction, University of Cambridge, Cambridge, CB3 0FA, United Kingdom, Laing o'Rourke Centre for Construction Engineering and Technology, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, United Kingdom; Schooling, J., Department of Engineering, Centre for Smart Infrastructure and Construction, University of Cambridge, Cambridge, CB3 0FA, United Kingdom","Smart infrastructure has the potential to revolutionise how infrastructure is delivered, managed and automatically controlled. Data and digital twins offer an opportunity to enable this revolution and secure sustainable future smart infrastructure. In this article, we discuss data as an engineering tool and propose to use data throughout the asset's whole life cycle from identifying the need, planning and designing to construction, operation, integration and maintenance. This requires systems thinking where focus is not limited to the problems but rather constructs a systemic perspective to understand the interrelationships between components and systems. Future infrastructure is connected, intelligent and data-driven. To enable more sustainable decision-making, we should not only consider how to integrate different infrastructure elements but also use data to monitor, learn from and inform decisions. To this end, we present a case study where several assets, such as bridges, railways and transport systems are integrated, and data are curated for the purpose of aiding climate-conscious, sustainable decision-making. An example systems architecture for integration of different digital twins is explained and benefits of this data-driven, systemic perspective are discussed. © 2013 IEEE.","cyber-physical system; Data science; information systems; sustainability; systems thinking","Cyber Physical System; Decision making; Digital twin; Embedded systems; Life cycle; Engineering tools; Planning and designings; Smart infrastructures; Sustainable decision makings; Systems architecture; Systems thinking; Transport systems; Whole life cycles; Data integration",,,,,"920035, EP/N021614/1; Engineering and Physical Sciences Research Council, EPSRC","This work was supported in part by the Centre for Digital Built Britain through the Government’s Modern Industrial Strategy by Innovate U.K., part of the U.K. Research and Innovation, and in part by the Engineering and Physical Sciences Research Council (EPSRC)/Innovate U.K. Centre for Smart Infrastructure and Construction under Grant EP/N021614/1 and Grant 920035.",,,,,,,,,,"Hoult, N., Bennett, P.J., Stoianov, I., Fidler, P., Maksimovi, M, Middleton, C., Graham, N., Soga, K., Wireless sensor networks: Creating `smart infrastructure (2009) Proc. 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Syst., 20 (2), pp. 1-16; Thacker, S., Adshead, D., Fay, M., Hallegatte, S., Harvey, M., Meller, H., O'Regan, N., Hall, J.W., Infrastructure for sustainable development (2019) Nature Sustainability, 2 (4), pp. 324-331. , Apr; Lee, E.A., Cyber-physical systems-Are computing foundations adequate (2006) Position Paper for NSF Workshop on Cyber-Physical Systems: Research Motivation, Techniques and Roadmap, 2, pp. 1-9; Klotzer, C., WeiBenborn, J., Paum, A., The evolution of cyberphysical systems as a driving force behind digital transformation (2017) Proc. IEEE 19th Conf. Bus. Informat. (CBI), pp. 5-14. , Jul; Gürdür, D., (2017) Making Interoperability Visible: A Novel Approach to Under-Stand Interoperability in Cyber-Physical Systems Toolchains., , Stockholm, Sweden: KTH Royal Institute of Technology; Gürdür, D., El-Khoury, J., Seceleanu, T., Lednicki, L., Making interoperability visible: Data visualization of cyber-physical systems development tool chains (2016) J. Ind. Inf. 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Appl., 35 (2), pp. 26-40; Qi, Q., Tao, F., Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison (2018) IEEE Access, 6, pp. 3585-3593; Derakhshannia, M., Gervet, C., Hajj-Hassan, H., Laurent, A., Martin, A., Data lake governance: Towards a systemic and natural ecosystem analogy (2020) Futur. Internet, 12 (8), pp. 1-16; Ahlgren, B., Hidell, M., Ngai, E.C.-H., Internet of Things for smart cities: Interoperability and open data (2016) IEEE Internet Comput., 20 (6), pp. 52-56. , Nov; Broring, A., Schmid, S., Schindhelm, C.-K., Khelil, A., Kabisch, S., Kramer, D., Le Phuoc, D., Teniente, E., Enabling IoT ecosystems through platform interoperability (2017) IEEE Softw., 34 (1), pp. 54-61. , Jan; Aloi, G., Caliciuri, G., Fortino, G., Gravina, R., Pace, P., Russo, W., Savaglio, C., Enabling IoT interoperability through opportunistic smartphone-based mobile gateways (2017) J. Netw. Comput. 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Syst., 48, pp. 25-33. , Jul; Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W., Digital twin in manufacturing: A categorical literature review and classification (2018) IFAC-PapersOnLine, 51 (11), pp. 1016-1022; Gürdür, D., Feljan, A.V., El-Khoury, J., Mohalik, S.K., Badrinath, R., Mujumdar, A.P., Fersman, E., Knowledge representation of cyberphysical systems for monitoring purpose (2018) Proc. 51th CIRP Int. Conf. Manuf. Syst. (CIRP CMS), pp. 468-473; Thabrew, L., Wiek, A., Ries, R., Environmental decision making in multi-stakeholder contexts: Applicability of life cycle thinking in development planning and implementation (2009) J. Cleaner Prod., 17 (1), pp. 67-76. , Jan; Waligo, V.M., Clarke, J., Hawkins, R., Implementing sustainable tourism: A multi-stakeholder involvement management framework (2013) Tourism Manage., 36, pp. 342-353. , Jun; Azadi, H., Ho, P., Hafni, E., Zarafshani, K., Witlox, F., Multistakeholder involvement and urban green space performance (2011) J. Env-Iron. Planning Manage., 54 (6), pp. 785-811. , Jul; Li, T.H.Y., Ng, S.T., Skitmore, M., Modeling multi-stakeholder multiobjective decisions during public participation in major infrastructure and construction projects: A decision rule approach (2016) J. Constr. Eng. Manag., 142 (3); Leifer, L., Meinel, C., Looking further: Design thinking beyond solution-_xation Design Thinking Research. 2019, pp. 1-12; Kolko, J., Design thinking comes of age (2015) Harv. Bus. Rev., 2015, pp. 66-71. , Sep; Stickdorn, M., Hormess, M.E., Lawrence, A., Schneider, J., (2018) This Is Service Design Doing: Applying Service Design Thinking in the Real World., , Newton, MA, USA: O'Reilly Media; Jittrapirom, P., Caiati, V., Feneri, A.-M., Ebrahimigharehbaghi, S., González, M.J.A., Narayan, J., Mobility as a service: A critical review of denitions, assessments of schemes, and key challenges (2017) Urban Plan., 2 (2), p. 13; Ceschin, F., Gaziulusoy, I., Evolution of design for sustainability: From product design to design for system innovations and transitions (2016) Design Stud., 47, pp. 118-163. , Nov; Rios, F.C., Grau, D., Circular economy in the built environment: Designing, deconstructing, and leasing reusable products (2020) Encycl. Renew. Sustain. Mater., 5, pp. 338-343. , Jan; Boder, A., Collective intelligence: A keystone in knowledge management (2006) J. Knowl. Manage., 10 (1), pp. 81-93. , Jan; Malone, T.W., Laubacher, R., Dellarocas, C.N., Harnessing crowds: Mapping the genome of collective intelligence (2009) SSRN Electron. J., 132 (2), pp. 327-335; Kusters, K., Buck, L., De Graaf, M., Minang, P., Van Oosten, C., Zagt, R., Participatory planning, monitoring and evaluation of multi-stakeholder platforms in integrated landscape initiatives (2018) Environ. Manage., 62 (1), pp. 170-181. , Jul","Broo, D.G.; Department of Engineering, United Kingdom; email: dg580@cam.ac.uk",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85100461393
"Jo S.-K., Park D.-H., Park H., Kwak Y., Kim S.-H.","8518110800;7403245696;57192084164;55122966500;54393433400;","Energy Planning of Pigsty Using Digital Twin",2019,"ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future",,,"8940032","723","725",,9,"10.1109/ICTC46691.2019.8940032","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078287207&doi=10.1109%2fICTC46691.2019.8940032&partnerID=40&md5=d72945f883c8d28fa90ba6175a4ce5ea","Electronics and Telecommunications Research Institute (ETRI), SDF Convergence Research Laboratory, South Korea; University of Seoul, Building Energy Environment Laboratory, South Korea","Jo, S.-K., Electronics and Telecommunications Research Institute (ETRI), SDF Convergence Research Laboratory, South Korea; Park, D.-H., Electronics and Telecommunications Research Institute (ETRI), SDF Convergence Research Laboratory, South Korea; Park, H., Electronics and Telecommunications Research Institute (ETRI), SDF Convergence Research Laboratory, South Korea; Kwak, Y., University of Seoul, Building Energy Environment Laboratory, South Korea; Kim, S.-H., Electronics and Telecommunications Research Institute (ETRI), SDF Convergence Research Laboratory, South Korea","Digital twin as a bridge between the physical and digital world is becoming more attractive with the realization of the industry 4.0. With the deployment of digital twin, pigsty has been replicated and simulated to find out more comfortable feeding environments in the digital world and applies outcomes to pigsty in the real world. In this paper, we propose a use case in the pigsty realized by digital twin and analyze energy related performance under a variety of virtual objects. The results provide the pigsty with criterion on installing new equipments without being installed. © 2019 IEEE.","Digital twin; energy planning; pigsty","Digital twin; Digital world; Energy planning; pigsty; Real-world; Virtual objects",,,,,"Institute for Information and Communications Technology Promotion, IITP; Ministry of Science and ICT, South Korea, MSIT: 2018-0-00387","ACKNOWLEDGMENT This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No. 2018-0-00387, Development of ICT based Intelligent Smart Welfare Housing System for the Prevention and Control of Livestock Disease)",,,,,,,,,,"(2019) World Population Prospectis 2019: Highlights, , tech. rep., United Nations Department of Economic and Social Affairs; Aker, J.C., Ghosh, I., Burrell, J., The promise (and pitfalls) of ict for agriculture initiatives (2016) Agricultural Economics, 47 (S1), pp. 35-48; Haag, S., Anderl, R., Digital twin-proof of concept (2018) Manufacturing Letters, 15, pp. 64-66; Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F., Digital twindriven product design, manufacturing and service with big data (2018) The International Journal of Advanced Manufacturing Technology, 94 (9-12), pp. 3563-3576; Jo, S.-K., Park, D.-H., Park, H., Kim, S.-H., Smart livestock farms using digital twin: Feasibility study (2018) 2018 International Conference on Information and Communication Technology Convergence (ICTC), IEEE, , oct; Predix Architecture, , https://www.predix.com/sites/default/files/ge-predix-architecture-r092615.pdf.Accessed:2018-07-24; GE Predix, , https://www.ge.com/digital/predix-platform-foundation-digital-industrial-applications, Accessed: 2018-07-24; Eclipse Ditto, , http://eclipse.org/ditto, Accessed: 2018-07-24; IBM Watson IoT, , https://developer.ibm.com/iotplatform/, Accessed: 2018-07-24; Crawley, D.B., Lawrie, L.K., Winkelmann, F.C., Buhl, W.F., Huang, Y.J., Pedersen, C.O., Strand, R.K., Witte, M.J., Energyplus: Creating a new-generation building energy simulation program (2001) Energy and Buildings, 33 (4), pp. 319-331; https://www.sketchup.com/ko, Google. Accessed: 2019-07-24; Ellis, P.G., Torcellini, P.A., Crawley, D.B., (2008) Energy Design Plugin: An Energyplus Plugin for Sketchup, , tech. rep., National Renewable Energy Lab. (NREL), Golden, CO (United States)",,,,"Institute of Electrical and Electronics Engineers Inc.","10th International Conference on Information and Communication Technology Convergence, ICTC 2019","16 October 2019 through 18 October 2019",,156267,,9781728108926,,,"English","ICTC - Int. Conf. ICT Converg.: ICT Converg. Lead. Auton. Future",Conference Paper,"Final","",Scopus,2-s2.0-85078287207
"Gao Y., Qian S., Li Z., Wang P., Wang F., He Q.","57282728800;57280644100;57218212553;56146668000;57211758869;36550317100;","Digital twin and its application in transportation infrastructure",2021,"Proceedings 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence, DTPI 2021",,,,"298","301",,8,"10.1109/DTPI52967.2021.9540108","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116131403&doi=10.1109%2fDTPI52967.2021.9540108&partnerID=40&md5=1ae4255cff2cf4eea9636af95531c67f","Southwest Jiaotong University, Key Laboratory of High-Speed Railway Engineering, Ministry of Education, Chengdu, China; School of Civil Engineering, Southwest Jiaotong University, Chengdu, China; Chinese Academy of Sciences, The State Key Laboratory of Management and Control for Complex Systems, Beijing, China; Institute of Automation, Chinese Academy of Sciences, Beijing, China","Gao, Y., Southwest Jiaotong University, Key Laboratory of High-Speed Railway Engineering, Ministry of Education, Chengdu, China, School of Civil Engineering, Southwest Jiaotong University, Chengdu, China; Qian, S., Southwest Jiaotong University, Key Laboratory of High-Speed Railway Engineering, Ministry of Education, Chengdu, China, School of Civil Engineering, Southwest Jiaotong University, Chengdu, China; Li, Z., Southwest Jiaotong University, Key Laboratory of High-Speed Railway Engineering, Ministry of Education, Chengdu, China, School of Civil Engineering, Southwest Jiaotong University, Chengdu, China; Wang, P., Southwest Jiaotong University, Key Laboratory of High-Speed Railway Engineering, Ministry of Education, Chengdu, China, School of Civil Engineering, Southwest Jiaotong University, Chengdu, China; Wang, F., Chinese Academy of Sciences, The State Key Laboratory of Management and Control for Complex Systems, Beijing, China, Institute of Automation, Chinese Academy of Sciences, Beijing, China; He, Q., Southwest Jiaotong University, Key Laboratory of High-Speed Railway Engineering, Ministry of Education, Chengdu, China, School of Civil Engineering, Southwest Jiaotong University, Chengdu, China","A prevailing challenge lies in how to properly design, construct and maintain transportation infrastructure engineering. Due to the development of sensor technology and computing technology, the amount of equipment monitoring data has multiplied and it causes difficulties in real-time state assessment and prediction under the traditional modeling condition. The introduction of Digital Twin technology, which aims to reflect the performance of the real-world product by simulating a virtual space, can solve these problems effectively. This paper reviews recent applications of four types of transportation infrastructure: railways, highways, bridges, and tunnels. Also, the existing research gaps are identified. © 2021 IEEE.","Data-driven; Digital twin; Physical entity; Simulation models; Transportation infrastructure","Computing technology; Data driven; Equipment monitoring; ITS applications; Physical entity; Real- time; Sensor computing; Sensor technologies; Simulation model; Transportation infrastructures; Bridges",,,,,"National Natural Science Foundation of China, NSFC: 51878576, U1934214; Department of Science and Technology of Sichuan Province, SPDST: 2020YFG0049","ACKNOWLEDGMENT This study was partially funded by The National Natural Science Foundation of China under award number U1934214, 51878576, and the Department of Science and Technology of Sichuan Province under award number 2020YFG0049.",,,,,,,,,,"Marai, O.E., Taleb, T., Song, J., Roads infrastructure digital twin: A step toward smarter cities realization (2021) IEEE Netw., 35 (2), pp. 136-143. , Mar; Tao, F., Zhang, H., Liu, A., Nee, A.Y.C., Digital twin in industry: State-of-The-art (2019) IEEE Trans. Ind. Inform., 15 (4), pp. 2405-2415. , Apr; Madni, A., Madni, C., Lucero, S., Leveraging digital twin technology in model-based systems engineering (2019) Systems, 7 (1), p. 7. , Jan; Schleich, B., Anwer, N., Mathieu, L., Wartzack, S., Shaping the digital twin for design and production engineering (2017) Cirp Ann., 66 (1), pp. 141-144; Kaewunruen, S., Lian, Q., Digital twin aided sustainability-based lifecycle management for railway turnout systems (2019) J. Clean. Prod., 228, pp. 1537-1551. , Aug; Guo, J., Wu, X., Liang, H., Hu, J., Liu, B., Digital-twin based power supply system modeling and analysis for urban rail transportation (2020) 2020 IEEE International Conference on Energy Internet (ICEI), pp. 74-79. , Sydney, NSW, Australia, Aug; Machl, T., Donaubauer, A., Kolbe, T.H., (2019) Planning Agricultural Core Road Networks Based on a Digital Twin of the Cultivated Landscape, , DE: Wichmann Verlag,. Accessed: May 08, 2021; Kim, J., Kim, S.-A., Lifespan prediction technique for digital twin-based noise barrier tunnels (2020) Sustainability, 12 (7), p. 2940. , Apr; Kaewunruen, S., Sresakoolchai, J., Ma, W., Phil-Ebosie, O., (2021) Digital Twin Aided Vulnerability Assessment and Risk-Based Maintenance Planning of Bridge Infrastructures Exposed to Extreme Conditions, p. 18; Dang, S., Kang, H., Lon, S., Shim, C., (2018) 3D Digital Twin Models for Bridge Maintenance; Dang, N., Shim, C., Nguyen, D., Bridge Assessment for Psc Girder Bridge Using Digital Twins Model, p. 7; Shim, C., Kang, H., Dang, N., (2019) Digital Twin Models for Maintenance of CABLE-SUPPORTED Bridges, p. 7; Sofia, H., Anas, E., Faiz, O., Mobile mapping, machine learning and digital twin for road infrastructure monitoring and maintenance: Case study of mohammed VI bridge in Morocco (2020) 2020 IEEE International Conference of Moroccan Geomatics (Morgeo), pp. 1-6. , Casablanca, Morocco, May; Ye, C., A digital twin of bridges for structural health monitoring (2019) 12th International Workshop on Structural Health Monitoring 2019, , Sep. 12; Febrianto, E., Butler, L., Girolami, M., Cirak, F., A self-sensing digital twin of a railway bridge using the statistical finite element method (2021) ArXiv210313729 Cs Math, , Mar., Accessed: Apr. 28, 2021; Kang, J.-S., Chung, K., Hong, E.J., Multimedia knowledge-based bridge health monitoring using digital twin Multimed. 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Constr., 123, p. 103547. , Mar; Panella, F., Loo, Y., Devriendt, M., Gonzalez, D., Kaushik, A., Boehm, J., Smart Image Based Technology and Deep Learning for Tunnel Inspection and Asset Management, p. 12; Kim, Y.-D., Son, G.-J., Kim, H., Song, C., Lee, J.-H., Smart disaster response in vehicular tunnels: Technologies for search and rescue applications (2018) Sustainability, 10 (7), p. 2509. , Jul; Ariyachandra, M.R.M.F., Brilakis, I., Digital twinning of railway overhead line equipment from airborne LiDAR Data (2020) 37th International Symposium on Automation and Robotics in Construction, , Kitakyushu, Japan, Oct; Kochan, A., Digital Twin Concept of the Etcs Application, p. 13; Zhao, L., Han, G., Li, Z., Shu, L., Intelligent digital twin-based software-defined vehicular networks (2020) IEEE Netw., 34 (5), pp. 178-184. , Sep; Kaliske, M., Behnke, R., Wollny, I., Vision on a digital twin of the road-tire-vehicle system for future mobility (2021) Tire Sci. Technol., 49 (1), pp. 2-18. , Jan; Wang, S., Zhang, F., Qin, T., Research on the construction of highway traffic digital twin system based on 3D GIS Technology (2021) J. Phys. Conf. Ser., 1802 (4), p. 042045. , Mar; Shim, C., Kang, H., Dang, N., Digital twin models for maintenance of cable-supported bridges (2019) International Conference on Smart Infrastructure and Construction 2019 (ICSIC), pp. 737-742. , Cambridge, UK, Jan; Lu, R., Brilakis, I., Digital twinning of existing reinforced concrete bridges from labelled point clusters (2019) Autom. Constr., 105, p. 102837. , Sep; Lau, D.-H.F., Butler, L.J., Adams, N.M., Elshafie, M.Z.E.B., Girolami, M.A., Real-time statistical modelling of data generated from self-sensing bridges (2018) Proc. Inst. Civ. Eng.-Smart Infrastruct. Constr., 171 (1), pp. 3-13. , Mar; Shabelnikov, A.N., Olgeyzer, I.A., Technology and mathematical basis of digital twin creation in railway infrastructure (2020) Proceedings of the Fourth International Scientific Conference, pp. 688-695. , Intelligent Information Technologies for Industry (IITI'19), vol1156","He, Q.; Southwest Jiaotong University, China; email: qhe@swjtu.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.","1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021","15 July 2021 through 15 August 2021",,171890,,9781665433372,,,"English","Proc. IEEE Int. Conf. Digit. Twins Parallel Intell., DTPI",Conference Paper,"Final","",Scopus,2-s2.0-85116131403
"Van Nimmen K., Van Hauwermeiren J., Van Den Broeck P.","55237620100;57214717938;6506964349;","Eeklo Footbridge: Benchmark Dataset on Pedestrian-Induced Vibrations",2021,"Journal of Bridge Engineering","26","7","05021007","","",,7,"10.1061/(ASCE)BE.1943-5592.0001707","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105199992&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001707&partnerID=40&md5=a4aa060a6367f0ac475c3c7add8ba51f","Dept. of Civil Engineering, Structural Mechanics, KU Leuven, Leuven, B-3001, Belgium","Van Nimmen, K., Dept. of Civil Engineering, Structural Mechanics, KU Leuven, Leuven, B-3001, Belgium; Van Hauwermeiren, J., Dept. of Civil Engineering, Structural Mechanics, KU Leuven, Leuven, B-3001, Belgium; Van Den Broeck, P., Dept. of Civil Engineering, Structural Mechanics, KU Leuven, Leuven, B-3001, Belgium","Vibration serviceability under crowd-induced loading has become a key design criterion for footbridges. Although increased research efforts are put into the characterization of crowd-induced loading, including related interaction phenomena, and first-generation design guides are available, a major challenge lies in the further development and validation of prediction models for crowd-induced vibrations. Full-scale benchmark datasets that simultaneously register structural and crowd motion make an invaluable contribution to meeting this need by providing detailed information on representative operational loading and response data. Currently available datasets either (1) involve a (too) small number of pedestrians or (2) do not involve the simultaneous registration of pedestrian and bridge motion, or else they involve a footbridge (3) where only a single mode or a very limited number of modes are sensitive to walking excitation, (4) for which no suitable digital twin is available, or (5) that is not open access. This paper therefore presents a new and publicly available full-scale dataset collected specifically for the further development and validation of models for crowd-induced loading. The dataset is collected for a real footbridge, with a number of modes that are sensitive to pedestrian-induced vibrations, and with a digital twin available. The pedestrian and bridge motions are registered simultaneously using wireless triaxial accelerometers and video cameras. In addition to two data blocks involving purely ambient excitation, four data blocks are collected for two pedestrian densities, 0.25 and 0.50 persons/m2, representing a total of more than 1 h of data for each pedestrian density. Analysis of the structural response shows that the different data blocks can be considered representative for the involved load case. The identified distribution of step frequencies in the crowd indicates a significant contribution of (near-)resonant loading for a number of modes of the footbridge. Furthermore, the dataset displays clear signs of human-structure interaction, suggesting a significant increase in effective modal damping ratios due to the presence of the crowd. © 2021 This work is made available under the terms of the Creative Commons Attribution 4.0 International license,.",,"Digital twin; Predictive analytics; Vibrations (mechanical); Video cameras; Human-structure interaction; Interaction phenomena; Modal damping ratios; Pedestrian density; Pedestrian-induced vibrations; Structural response; Triaxial accelerometer; Vibration serviceability; Footbridges",,,,,"Fonds Wetenschappelijk Onderzoek, FWO","The first author is a postdoctoral fellow of Research Foundation Flanders (FWO). The second author is a doctoral fellow of FWO. Financial support from FWO is gratefully acknowledged.",,,,,,,,,,"(2006) Sétra: Evaluation du Comportement Vibratoire des Passerelles Piétonnes Sous l'Action des Piétons (Assessment of Vibrational Behaviour of Footbridges under Pedestrian Loading), , AFGC (Association Française de Génie Civil). Sétra/AFGC; Agu, E., Kasperski, M., Influence of the random dynamic parameters of the human body on the dynamic characteristics of the coupled system structure-crowd (2011) J. Sound Vib., 330 (3), pp. 431-444. , https://doi.org/10.1016/j.jsv.2010.06.029; Bocian, M., Brownjoh, J., Racić, V., Hester, D., Quattrone, A., Gilbert, L., Beasley, R., Time-dependent spectral analysis of interactions within groups of walking pedestrians and vertical structural motion using wavelets (2018) Mech. Syst. Sig. 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Struct., 59, pp. 448-461. , https://doi.org/10.1016/j.engstruct.2013.11.006; Van Nimmen, K., Lombaert, G., De Roeck, G., Van Den Broeck, P., The impact of vertical human-structure interaction on the response of footbridges to pedestrian excitation (2017) J. Sound Vib., 402, pp. 104-121. , https://doi.org/10.1016/j.jsv.2017.05.017; Van Nimmen, K., Lombaert, G., Jonkers, I., De Roeck, G., Van Den Broeck, P., Characterisation of walking loads by 3D inertial motion tracking (2014) J. Sound Vib., 333, pp. 5212-5226. , https://doi.org/10.1016/j.jsv.2014.05.022; Van Nimmen, K., Van Den Broeck, P., Lombaert, G., Tubino, F., Pedestrian-induced vibrations of footbridges: An extended spectral approach (2020) J. Bridge Eng., 25 (8), p. 04020058. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0001582; Van Nimmen, K., Verbeke, P., Lombaert, G., De Roeck, G., Van Den Broeck, P., Numerical and experimental evaluation of the dynamic performance of a footbridge with tuned mass dampers (2016) J. Bridge Eng., 21 (8), p. 4016001. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000815; Van Nimmen, K., Zhao, G., Seyfarth, A., Van Den Broeck, P., A robust methodology for the reconstruction of the vertical pedestrian-induced load from the registered body motion (2018) Vibration, 2, pp. 250-268. , https://doi.org/10.3390/vibration1020018; Venuti, F., Bruno, L., An interpretative model of the pedestrian fundamental relation (2007) C.R. Mec., 335 (4), pp. 194-200. , https://doi.org/10.1016/j.crme.2007.03.008; Venuti, F., Racic, V., Corbetta, A., Modelling framework for dynamic interaction between multiple pedestrians and vertical vibrations of footbridges (2016) J. Sound Vib., 379, pp. 245-263. , https://doi.org/10.1016/j.jsv.2016.05.047; Wei, X., Van Den Broeck, P., De Roeck, G., Van Nimmen, K., A simplified method to account for the effect of human-human interaction on the pedestrian-induced vibrations of footbridges (2017) Proc. 10th Int. Conf. On Structural Dynamics, EURODYN 2017, pp. 2907-2912. , edited by F. Vestroni, F. Romeo, and V. Gattulli, Amsterdam: Elsevier Ltd; Weidmann, U., Transporttechnik der Fussgänger (Transport technology of the pedestrian) (1993) Schriftenr. IVT-Berichte, 90, p. 110. , Zürich, Switzerland: Institut für Verkehrsplanung, Transporttechnik, Strassen- und Eisenbahnbau (IVT), ETH Zürich; Zivanović, S., Benchmark footbridge for vibration serviceability assessment under vertical component of pedestrian load (2012) J. Struct. Eng., 138, pp. 1193-1202. , https://doi.org/10.1061/(ASCE)ST.1943-541X.0000571; Zivanović, S., Pavić, A., Ingólfsson, E., Modelling spatially unrestricted pedestrian traffic on footbridges (2010) J. Struct. Eng., 136 (10), pp. 1296-1308. , https://doi.org/10.1061/(ASCE)ST.1943-541X.0000226; Zivanović, S., Pavić, A., Reynolds, P., Vibration serviceability of footbridges under human-induced excitation: A literature review (2005) J. Sound Vib., 279 (12), pp. 1-74. , https://doi.org/10.1016/j.jsv.2004.01.019; Zivanović, S., Pavić, A., Reynolds, P., Modal testing and FE model tuning of a lively footbridge structure (2006) Eng. Struct., 28, pp. 857-868. , https://doi.org/10.1016/j.engstruct.2005.10.012","Van Nimmen, K.; Dept. of Civil Engineering, Belgium; email: katrien.vannimmen@kuleuven.be",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85105199992
"Liang C.J., McGee W., Menassa C.C., Kamat V.R.","55787565100;55346988900;15923411800;7004477339;","Bi-directional communication bridge for state synchronization between digital twin simulations and physical construction robots",2020,"Proceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020: From Demonstration to Practical Use - To New Stage of Construction Robot",,,,"1480","1487",,7,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109393552&partnerID=40&md5=d75e400442b6eb91a4602ebc787cfb3c","Department of Civil and Environmental Engineering, University of Michigan, United States; School of Architecture, University of Michigan, United States; Robotics Institute, University of Michigan, United States","Liang, C.J., Department of Civil and Environmental Engineering, University of Michigan, United States; McGee, W., School of Architecture, University of Michigan, United States, Robotics Institute, University of Michigan, United States; Menassa, C.C., Department of Civil and Environmental Engineering, University of Michigan, United States, Robotics Institute, University of Michigan, United States; Kamat, V.R., Department of Civil and Environmental Engineering, University of Michigan, United States, Robotics Institute, University of Michigan, United States","Collaborative robot (co-robots) are being increasingly deployed on construction sites to assist human workers with physically demanding work tasks. However, due to inherent safety and trust-related concerns, human-robot collaborative work is subject to strict safety standards that require robot motion and forces to be sensitive to proximate human workers. Robot simulations in online digital twins can be used to extend designed construction models, such as BIM, to the construction phase for real-time monitoring of robot motion planning and control. Robots plan work tasks and execute them in the digital twin simulations allowing humans to review and approve robot trajectories. Once approved, commands can be sent to the physical robots to perform the tasks. This paper discusses the development of a system to bridge robot simulations and physical robots in construction and digital fabrication. The Robot Operating System (ROS) is leveraged as the primary framework for bi-directional communication and Gazebo is used for robot simulations. The virtual robots in Gazebo receive work tasks from a BIM model to plan their trajectories, and then send the commands to the physical robots for execution. The system is implemented with a digital fabrication case study with a fullscale mobile KUKA KR120 six-degrees-of-freedom robotic arm mounted on a track system for an additional degree-offreedom, and evaluated by comparing the pose between the physical robot and the virtual robot. The results show a high accuracy of the pose synchronization between two robots, which provide the opportunity for further deploying to real construction sites. © 2020 Proceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020: From Demonstration to Practical Use - To New Stage of Construction Robot. All rights reserved.","Co-robots; Digital twin; Human-robot collaboration; Robot operating system","Agricultural robots; Architectural design; Bridges; Degrees of freedom (mechanics); Digital twin; Industrial robots; Robotics; Social robots; Bi-directional communication; Collaborative robots; Digital fabrication; Real time monitoring; Robot motion planning; Robot operating systems (ROS); Six degrees of freedom; State synchronization; Robot programming",,,,,,,,,,,,,,,,"Lundeen, Kurt M., Kamat, Vineet R., Menassa, Carol C., McGee, Wes, Autonomous motion planning and task execution in geometrically adaptive robotized construction work (2019) Automation in Construction, 100, pp. 24-45; (2018) Census of fatal occupational injuries (CFOI)-current and revised data, , https://www.bls.gov/iif/oshcfoi1.htm, BLS; Liang, Ci-Jyun, Kamat, Vineet, Menassa, Carol, Teaching robots to perform construction tasks via learning from demonstration (2019) Proceedings of the International Symposium on Automation and Robotics in Construction (ISARC), pp. 1305-1311. , Banff, Canada, May IAARC; Liang, Ci-Jyun, Kamat, Vineet R., Menassa, Carol C., Teaching robots to perform quasirepetitive construction tasks through human demonstration (2020) Automation in Construction, 120, p. 103370. , ISSN 0926-5805; Freedy, Amos, DeVisser, Ewart, Weltman, Gershon, Coeyman, Nicole, Measurement of trust in human-robot collaboration (2007) Proceedings of the International Symposium on Collaborative Technologies and Systems, pp. 106-114. , Orlando, FL, USA, May IEEE; You, Sangseok, Kim, Jeong-Hwan, Lee, SangHyun, Kamat, Vineet, Robert, Lionel P., Enhancing perceived safety in human-robot collaborative construction using immersive virtual environments (2018) Automation in Construction, 96, pp. 161-170; Salmi, Timo, Ahola, Jari M., Heikkilä, Tapio, Kilpelaïnen, Pekka, Malm, Timo, Human-robot collaboration and sensor-based robots in industrial applications and construction (2018) Robotic Building, pp. 25-52. , Henriette Bier, editor, pages Springer International Publishing, ISBN 978-3-319-70866-9; Morato, Carlos, Kaipa, Krishnanand N., Zhao, Boxuan, Gupta, Satyandra K., Toward safe human robot collaboration by using multiple kinects based real-time human tracking (2014) Journal of Computing and Information Science in Engineering, 14 (1), p. 011006; Aheleroff, Shohin, Polzer, Jan, Huang, Huiyue, Zhu, Zexuan, Tomzik, David, Lu, Yuqian, Lin, Yuan, Xu, Xun, Smart manufacturing based on digital twin technologies (2020) Industry 4.0: Challenges, Trends, and Solutions in Management and Engineering, p. 77. , Carolina Machado and J. Paulo Davim, editors, page CRC Press, ISBN 978-0-8153-5440-6; Madni, Azad M., Madni, Carla C., Lucero, Scott D., Leveraging digital twin technology in model-based systems engineering (2019) Systems, 7 (1), p. 7; Lu, Yuqian, Xu, Xun, Resource virtualization: A core technology for developing cyber-physical production systems (2018) Journal of Manufacturing Systems, 47, pp. 128-140; Delbrügger, Tim, Lenz, Lisa Theresa, Losch, Daniel, Roßmann, Jürgen, A navigation framework for digital twins of factories based on building information modeling (2017) Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1-4. , Limassol, Cyprus, September IEEE; Marshall, Matthew Q., Redovian, Cameron, An application of a digital twin to robotic system design for an unstructured environment (2019) Proceedings of the ASME International Mechanical Engineering Congress and Exposition, p. V02BT02A010. , page Salt Lake City, UT, USA, November ASME; (2018) Robot Operating System, , https://www.ros.org/, OpenRobotics; (2019) Open Robotics, , http://gazebosim.org/, Gazebo; Light, Roger A., Mosquitto: Server and client implementation of the MQTT protocol (2017) Journal of Open Source Software, 2 (13), p. 265; Kamat, Vineet R., Martinez, Julio C., Largescale dynamic terrain in three-dimensional construction process visualizations (2005) Journal of Computing in Civil Engineering, 19 (2), pp. 160-171; Eadie, Robert, Browne, Mike, Odeyinka, Henry, McKeown, Clare, McNiff, Sean, BIM implementation throughout theUKconstruction project lifecycle: An analysis (2013) Automation in Construction, 36, pp. 145-151; Sampaio, Alcinia Z., Berdeja, Edgar, Collaborative BIM environment as a support to conflict analysis in building design (2017) Proceedings of the Experiment@International Conference (exp.at'17), pp. 77-82. , Faro, Portugal, June IEEE; Wu, Tzong-Hann, Wu, Feng, Liang, Ci-Jyun, Li, Yi-Fen, Tseng, Ching-Mei, Kang, Shih-Chung, A virtual reality tool for training in global engineering collaboration (2017) Universal Access in the Information Society, pp. 1-13; Ochmann, Sebastian, Vock, Richard, Wessel, Raoul, Klein, Reinhard, Automatic reconstruction of parametric building models from indoor point clouds (2016) Computers & Graphics, 54, pp. 94-103; Hamledari, Hesam, McCabe, Brenda, Davari, Shakiba, Shahi, Arash, Automated schedule and progress updating of IFC-based 4D BIMs (2017) Journal of Computing in Civil Engineering, 31 (4), p. 04017012; Xiao, Yong, Taguchi, Yuichi, Kamat, Vineet R., Coupling point cloud completion and surface connectivity relation inference for 3D modeling of indoor building environments (2018) Journal of Computing in Civil Engineering, 32 (5), p. 04018033; Xu, Lichao, Feng, Chen, Kamat, Vineet R., Menassa, Carol C., An Occupancy Grid Mapping enhanced visual SLAM for real-time locating applications in indoor GPS-denied environments (2019) Automation in Construction, 104, pp. 230-245; Feng, Chen, Xiao, Yong, Willette, Aaron, McGee, Wes, Kamat, Vineet R., Vision guided autonomous robotic assembly and as-built scanning on unstructured construction sites (2015) Automation in Construction, 59, pp. 128-138; Bosché, Frédéric, Ahmed, Mahmoud, Turkan, Yelda, Haas, Carl T., Haas, Ralph, The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components (2015) Automation in Construction, 49, pp. 201-213; Dimitrov, Andrey, Golparvar-Fard, Mani, Segmentation of building point cloud models including detailed architectural/structural features and MEP systems (2015) Automation in Construction, 51, pp. 32-45; Stojanovic, Vladeta, Trapp, Matthias, Richter, Rico, Hagedorn, Benjamin, Döllner, Jürgen, Towards the generation of digital twins for facility management based on 3D point clouds (2018) Proceedings of the ARCOM 34th Annual Conference, pp. 270-279. , Belfast, UK, September; Wang, Chao, Cho, Yong K., Smart scanning and near real-time 3D surface modeling of dynamic construction equipment from a point cloud (2015) Automation in Construction, 49, pp. 239-249. , January; Lin, Jacob J., Lee, Jae Yong, Golparvar-Fard, Mani, Exploring the potential of image-based 3D geometry and appearance reasoning for automated construction progress monitoring (2019) Proceedings of the ASCE International Conference on Computing in Civil Engineering (i3CE), pp. 162-170. , Atlanta, GA, USA, June ASCE; Kamat, Vineet R., Martinez, Julio C., Dynamic 3d visualization of articulated construction equipment (2005) Journal of Computing in Civil Engineering, 19 (4), pp. 356-368; Yang, Cheng-Hsuan, Wu, Tzong-Hann, Xiao, Bo, Kang, Shih-Chung, Design of a robotic software package for modular home builder (2019) Proceedings of the International Symposium on Automation and Robotics in Construction (ISARC), pp. 1217-1222. , Banff, AB, Canada, May IAARC; Lundeen, Kurt M., Kamat, Vineet R., Menassa, Carol C., McGee, Wes, Scene understanding for adaptive manipulation in robotized construction work (2017) Automation in Construction, 82, pp. 16-30; Zhuang, Cunbo, Liu, Jianhua, Xiong, Hui, Digital twin-based smart production management and control framework for the complex product assembly shop-floor (2018) The International Journal of Advanced Manufacturing Technology, 96 (1), pp. 1149-1163; Bilberg, Arne, Malik, Ali Ahmad, Digital twin driven human-robot collaborative assembly (2019) CIRP Annals, 68 (1), pp. 499-502; Naboni, Roberto, Kunic, Anja, A computational framework for the design and robotic manufacturing of complex wood structures (2019) Proceedings of the Education and Research in Computer Aided Architectural Design in Europe and Iberoamerican Society of Digital Graphics, Joint Conference, 7, pp. 189-196. , pages Porto, Portugal, September; Cai, Yi, Wang, Yi, Burnett, Morice, Using augmented reality to build digital twin for reconfigurable additive manufacturing system (2020) Journal of Manufacturing Systems, , Press, May; (2020) ROS 3D Robot Visualizer, , https://github.com/ros-visualization/rviz, ros-visualization; Coleman, David T., Sucan, Ioan A., Chitta, Sachin, Correll, Nikolaus, Reducing the barrier to entry of complex robotic software: A MoveIt! case study (2014) Journal of Software Engineering for Robotics, 5 (1), pp. 3-16",,,"Advanced Construction Technology Center;Architectural Institute of Japan;Council for Construction Robot Research;et al.;Japan Robot Association;The International Association for Automation and Robotics in Construction (IAARC)","International Association on Automation and Robotics in Construction (IAARC)","37th International Symposium on Automation and Robotics in Construction: From Demonstration to Practical Use - To New Stage of Construction Robot, ISARC 2020","27 October 2020 through 28 October 2020",,169727,,9789529436347,,,"English","Proc. Int. Symp. Autom. Robot. Constr., ISARC: From Demonstr. Pract. Use - New Stage Constr. Robot",Conference Paper,"Final","",Scopus,2-s2.0-85109393552
"Szpytko J., Duarte Y.S.","6507258714;57192430925;","Integrated maintenance platform for critical cranes under operation: Database for maintenance purposes",2020,"IFAC-PapersOnLine","53","3",,"167","172",,7,"10.1016/j.ifacol.2020.11.027","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105550970&doi=10.1016%2fj.ifacol.2020.11.027&partnerID=40&md5=74681190fedf40972b3fd108d16cecd4","AGH University of Science and Technology, Ave A. Mickiewicza 30, Krakow, PL 30-059, Poland","Szpytko, J., AGH University of Science and Technology, Ave A. Mickiewicza 30, Krakow, PL 30-059, Poland; Duarte, Y.S., AGH University of Science and Technology, Ave A. Mickiewicza 30, Krakow, PL 30-059, Poland","The subject of the paper is the mathematical model to manage the logistics-maintenance process of overhead type cranes operated in critical systems. The study case of the paper is a hot rolling mills system of a steel plant with critical overhead cranes operating with hazard conditions and continuous operation. The model output is an optimal overhead cranes maintenance-task distribution minimizing the production line risk stopped and the model input is a digital database with historical worker-orders related with the operation, maintenance-logistics and management process of the hot rolling mills plant. The mathematical model behind the maintenance task making-decision process is a stochastic no-linear optimization model with bounded constraint that evaluates a risk global-system behaviour indicator based on Monte Carlo simulations. The definition and description volume of work in this investigation is extensive, reason why the current paper focusses the attention on model definition and dataflow concepts, specifically data collection, filtering, analysis, and storage from different sources, leaving open the application and numerical results for future works. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0)","Digital twins; Maintenance; Risk analysis; Stochastic optimization","Bridge cranes; Data flow analysis; Digital storage; Gantry cranes; Hot rolled steel; Hot rolling; Hot rolling mills; Human resource management; Linear programming; Maintenance; Monte Carlo methods; Steelmaking; Stochastic models; Stochastic systems; Continuous operation; Digital database; Integrated maintenance; Linear optimization model; Maintenance process; Maintenance tasks; Management process; Numerical results; Distributed database systems",,,,,"Ministerstwo Edukacji i Nauki, MNiSW","The work has been financially supported Ministry of Science and Higher Education.",,,,,,,,,,"Barari, A., Pop-Iliev, R., Reducing rigidity by implementing closed-loop engineering in adaptable design and manufacturing systems (2009) Journal of Manufacturing Systems, 28, pp. 47-54; Domazet, Z., Luka, F., Bugarin, M., Failure of two overhead crane shafts (2014) Engineering Failure Analysis, 44, pp. 125-135; Govindbhai, P.V., Ishwarbhai, D.P., Safety measurement of high-rise building (2013) Paripex-Indian Journal of Research, 3 (4); Lu, B., Fang, Y., Sun, N., Modeling and nonlinear coordination control for an underactuated dual overhead crane system (2018) Automatica, 91, pp. 244-255; Marquez, A., Venturino, P., Otegui, J., Common root causes in recent failures of cranes (2014) Engineering Failure Analysis, 39, pp. 55-64; Mori, Y., Tagawa, Y., Vibration controller for overhead cranes considering limited horizontal acceleration (2018) Control Engineering Practice, 81, pp. 256-263; Qian, D., Yi, J., (2016) Hierarchical Sliding Mode Control for Underactuated Cranes: Design, Analysis and Simulation, , Springer, Berlin; Rusinski, E., Failure analysis of an overhead traveling crane lifting system operating in a turbo generator hall (2013) Engineering Failure Analysis, 31, pp. 90-100; Szpytko, J., Automated overhead crane as the system based on human-machine interface (2000) IFAC Automated Systems Based on Human Skill, pp. 121-124. , Aachen, Germany; Tao, F., Zhang, M., Liu, Y., Nee, A.Y.C., Digital twin driven prognostics and health management for complex equipment (2018) CIRP Annals - Manufacturing Technology, 67, pp. 169-172",,"Parlikad A.Emmanouilidis C.Iung B.Macchi M.",,"Elsevier B.V.","4th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies, AMEST 2020","10 September 2020 through 11 September 2020",,168746,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85105550970
"Dan D., Ying Y., Ge L.","7004963131;57375558900;57209056473;","Digital Twin System of Bridges Group Based on Machine Vision Fusion Monitoring of Bridge Traffic Load",2022,"IEEE Transactions on Intelligent Transportation Systems","23","11",,"22190","22205",,6,"10.1109/TITS.2021.3130025","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121394671&doi=10.1109%2fTITS.2021.3130025&partnerID=40&md5=f664ff8e4903815c772725c427b790a8","Tongji University, Key Lab. of Performance Evolution and Control for Engineering Structures of Ministry of Education, The School of Civil Engineering, Shanghai, 200092, China; Tongji University, School of Civil Engineering, Shanghai, 200092, China","Dan, D., Tongji University, Key Lab. of Performance Evolution and Control for Engineering Structures of Ministry of Education, The School of Civil Engineering, Shanghai, 200092, China; Ying, Y., Tongji University, School of Civil Engineering, Shanghai, 200092, China; Ge, L., Tongji University, School of Civil Engineering, Shanghai, 200092, China","Bridges play an important role in transportation infrastructure systems. Intelligent and digital management of bridges group is an essential part of the future intelligent transportation infrastructure system. This paper proposes a digital twin system for bridges group in the regional transportation infrastructure network, which is interconnected by measured traffic loads. In physical space, a full-bridge traffic load monitoring system based on information fusion of weigh-in-motion (WIM) and multi-source heterogeneous machine vision is set up on the target bridge to measure traffic loads, also lightweight sensors are employed on the bridges group for structural response information. Furthermore, by establishing mechanical analysis models in the corresponding digital space and using the measured traffic loads as links, the working condition perception and safety warning of all bridges in the regional transportation network is achieved, forming an important support for further intelligent transportation infrastructure system. The proposed digital twin system has been preliminarily implemented in a bridges group around Shanghai, China, demonstrating the feasibility of the technical framework proposed in this paper and the bright prospects. © 2000-2011 IEEE.","AI-driven machine vision; bridge digital twin system; multi-source information fusion; structural health monitoring; traffic load monitoring","Computer vision; Information fusion; Weigh-in-motion (WIM); AI-driven machine vision; Bridge digital twin system; Load modeling; Load monitoring; Machine-vision; Multi-source information fusion; Structural health monitoring.; Traffic load monitoring; Traffic loads; Transportation infrastructures; Structural health monitoring",,,,,,,,,,,,,,,,"Lu, Y., Liu, C., Wang, K.I.-K., Huang, H., Xu, X., Digital twindriven smart manufacturing: Connotation, reference model, applications and research issues (2020) Robot. Comput.-Integr. Manuf., 61. , Feb; Lim, K.Y.H., Zheng, P., Chen, C.-H., A state-of-the-art survey of digital twin: Techniques, engineering product lifecycle management and business innovation perspectives (2019) J. Intell. Manuf., 31, pp. 1313-1337. , Nov; Grieves, M., (2015) Digital twin: Manufacturing excellence through virtual factory replication, pp. 1-7. , Florida Inst. Technol., Melbourne, FL, USA, White Paper 1; Grieves, M., (2016) Origins of the digital twin concept, , Florida Inst. Technol., Melbourne, FL, USA, White Paper 2; Grieves, M., Vickers, J., Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems (2017) Transdisciplinary Perspectives on Complex Systems, pp. 85-113. , F. J. Kahlen, S. Flumerfelt, and A. Alves, Eds. Berlin, Germany: Springer; Zhuang, C., Connotation, architecture and trends and trends of product digital twin (2017) (in Chinese), Comput. Integr. Manuf. Syst., 23 (4), pp. 753-768; Volk, R., Stengel, J., Schultmann, F., Building information modeling (BIM) for existing buildings-Literature review and future needs (2014) Autom. Construct., 38, pp. 109-127. , Mar; Valinejadshoubi, M., Bagchi, A., Moselhi, O., Development of a BIM-based data management system for structural health monitoring with application to modular buildings: Case study (2019) J. Comput. Civil Eng., 33 (3). , May; Vilutiene, T., Kalibatiene, D., Hosseini, M.R., Pellicer, E., Zavadskas, E.K., Building information modeling (BIM) for structural engineering: A bibliometric analysis of the literature (2019) Adv. Civil Eng., 2019, pp. 1-19. , Aug; Wan, C., Development of a bridge management system based on the building information modeling technology (2019) Sustainability, 11 (17), pp. 1-17; Webb, G.T., Vardanega, P.J., Middleton, C.R., Categories of SHM deployments: Technologies and capabilities (2015) J. Bridge Eng., 20 (11). , Nov; Delgado, J.M.D., Butler, L., Brilakis, I., Elshafie, M., Middleton, C., Structural performance monitoring using a dynamic data-driven BIM environment (2018) J. Comput. Civil Eng., 32 (3), pp. 1-25. , May; Bhuiyan, M.Z.A., Wu, J., Wang, G., Cao, J., Sensing and decision making in cyber-physical systems: The case of structural event monitoring (2016) IEEE Trans. Ind. Informat., 12 (6), pp. 2103-2114. , Dec; Yuan, X., Anumba, C.J., Parfitt, M.K., Cyber-physical systems for temporary structure monitoring (2016) Autom. Construct., 66, pp. 1-14. , Jun; Ozer, E., Feng, M.Q., Structural reliability estimation with participatory sensing and mobile cyber-physical structural health monitoring systems (2019) Appl. Sci., 9 (14), p. 2840. , Jul; Kang, J.-S., Chung, K., Hong, E.J., Multimedia knowledgebased bridge health monitoring using digital twin (2021) Multimedia Tools Appl., 80 (26-27), pp. 34609-34624. , Nov; Hodge, V.J., O'Keefe, S., Weeks, M., Moulds, A., Wireless sensor networks for condition monitoring in the railway industry: A survey (2015) IEEE Trans. Intell. Transp. Syst., 16 (3), pp. 1088-1106. , Jun; Khan, S.M., Atamturktur, S., Chowdhury, M., Rahman, M., Integration of structural health monitoring and intelligent transportation systems for bridge condition assessment: Current status and future direction (2016) IEEE Trans. Intell. Transp. Syst., 17 (8), pp. 2107-2122. , Aug; Marwala, T., (2010) Finite-Element-Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics, , London, U.K.: Springer; Garcia-Palencia, A.J., Santini-Bell, E., Sipple, J.D., Sanayei, M., Structural model updating of an in-service bridge using dynamic data (2015) Struct. Control Health Monitor., 22 (10), pp. 1265-1281. , Oct; Friswell, M.I., Damage identification using inverse methods (2007) Dynamic Methods for Damage Detection in Structures, , Vienna, Austria: Springer-Verlag Wien; Pecht, M.G., (2009) Encyclopedia of Structural Health Monitoring, , Hoboken, NJ, USA: Wiley; Hou, R., Jeong, S., Lynch, J.P., Law, K.H., Cyber-physical system architecture for automating the mapping of truck loads to bridge behavior using computer vision in connected highway corridors (2020) Transp. Res. C, Emerg. Technol., 111, pp. 547-571. , Feb; Dan, D., Ge, L., Yan, X., Identification of moving loads based on the information fusion of weigh-in-motion system and multiple camera machine vision (2019) Measurement, 144, pp. 155-166. , Oct; Ge, L., Dan, D., Li, H., An accurate and robust monitoring method of full-bridge traffic load distribution based on YOLO-v3 machine vision (2020) Struct. 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TJ20200826; Dan, D., Xu, Z., Zhang, K., Yan, X., Monitoring index of transverse collaborative working performance of assembled beam bridges based on transverse modal shape (2019) Int. J. Struct. Stability Dyn., 19 (8). , Aug","Dan, D.; Tongji University, China; email: dandanhui@tongji.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15249050,,,,"English","IEEE Trans. Intell. Transp. Syst.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85121394671
"Gürdür Broo D., Bravo-Haro M., Schooling J.","55648593700;57195576835;57189900369;","Design and implementation of a smart infrastructure digital twin",2022,"Automation in Construction","136",,"104171","","",,6,"10.1016/j.autcon.2022.104171","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124900033&doi=10.1016%2fj.autcon.2022.104171&partnerID=40&md5=61994e31f647aefd2ad08c06b4f29c2f","Center for Design Research, Mechanical Engineering, Stanford University, 424 Panama Mall, Stanford, CA  94305, United States; Laing O'Rourke Centre for Construction Engineering and Technology, Department of Engineering, University of Cambridge, UK, Cambridge, CB3 0FA, United Kingdom; Centre for Digital Built Britain, University of Cambridge, UK, Cambridge, CB3 0FA, United Kingdom","Gürdür Broo, D., Center for Design Research, Mechanical Engineering, Stanford University, 424 Panama Mall, Stanford, CA  94305, United States, Laing O'Rourke Centre for Construction Engineering and Technology, Department of Engineering, University of Cambridge, UK, Cambridge, CB3 0FA, United Kingdom, Centre for Digital Built Britain, University of Cambridge, UK, Cambridge, CB3 0FA, United Kingdom; Bravo-Haro, M., Laing O'Rourke Centre for Construction Engineering and Technology, Department of Engineering, University of Cambridge, UK, Cambridge, CB3 0FA, United Kingdom; Schooling, J., Laing O'Rourke Centre for Construction Engineering and Technology, Department of Engineering, University of Cambridge, UK, Cambridge, CB3 0FA, United Kingdom","There is a critical need to make infrastructure systems more efficient, resilient, and sustainable. Infrastructure systems provide the basis for everyday life and enable the flow of goods, information, and services within urban and regional settings. Providing data-centric solutions to improve this flow is essential. This can only be achieved if we manage to transform passive infrastructure assets into cyber-physical systems. Digital twins bring the opportunity to turn passive infrastructure assets into data-centric systems of systems. This article aims to provide a summary of existing digital twin architectures and exemplify a digital twin design and implementation. To this end, a literature review of digital twin architecture is presented in addition to a case study of a digital twin implementation in smart infrastructure. The case study focuses on a digital twin implementation of a bridge and describes in detail the physical, cyber, integration, and service layers of this implementation. Later in the article, we discuss the learnings from this case study under three main categories – systems perspective, information perspective, and organisational perspective. The findings show the importance of acquiring a systems perspective when designing digital twins today to enable interoperable systems of systems in the future. Furthermore, the findings highlight the vital necessity of data and information management while also considering the multidisciplinary aspects of digital twin design and implementation. © 2022","Data; Digital twins; Infrastructure; Resilience; Smart infrastructure","Embedded systems; Interoperability; Case-studies; Data centric; Design and implementations; Infrastructure; Infrastructure assets; Infrastructure systems; Literature reviews; Resilience; Smart infrastructures; Twin design; Information management",,,,,"UK Research and Innovation, UKRI; University of Cambridge; Horizon 2020: 882550","The authors are grateful for the feedback from Paul Fidler and the support from National Rail. Without their contribution this project wouldn't be possible. This research forms part of the Centre for Digital Built Britain's (CDBB) work at the University of Cambridge. It was enabled by the Construction Innovation Hub, of which CDBB is a core partner, and funded by UK Research and Innovation (UKRI) through the Industrial Strategy Challenge Fund (ISCF) . This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 882550 .","The authors are grateful for the feedback from Paul Fidler and the support from National Rail. Without their contribution this project wouldn't be possible. This research forms part of the Centre for Digital Built Britain's (CDBB) work at the University of Cambridge. It was enabled by the Construction Innovation Hub, of which CDBB is a core partner, and funded by UK Research and Innovation (UKRI) through the Industrial Strategy Challenge Fund (ISCF). This research has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No. 882550.",,,,,,,,,"Aheleroff, S., Xu, X., Zhong, R.Y., Lu, Y., Digital twin as a service (DTaaS) in industry 4.0: an architecture reference model (2021) Adv. Eng. 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CIRP, 84, pp. 545-549; Trauer, J., Schweigert-Recksiek, S., Okamoto, L.O., Spreitzer, K., Mörtl, M., Zimmermann, M., Data-driven engineering definitions and insights from an industrial case study for a new approach in technical product development (2020) Proceedings of the NordDesign 2020 Conference, NordDesign 2020, pp. 757-766; XMPRO, The Ultimate Guide to Digital Marketing (2019), Vol. 53 Issue 9; Zhang, H., Liu, Q., Chen, X., Zhang, D., Leng, J., A digital twin-based approach for designing and multi-objective optimization of hollow glass production line (2017) IEEE Access, 5, pp. 26901-26911","Gürdür Broo, D.; Center for Design Research, 424 Panama Mall, United States; email: didem@stanford.edu",,,"Elsevier B.V.",,,,,09265805,,AUCOE,,"English","Autom Constr",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85124900033
"Saif Y., Yusof Y., Latif K., Abdul Kadir A.Z., Ahmad M.I., Adam A., Hatem N.","57206241526;35101180500;55793681700;57305776500;57210176952;57205989395;57220331125;","Development of a smart system based on STEP-NC for machine vision inspection with IoT environmental",2022,"International Journal of Advanced Manufacturing Technology","118","11-12",,"4055","4072",,6,"10.1007/s00170-021-08095-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117718472&doi=10.1007%2fs00170-021-08095-y&partnerID=40&md5=09690e5649d4254676fda2c5160369cf","Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia; Faculty of Mechanical and Manufacturing Engineering Technology, Universiti Teknikal Malaysia Melaka (UTeM), Malacca, Malaysia; School of Mechanical Engineering, Faculty of Engineering, Universiti Tecknologi Malaysia (UTM), Johor, Malaysia","Saif, Y., Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia; Yusof, Y., Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia; Latif, K., Faculty of Mechanical and Manufacturing Engineering Technology, Universiti Teknikal Malaysia Melaka (UTeM), Malacca, Malaysia; Abdul Kadir, A.Z., School of Mechanical Engineering, Faculty of Engineering, Universiti Tecknologi Malaysia (UTM), Johor, Malaysia; Ahmad, M.I., Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia; Adam, A., Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia; Hatem, N., Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia","The advancement of technology and the accomplishments of Industry 4.0 have expanded the possibilities for research in the vision system of the industrial inspection sector, which are intelligent, widely connected, fully adaptable, autonomous, and fully accessible based on the IoT environment. This advancement precisely bridges the gap between machine vision and object dimension measurements. The main goal is to develop the system of the 3SMVI in the milling machine based on the camera system and lighting system to ensure the quality of the product. This study proposes the development of an automated smart system-based interpreter STEP-NC information for a machine vision inspection (3SMVI) for detecting and measuring the surface feature of Example 1 Part 21 of ISO 14649 standard. This presents a 3SMVI development architecture that will guide the advancement with 3SMVI of the current Intelitek proLIGHT CNC milling machine. A standardised architectural 3SMVI platform is created. On that basis, machine tools, physical processes, real database range, operating units, and intelligent computer technologies are interconnected through a broad range of networks, including Wi-Fi and wireless connections. Machine vision inspection system (MVIS) is suggested as the digital model of the cyber domain physical tool. The OpenCV library developed a system platform that becomes cloud connectivity between the Raspberry Pi 4 board and the USB microprocessor on camera range. As a result, the machine vision inspection system is operated based on an algorithm designed for automatic measuring on a standard model. The primary outcome is developing a system prototype capable of inspecting the surface feature of a workpiece in real-time operation. Nonetheless, connectivity between machines can be carried out through Cyber Digital Twins (CDT). The obtained MVIS and prototype structure is appropriate for online surface measurement, and the system implementation has been designed and realised for automated measurement. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.","Cyber Digital Twins; Inspection; IoT; Machine vision; Measurement; OpenCV; STEP-NC","Cameras; Computer operating systems; Computer vision; Industrial research; Inspection equipment; Internet of things; Machine components; Milling (machining); Milling machines; Surface measurement; Wi-Fi; Cybe digital twin; Industrial inspections; Machine vision inspection; Machine-vision; Opencv; Smart System; STEP-NC; Surface feature; Vision inspection systems; Vision systems; Inspection",,,,,,"This paper was partly sponsored by the research project “A Novel ISO 6983 Interpreter for Open Architecture CNC Systems” (Grant by PRGS code: G011) and the Ministry of Higher Education in Yemen.",,,,,,,,,,"Steger, C., Ulrich, M., Wiedemann, C., (2018) Machine vision algorithms and applications, , Wiley, Berlin; Iliyas Ahmad, M., Machine monitoring system: a decade in review (2020) Int J Adv Manuf Technol, 108, pp. 3645-3659; Qin, W., Chen, S., Peng, M., Recent advances in Industrial Internet: insights and challenges (2020) Digit Commun Netw, 6, pp. 1-13; Wang, J., Fu, P., Gao, R.X., Machine vision intelligence for product defect inspection based on deep learning and Hough transform (2019) J Manuf Syst, 51, pp. 52-60; Pan, Y., Taxonomies for reasoning about cyber-physical attacks in IoT-based manufacturing systems (2017) Int J Interact Multimed Artif Intell, 4, p. 45; Liu, C., Vengayil, H., Zhong, R.Y., Xu, X., A systematic development method for cyber-physical machine tools (2018) J Manuf Syst, 48, pp. 13-24; Rocha, M.S., On the performance of OPC UA and MQTT for data exchange between industrial plants and cloud servers (2019) Acta IMEKO, 8, pp. 80-87; Adam, A., Yusof, Y., Iliyas, M., Saif, Y., Hatem, N., Review on manufacturing for advancement of Industrial Revolution 4.0 (2019) Int J Integr Eng; Stojadinovic, S.M., Majstorovic, V.D., Durakbasa, N.M., Toward a cyber-physical manufacturing metrology model for industry (2020) Artif Intell Eng Des Anal Manuf AIEDAM; Dafflon, B., Moalla, N., Ouzrout, Y., The challenges, approaches, and used techniques of CPS for manufacturing in Industry: a literature review (2021) Int J Adv Manuf Technol; Saif, Y., Yusof, Y., Latif, K., Kadir, A.Z.A., Iliyas Ahmed, M., Systematic review of STEP-NC-based inspection (2020) Int J Adv Manuf Technol; Latif, K., Yusof, Y., Nassehi, A., Latif, A.I., Latif, Q.B.A.I., Development of a feature-based open soft-CNC system (2017) Int J Adv Manuf Technol, 89, pp. 1013-1024; Parakontan, T., Sawangsri, W., Development of the machine vision system for automated inspection of printed circuit board assembl. 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Kim, H., Kim, W., Automated inspection system for rolling stock brake shoes (2011) IEEE Trans Instrum Meas, 60, pp. 2835-2847; Li, Q., Ren, S., A real-time visual inspection system for discrete surface defects of rail heads (2012) IEEE Trans Instrum Meas, 61, pp. 2189-2199; Adamo, F., Attivissimo, F., Nisio, A.D., Calibration of an inspection system for online quality control of satin glass (2010) IEEE Trans Instrum Meas, 59, pp. 1035-1046; Koch, H., König, A., Weigl-seitz, A., Kleinmann, K., Suchý, J., Multisensor Contour following with vision, force, and acceleration sensors for an industrial robot (2013) IEEE Trans Instrum Meas, 62, pp. 268-280; Sharma, K.D., Chatterjee, A., Rakshit, A., A PSO – Lyapunov hybrid stable adaptive fuzzy tracking control approach for vision-based robot Navigation (2012) IEEE Trans Instrum Meas, 61, pp. 1908-1914; Yogesh, D., A. K., Ratan, R. & Rocha,, A., Computer vision based analysis and detection of defects in fruits causes due to nutrients deficiency (2020) Clust Comput, 23, pp. 1817-1826; Ridwan, F., Xu, X., Liu, G., A framework for machining optimisation based on STEP-NC (2012) J Intell Manuf, 23, pp. 423-441; Guo, L., Wang, S., Kang, L., Cao, Y., Agent-based manufacturing service discovery method for cloud manufacturing (2015) Int J Adv Manuf Technol, 81, pp. 2167-2181; International vocabulary of metrology—basic and general concepts and associated terms (VIM) (2012) , J., 58, pp. 3493-3495; Evaluation of measurement data—guide to the expression of uncertainty in measurement (2008) Int Organ Stand Geneva ISBN, 50, p. 134","Saif, Y.; Faculty of Mechanical and Manufacturing Engineering, Malaysia",,,"Springer Science and Business Media Deutschland GmbH",,,,,02683768,,IJATE,,"English","Int J Adv Manuf Technol",Article,"Final","",Scopus,2-s2.0-85117718472
"Hämäläinen M.","57188818067;","Urban development with dynamic digital twins in Helsinki city",2021,"IET Smart Cities","3","4",,"201","210",,6,"10.1049/smc2.12015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118151915&doi=10.1049%2fsmc2.12015&partnerID=40&md5=3c7994a41438eb7fa20ec06948500b29","School of Marketing and Communication, University of Vaasa, Vaasa, Finland","Hämäläinen, M., School of Marketing and Communication, University of Vaasa, Vaasa, Finland","A dynamic digital twin is a feasible solution that can be employed to build real-time connectivity between virtual and physical objects. Industries like manufacturing, aerospace and healthcare utilise dynamic digital twins for simulation, monitoring and control purposes, but recently, this nascent technology has also attracted the interest of urban designers. Due to the novelty of the dynamic digital twin in urban design, this research study addresses the concept of digital twin technology and investigates its applicability in so-called smart city settings. Drawing on results from research interviews and examples from the Digital Twin project in Helsinki city, the research illustrates that solid data infrastructure forms the foundation for urban digital twins and the development of future smart city applications and services. Furthermore, data-enriched digital twins evidently accelerate smart city experimentations and strengthen both learning and knowledge-based decision-making. Digital twins have also proved that they offer an environment in which smart city practitioners can bridge multi-stakeholder urban design teams through one digital platform. © 2021 The Authors. IET Smart Cities published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.","Digital twin; smart city; smart city experimentation; stakeholders","Bridges; Decision making; Knowledge based systems; Urban growth; Feasible solution; Helsinki; Physical objects; Real- time; Smart city experimentation; Stakeholder; Time connectivity; Urban design; Urban development; Virtual objects; Smart city",,,,,,"There are no funders.",,,,,,,,,,"Carvalho, L., Smart cities from scratch? A socio-technical perspective (2014) Camb. J. Reg Econ. Soc, 8 (1), pp. 43-60; Gabrys, J., Programming environments: environmentality and citizen sensing in the smart city (2014) Environ. Plann. Soc. 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Inform, 15 (4), pp. 2405-2415; Dembski, F., Wössner, U., Letzgus, M., Ruddat, M., Yamu, C., Urban digital twins for smart cities and citizens: the case study of Herrenberg, Germany (2020) Sustainability, 12 (6), p. 2307; Dignan, J., Smart cities in the time of climate change and Covid-19 need digital twins (2020) IET Smart Cities, 2 (3), pp. 109-110; Hämäläinen, M., A framework for a smart city design: digital transformation in the Helsinki smart city (2020) Entrepreneurship and the Community, pp. 63-86. , Springer, Cham; Ruohomäki, T., Airaksinen, E., Huuska, P., Kesäniemi, O., Martikka, M., Suomisto, J., Smart city platform enabling digital twin (2018) 2018 International Conference on Intelligent Systems (IS), pp. 155-161. , IEEE; Finger, M., Portmann, E., What are cognitive cities? (2016) Towards Cognitive Cities, pp. 1-11. , Springer, Cham; Baccarne, B., Schuurman, D., Mechant, P., De Marez, L., The role of urban living labs in a smart city (2014) XXV ISPIM Innovation Conference; Borkowska, K., Osborne, M., Locating the fourth helix: rethinking the role of civil society in developing smart learning cities (2018) Int. Rev. Educ, 64 (3), pp. 355-372; Shetty, V., A tale of smart cities (1997) Commun. Int, 24 (8), pp. 16-18; Tokmakoff, A., Billington, J., Consumer services in smart city Adelaide (1994) Paper published at HOIT 94. Proceedings of an International Cross-disciplinary Conference on Home-Oriented Informatics, Telematics & Automation, , University of Copenhagen; (2020) Worldwide Smart Cities Spending Guide, 20, p. 11. , https://www.idc.com/getdoc.jsp?containerId=IDC_P37477, Ref; (2020) Research and Markets, , https://www.researchandmarkets.com/reports/5146372/global-smart-cities-market-by-smart?utm_source=CI&utm_medium=PressRelease&utm_code=bf92ng&utm_campaign=1447907+-+Global+Smart+Cities+Market+to+2025%3a+Increasing+Number+of+Government+Initiatives+and+PPP+Models+for+Smart+Cities&utm_exec=chdo54prd, Ref. 18.11; Spending on smart city projects from 2019 to 2025 (2020) (2020) Ref, 18, p. 11. , https://www.statista.com/statistics/1111626/worldwide-smart-city-market-revenue/; Söderström, O., Paasche, T., Klauser, F., Smart cities as corporate storytelling (2014) City, 18 (3), pp. 307-320; Vilajosana, I., Llosa, J., Martinez, B., Domingo-Prieto, M., Angles, A., Vilajosana, X., Bootstrapping smart cities through a self-sustainable model based on big data flows (2013) IEEE Commun. Mag, 51 (6), pp. 128-134; Albino, V., Berardi, U., Dangelico, R.M., Smart cities: definitions, dimensions, performance, and initiatives (2015) J. Urban Technol, 22 (1), pp. 3-21; Hämäläinen, M., Tyrväinen, P., Improving smart city design: a conceptual model for governing complex smart city ecosystems (2018) Bled eConference, , University of Maribor Press; Hamalainen, M., Tyrvainen, P., A framework for iot service experiment platforms in smart-city environments (2016) 2016 IEEE International Smart Cities Conference (ISC2), pp. 1-8. , IEEE; Ibba, S., Pinna, A., Seu, M., Pani, F.E., CitySense: blockchain-oriented smart cities (2017) Proceedings of the XP2017 Scientific Workshops, pp. 1-5. , May); Sanchez, L., Muñoz, L., Galache, J.A., Sotres, P., Santana, J.R., Gutierrez, V., Ramdhany, R., Pfisterer, D., SmartSantander: IoT experimentation over a smart city testbed (2014) Comput. 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Comput, 10 (3), pp. 1141-1153; Grieves, M., Vickers, J., Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems (2017) Transdisciplinary Perspectives on Complex Systems, pp. 85-113. , Springer, Cham; Negri, E., Fumagalli, L., Macchi, M., A review of the roles of digital twin in cps-based production systems (2017) Procedia Manuf, 11, pp. 939-948; Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W., Digital twin in manufacturing: a categorical literature review and classification (2018) IFAC-PapersOnLine, 51 (11), pp. 1016-1022; Redelinghuys, A.J.H., Kruger, K., Basson, A., A six-layer architecture for digital twins with aggregation (2019) International Workshop on Service Orientation in Holonic and Multi-Agent Manufacturing, pp. 171-182. , Springer, Cham; Vachálek, J., Bartalský, L., Rovný, O., Šišmišová, D., Morháč, M., Lokšík, M., The digital twin of an industrial production line within the industry 4.0 concept (2017) 2017 21st International Conference on Process Control (PC), pp. 258-262. , IEEE; Ding, K., Chan, F.T., Zhang, X., Zhou, G., Zhang, F., Defining a digital twin-based cyber-physical production system for autonomous manufacturing in smart shop floors (2019) Int. J. Prod. Res, 57 (20), pp. 6315-6334; Khajavi, S.H., Motlagh, N.H., Jaribion, A., Werner, L.C., Holmström, J., (2019) Digital twin: vision, benefits, boundaries, and creation for buildings, 7, pp. 147406-147419. , https://doi.org/10.1109/ACCESS.2019.2946515, Abbott, D., (ed.), IEEE Access; Alaei, N., Rouvinen, A., Mikkola, A., Nikkilä, R., Product processes based on digital twin (2018) Commercial Vehicle Technology 2018, pp. 187-194. , Springer Vieweg, Wiesbaden; Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F., a. Digital twin-driven product design, manufacturing and service with big data (2018) Int. J. Adv. Manuf. Technol, 94 (9-12), pp. 3563-3576; Creswell, J.W., Poth, C.N., (2016) Qualitative inquiry and research design: Choosing among five approaches, , Sage publications; (2021), https://buildingsmart-1xbd3ajdayi.netdna-ssl.com/wp-content/uploads/2019/05/buildingSMART_A5-FLYER_V5-1.pdf; (2021), https://www.ogc.org/standards/citygml, Open Geospatial Consortium.,).Retrieved June, 14; Erving, A., (2008), Paikkatiedoista kaupunkimalleihin CityGML selvitystyö. Teknillinen korkeakoulu, fotogrammetrian ja kaukokartoituksen laboratorio. Espoo; Kira-digi: Kalasataman digitaaliset kaksoset. Kira-digi-kokeiluhankkeen loppuraportti. 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Behav, 27 (6), pp. 989-993; Moyser, R., Uffer, S., From smart to cognitive: a roadmap for the adoption of technology in cities (2016) Towards Cognitive Cities, pp. 13-35. , Springer, Cham; Chanias, S., Hess, T., Understanding digital transformation strategy formation: insights from Europe's Automotive Industry (2016) PACIS, p. 296; Singh, A., Hess, T., How chief digital officers promote the digital transformation of their companies (2017) MIS Q. Exec, 16 (1), pp. 1-17; Bostrom, R., Heinen, J., MIS problems and failures: a socio-technical perspective part II: the application of socio-technical theory (1977) MIS Q, 1, pp. 11-28","Hämäläinen, M.; School of Marketing and Communication, Finland; email: mervi.hamalainen@uwasa.fi",,,"John Wiley and Sons Inc",,,,,26317680,,,,"English","IET Smart Cities",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85118151915
"Liu X., Jiang Y., Wang Z., Zhong R.Y., Cheung H.H., Huang G.Q.","57204569405;57298534300;57297521200;55353690000;57349647200;7403425048;","imseStudio: blockchain-enabled secure digital twin platform for service manufacturing",2021,"International Journal of Production Research",,,,"","",,6,"10.1080/00207543.2021.2003462","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119593603&doi=10.1080%2f00207543.2021.2003462&partnerID=40&md5=73bf1766922e24257e0ad27fde6ac65d","Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong; Department of Computer Science, The University of Hong Kong, Hong Kong","Liu, X., Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong; Jiang, Y., Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong; Wang, Z., Department of Computer Science, The University of Hong Kong, Hong Kong; Zhong, R.Y., Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong; Cheung, H.H., Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong; Huang, G.Q., Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong","The manufacturing industry is experiencing a service-oriented transformation in the digitalisation era. However, many small and middle enterprises (SMEs) still rely on traditional manufacturing patterns in which they can hardly servitise manufacturing resources due to the limited budget and poor digitalisation capability. To servitise manufacturing resources, this paper proposes unified five-layer blockchain-enabled secure digital twin platform architecture, followed by its core enabling components and technologies. Firstly, a service-oriented digital twinning model is developed to transform physical resources into digital services. Secondly, a rule-based off-chain matching mechanism is designed to bridge customers’ orders with manufacturing services. Thirdly, service-oriented architecture (SOA) is adopted as the major methodology to design and develop the whole blockchain platform. Four blockchain frontend services are developed using React.js, whilst the blockchain backend is developed using private Ethereum blockchain and InterPlanetary File System (IPFS). Finally, an experimental case is conducted based on the 3D printing scenario to verify the effectiveness and efficiency of the proposed platform, named imseStudio. The results show that it not only provides an effective solution to digitalise manufacturing resources but also promotes the transformation towards service manufacturing. Highlights: Blockchain-enabled secure digital twin platform is developed to servitise manufacturing resources Service-oriented digital twinning model is developed to transform physical resources into digital services Rule-based off-chain matching mechanism is built to bridge customers’ orders with 3D printer Four blockchain explorers are developed to facilitate 3D printing services. © 2021 Informa UK Limited, trading as Taylor & Francis Group.","3D printing service; Blockchain; digital twin; service manufacturing","Blockchain; Bridges; Budget control; Information services; Service oriented architecture (SOA); 3-D printing; 3d printing service; 3D-printing; Block-chain; Manufacturing resource; Physical resources; Printing services; Secure digital; Service manufacturing; Service Oriented; 3D printers",,,,,"2019BT02S593, PRP/068/20LI; Research Grants Council, University Grants Committee, 研究資助局: R7027-18, S2019.A8.013.19S; University of Hong Kong, HKU: 201906159001; National Key Research and Development Program of China, NKRDPC: 2019YFB1705401","The authors would like to acknowledge the partial supports from National Key R&D Program of China No. 2019YFB1705401, Research Grants Council Research Impact Fund No. R7027-18, and Strategic Public Policy Research Funding Scheme No. S2019.A8.013.19S, Seed Fund for Basic Research in HKU under Grant 201906159001, Guangdong Special Support Talent Program - Innovation and Entrepreneurship Leading Team under Grant 2019BT02S593 and ITF project (PRP/068/20LI). Authors are grateful to industrial collaborators for providing field studies and technical supports.",,,,,,,,,,"Aghamohammadzadeh, E., Valilai, O.F., A Novel Cloud Manufacturing Service Composition Platform Enabled by Blockchain Technology (2020) International Journal of Production Research, pp. 1-19; Aheleroff, S., Xu, X., Zhong, R.Y., Lu, Y., Digital Twin as a Service (DTaaS) in Industry 4.0: An Architecture Reference Model (2021) Advanced Engineering Informatics, 47; Albaz, A., Dondi, M., Rida, T., Schubert, J., (2020) Unlocking Growth in Small and Medium-Size enterprises, , https://www.mckinsey.com/industries/public-and-social-sector/our-insights/unlocking-growth-in-small-and-medium-size-enterprises, McKinsey & Company, and,. ; Annarelli, A., Battistella, C., Nonino, F., Product Service System: A Conceptual Framework from a Systematic Review (2016) Journal of Cleaner Production, 139, pp. 1011-1032; Cavalieri, S., Ouertani, Z.M., Zhibin, J., Rondini, A., Service Transformation in Industrial Companies (2017) International Journal of Production Research, 56 (6), pp. 2099-2102; David, E.B., Caren, S., Benjamin, S., A Framework for Analyzing Customer Service Orientations in Manufacturing (1989) The Academy of Management Review, 14 (1), pp. 75-95; Ding, K., Chan, F.T.S., Zhang, X., Zhou, G., Zhang, F., Defining a Digital Twin-Based Cyber-Physical Production System for Autonomous Manufacturing in Smart Shop Floors (2019) International Journal of Production Research, 57 (20), pp. 6315-6334; Fry, T.D., Steele, D.C., Saladin, B.A., A Service-Oriented Manufacturing Strategy (1993) International Journal of Operations & Production Management, 14 (10), pp. 17-29; Fu, S., Han, Z., Huo, B., Relational Enablers of Information Sharing: Evidence from Chinese Food Supply Chains (2017) Industrial Management and Data Systems, 117 (5), pp. 838-852; Gao, J., Yao, Y., Zhu, V.C.Y., Sun, L., Lin, L., Service-oriented Manufacturing: A New Product Pattern and Manufacturing Paradigm (2009) Journal of Intelligent Manufacturing, 22 (3), pp. 435-446; Gebauer, H., Ren, G.-J., Valtakoski, A., Reynoso, J., Service-Driven Manufacturing (2012) Journal of Service Management, 23 (1), pp. 120-136; 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Zheng, P., Wang, Z., Chen, C.-H., Khoo, L.P., A Survey of Smart Product-Service Systems: Key Aspects, Challenges and Future Perspectives (2019) Advanced Engineering Informatics, 42; Zhou, L., Zhang, L., Laili, Y., Zhao, C., Xiao, Y., Multi-task Scheduling of Distributed 3D Printing Services in Cloud Manufacturing (2018) The International Journal of Advanced Manufacturing Technology, 96 (9-12), pp. 3003-3017","Huang, G.Q.; Department of Industrial and Manufacturing Systems Engineering, Hong Kong; email: gqhuang@hku.hk",,,"Taylor and Francis Ltd.",,,,,00207543,,IJPRB,,"English","Int J Prod Res",Article,"Article in Press","",Scopus,2-s2.0-85119593603
"Ramancha M.K., Astroza R., Conte J.P., Restrepo J.I., Todd M.D.","56926718100;55619989200;7101953827;7005927192;7202805915;","Bayesian Nonlinear Finite Element Model Updating of a Full-Scale Bridge-Column Using Sequential Monte Carlo",2020,"Conference Proceedings of the Society for Experimental Mechanics Series",,,,"389","397",,6,"10.1007/978-3-030-47638-0_43","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120419366&doi=10.1007%2f978-3-030-47638-0_43&partnerID=40&md5=dcf574fd29d39ad39e5436879c08b9b8","Department of Structural Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States; Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago, Chile","Ramancha, M.K., Department of Structural Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States; Astroza, R., Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Santiago, Chile; Conte, J.P., Department of Structural Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States; Restrepo, J.I., Department of Structural Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States; Todd, M.D., Department of Structural Engineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA, United States","Digital twin-based approaches for structural health monitoring (SHM) and damage prognosis (DP) are emerging as a powerful framework for intelligent maintenance of civil structures and infrastructure systems. Model updating of nonlinear mechanics-based Finite Element (FE) models using input and output measurement data with advanced Bayesian inference methods is an effective way of constructing a digital twin. In this regard, the nonlinear FE model updating of a full-scale reinforced-concrete bridge column subjected to seismic excitations applied by a large shake table is considered in this paper. This bridge column, designed according to US seismic design provisions, was tested on the NEES@UCSD Large High-Performance Outdoor Shake Table (LHPOST). The column was subjected to a sequence of ten recorded earthquake ground motions and was densely instrumented with an array of 278 sensors consisting of strain gauges, linear and string potentiometers, accelerometers and Global Positioning System (GPS) based displacement sensors to measure local and global responses during testing. This heterogeneous dataset is used to estimate/update the material and damping parameters of the developed mechanics-based distributed plasticity FE model of the bridge column. The sequential Monte Carlo (SMC) method (set of advanced simulation-based Bayesian inference methods) is used herein for the model updating process. The inherent architecture of SMC methods allows for parallel model evaluations, which is ideal for updating computationally expensive models. © 2020, The Society for Experimental Mechanics, Inc.","Bayesian inference; Digital twin; Earthquake; Finite element; Full-scale structural systems; Model updating; Sequential Monte Carlo; Structural health monitoring","Bayesian networks; Earthquakes; Finite element method; Global positioning system; Inference engines; Reinforced concrete; Seismic design; Structural health monitoring; Voltage dividers; Bayesian inference; Bridge columns; Earthquake; Finite element modelling (FEM); Finite-element model updating; Full-scale structural system; Model updating; Non-linear finite element modeling; Sequential Monte Carlo; Structural systems; Monte Carlo methods",,,,,"Engineer Research and Development Center, ERDC: W912HZ-17-2-0024; U.S. Army Corps of Engineers, USACE","Acknowledgements Funding for this work was provided by the U.S. Army Corps of Engineers through the U.S. Army Engineer Research and Development Center Research Cooperative Agreement W912HZ-17-2-0024.",,,,,,,,,,"Kadry, S., Diagnostics and prognostics of engineering systems: Methods and techniques (2012) Diagnostics Prognostics of Engineering Systems: Methods and Techniques, pp. 1-433. , pp., IGI Global, Hershey; Farrar, C.R., Worden, K., An introduction to structural health monitoring (2007) Math Phys Eng Sci, 365, pp. 303-315; Farrar, C.R., Worden, K., (2012) Structural Health Monitoring: A Machine Learning Perspective, , Wiley, Chichester; Astroza, R., Ebrahimian, H., Conte, J.P., Material parameter identification in distributed plasticity FE models of frame-type structures using nonlinear stochastic filtering (2015) J. Eng. Mech. ASCE., 141 (5), pp. 1-17; Astroza, R., Alessandri, A., Conte, J.P., A dual adaptive filtering approach for nonlinear finite element model updating accounting for modeling uncertainty (2019) Mech. Syst. Signal Process., 115, pp. 782-800; Ramancha, M.K., Madarshahian, R., Astroza, R., Conte, J.P., Non-unique estimates in material parameter identification of nonlinear FE models governed by multiaxial material models using unscented kalman filtering (2020) Conference Proceedings of Society of Experimental Mechanics Series, pp. 257-265. , pp., Springer International Publishing, Cham; Ebrahimian, H., Astroza, R., Conte, J.P., de Callafon, R.A., Nonlinear finite element model updating for damage identification of civil structures using batch Bayesian estimation (2017) Mech. Syst. Signal Process., 84, pp. 194-222; Schoettler, D.C., Restrepo, J.I., Guerrini, G., A full-scale, single-column bridge bent tested by shake-table excitation (2015) PEER Rep, 11 (3), pp. 555-565; Minson, S.E., Simons, M., Beck, J.L., Bayesian inversion for finite fault earthquake source models I-theory and algorithm (2013) Geophys. J. Int., 194 (3), pp. 1701-1726; Ching, J., Chen, Y.C., Transitional Markov chain Monte Carlo method for Bayesian model updating, model class selection, and model averaging (2007) J. Eng. Mech., 133 (7), pp. 816-832. , July; Chatfield, C., Model uncertainty, data mining and statistical inference (1995) J. R. Stat. Soc. Ser. A., 158, pp. 419-444; Kemp, F., An introduction to sequential Monte Carlo methods (2003) J R Stat Soc Ser D, 52 (4), pp. 694-695. , December; Taucer, F.F., Spacone, E., Filippou, F.C., A Fiber Beam-Column Element for Seismic Response Analysis of Reinforced Concrete Structures Rep 91/17, , Berkeley, CA; Popovics, S., A numerical approach to the complete stress-strain curve of concrete (1973) Cem. Concr. Res., 3 (5), pp. 583-599; Brown, J., Kunnath, S.K., (2000) Low-Cycle Fatigue Behavior of Longitudinal Reinforcement in Reinforced Concrete Bridge Columns. Multidisci-Plinary, , Buffalo","Ramancha, M.K.; Department of Structural Engineering, United States; email: mramanch@eng.ucsd.edu","Mao Z.",,"Springer","38th IMAC, A Conference and Exposition on Structural Dynamics, 2020","10 February 2020 through 13 February 2020",,245349,21915644,9783030487782,,,"English","Conf. Proc. Soc. Exp. Mech. Ser.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85120419366
"Sánchez-Rodríguez A., Esser S., Abualdenien J., Borrmann A., Riveiro B.","57200043734;57209973260;57208143811;14824718700;35096575300;","From point cloud to IFC: A masonry arch bridge case study",2020,"EG-ICE 2020 Workshop on Intelligent Computing in Engineering, Proceedings",,,,"422","431",,6,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091042409&partnerID=40&md5=77a5d4efc41ca2e31c57e0a9641f5970","Department of Materials Engineering, Applied Mechanics and Construction, School of Industrial Engineering, University of Vigo, Spain; Chair of Computational Modelling and Simulation, Technical University of Munich, Germany","Sánchez-Rodríguez, A., Department of Materials Engineering, Applied Mechanics and Construction, School of Industrial Engineering, University of Vigo, Spain; Esser, S., Chair of Computational Modelling and Simulation, Technical University of Munich, Germany; Abualdenien, J., Chair of Computational Modelling and Simulation, Technical University of Munich, Germany; Borrmann, A., Chair of Computational Modelling and Simulation, Technical University of Munich, Germany; Riveiro, B., Department of Materials Engineering, Applied Mechanics and Construction, School of Industrial Engineering, University of Vigo, Spain","For the last several years, laser scanning has become one of the reference technologies when talking about the monitoring of assets. Nowadays, the trend is to use these data for creating semantically rich three-dimensional (3D) models, broadly known as digital twins. The bottleneck appears when processing the large amount of data acquired with the laser scanner. This paper tackles the creation of IFC data models using classified point cloud data. The point labelling methodology is based on one in the state-of-the-art, whose results have been improved. Then, each group of points is converted to a triangulated mesh, and the resultant geometrical objects are placed in an IFC-based model in a low and high level of detail. Moreover, the resultant IFC model allows the enrichment of the captured geometry with additional information. © EG-ICE 2020 Workshop on Intelligent Computing in Engineering, Proceedings. All rights reserved.",,"Arch bridges; Digital twin; Laser applications; Masonry bridges; Geometrical objects; Laser scanner; Laser scanning; Level of detail; Masonry arch bridges; Point cloud data; State of the art; Three-dimensional (3D) model; Intelligent computing",,,,,"Horizon 2020 Framework Programme, H2020; Ministerio de Ciencia, Innovación y Universidades, MCIU: RTI2018-095893-B-C21; European Commission, EC; Horizon 2020: 769255; Innovation and Networks Executive Agency, INEA","This work has been partially supported by the Spanish Ministry of Science, Innovation and Universities through the project Ref. RTI2018-095893-B-C21. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 769255. This document reflects only the views of the author(s). Neither the Innovation and Networks Executive Agency (INEA) or the European Commission is in any way responsible for any use that may be made of the information it contains.",,,,,,,,,,"Arias, P., Terrestrial Laser Scanning and Non Parametric Methods in Masonry Arches Inspection (2010) International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII; (2017) Autodesk Developer Network - DXF Reference, , https://web.archive.org/web/20171019003526/http://usa.autodesk.com/adsk/servlet/item?id=24240325&siteID=123112, Autodesk (Accessed: 3 March 2020); Barber, C. B., Dobkin, D. P., Huhdanpaa, H., The Quickhull Algorithm for Convex Hulls (1996) ACM Transactions on Mathematical Software; Barrile, V., Candela, G., Fotia, A., (2019) Point cloud segmentation using image processing techniques for structural analysis; Beetz, J., Structured Vocabularies in Construction: Classifications, Taxonomies and Ontologies (2018) Building Information Modeling, pp. 155-165. , Aachen: Springer International Publishing; (2018) Umsetzung des Stufenplans 'Digitales Planen und Bauen' AP 1.2 'Szenariendefinition' und AP 1.3 'Empfehlung', , https://bim4infra.de/wp-content/uploads/2018/09/AP1.2AP1.3_BIM4INFRA_Bericht-Stufenplan.pdf, BIM4INFRA2020 (Accessed: 3 March 2020); Borrmann, A., (2017) IFC Infra Overall Architecture Project Documentation and Guidelines, , buildingSMART; Borrmann, A., The IFC-Bridge project - Extending the IFC standard to enable high-quality exchange of bridge information models (2019) Proceedings of the 2019 European Conference for Computing in Construction, 1, pp. 377-386. , (July); (2018) Industry Foundation Classes - Version 4.1.0.0, , https://standards.buildingsmart.org/IFC/RELEASE/IFC4_1/FINAL/HTML/, buildingSMART International (Accessed: 26 February 2020); (2007) IFC 2x3, , https://standards.buildingsmart.org/IFC/RELEASE/IFC2x3/TC1/HTML/, buildingSMART International Alliance for Interoperability; (2020), https://www.cloudcompare.org/, CloudComapre; Cohen-Steiner, D., Da, F., A greedy Delaunay-based surface reconstruction algorithm (2004) Visual Computer; Durán, M., Puentes Romanos Peninsulares: Tipología y Construcción (1996) Actas del I Congreso Nacional de Historia de la Construcción, , Madrid; Gressin, A., Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge (2013) ISPRS Journal of Photogrammetry and Remote Sensing, 79, pp. 240-251; He, K., Mask R-CNN (2017) Proceedings of the IEEE International Conference on Computer Vision, pp. 2980-2988; Hoppe, H., Piecewise smooth surface reconstruction (1994) Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1994; Lloyd, S. P., Least Squares Quantization in PCM (1982) IEEE Transactions on Information Theory; Lockley, S., (2007) xBIM Toolkit, , https://docs.xbim.net/index.html, (Accessed: 29 November 2019); Lu, R., Brilakis, I., Digital twinning of existing reinforced concrete bridges from labelled point clusters (2019) Automation in Construction, 105, p. 102837. , Elsevier, (February); Lu, R., Brilakis, I., Generating bridge geometric digital twins from point clouds (2019) Proceedings of the 2019 European Conference for Computing in Construction, 1, pp. 367-376; Ma, L., 3D Object Classification Using Geometric Features and Pairwise Relationships (2018) Computer-Aided Civil and Infrastructure Engineering, 33 (2), pp. 152-164. , Blackwell Publishing Inc; Medioni, G., Lee, M., Tang, C., A Computational Framework for Segmentation and Grouping, A Computational Framework for Segmentation and Grouping (2000), Elsevier Science; Olsen, M. J., (2010) terrestrial Laser Scanned-Based Structural Damage Assessment; (2020) RIEGL, Laser Measurement Systems GmbH, , RIEGL; Riveiro, B., DeJong, M., Conde, B., Automated processing of large point clouds for structural health monitoring of masonry arch bridges (2016) Automation in Construction, 72, pp. 258-268. , Elsevier B.V; El Saddik, A., Digital Twins: The Convergence of Multimedia Technologies (2018) IEEE Multimedia. IEEE Computer Society, 25 (2), pp. 87-92; Sánchez-Rodríguez, A., Detection of structural faults in piers of masonry arch bridges through automated processing of laser scanning data (2018) Structural Control and Health Monitoring, 25 (3), p. e2126; Soilán, M., Review of Laser Scanning Technologies and Their Applications for Road and Railway Infrastructure Monitoring (2019) Infrastructures, 4 (58); (2013) Project Progression Planning with MPS 3.0, , http://support.vicosoftware.com/FlareFiles/Content/KB/Trimble-ProgressionPlanningV15.pdf, Trimble; Ural, A., Turkish historical arch bridges and their deteriorations and failures (2008) Engineering Failure Analysis. Pergamon, 15 (1-2), pp. 43-53; Walsh, S. B., Data Processing of Point Clouds for Object Detection for Structural Engineering Applications (2013) Computer-Aided Civil and Infrastructure Engineering, 28 (7), pp. 495-508. , Wiley/Blackwell (10.1111); (2019) B1. Object Files (.obj), , Wavefront; Zhao, H. K., Implicit and nonparametric shape reconstruction from unorganized data using a variational level set method (2000) Computer Vision and Image Understanding; Zhao, Y.-P., Vela, P. A., Scan2BrIM: IFC Model Generation of Concrete Bridges from Point Clouds (2019) Computing in Civil Engineering 2019, pp. 455-463. , Reston, VA: American Society of Civil Engineers","Sánchez-Rodríguez, A.; Department of Materials Engineering, Spain; email: anasanchez@uvigo.es","Ungureanu L.-C.Hartmann T.",,"Universitatsverlag der TU Berlin","27th EG-ICE International Workshop on Intelligent Computing in Engineering 2020","1 July 2020 through 4 July 2020",,162343,,9783798331556,,,"English","EG-ICE Workshop Intell. Comput. Eng., Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85091042409
"Blomfors M., Lundgren K., Zandi K.","57156650200;7005462844;57192681171;","Incorporation of pre-existing longitudinal cracks in finite element analyses of corroded reinforced concrete beams failing in anchorage",2020,"Structure and Infrastructure Engineering",,,,"1","17",,6,"10.1080/15732479.2020.1782444","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087878859&doi=10.1080%2f15732479.2020.1782444&partnerID=40&md5=e4d641f85b10506ccd8d896bd6aae922","Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden","Blomfors, M., Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden; Lundgren, K., Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden; Zandi, K., Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden","Transportation infrastructure is of fundamental importance and must be regularly assessed to ensure its safety and serviceability. The assessment of ageing reinforced concrete bridge stock may need to consider corrosion and cracks, as the likelihood of deterioration increases with age. This work accordingly investigates the incorporation of pre-existing anchorage zone corrosion cracks into the finite element modelling of reinforced concrete beam structural behaviour. Three methods of accounting for cracks were applied: (1) modifying the bond stress–slip relation, (2) weakening elements at the position of the crack, and (3) weakened discrete crack elements. The results show that modifying the bond stress–slip relation results in accurate predictions of the ultimate capacity when one-dimensional reinforcement bars are used in the model. Weakening elements at the position of the crack provides reasonable results when the anchorage is modelled with three-dimensional reinforcement bars and a frictional bond model. The implementation of discrete cracks was found to be unsuitable for the studied load situation, as compressive stresses formed perpendicular to the crack. It was concluded that the capacity of the studied case could be well estimated based on visual measurements, without knowledge of the exact corrosion level. © 2020, © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","Concrete–steel bond slip; digital twin modelling; nonlinear finite element analysis; pre-existing crack modelling; reinforced concrete; reinforcement anchorage zone","Anchorage zones; Anchorages (foundations); Concrete beams and girders; Concrete construction; Corrosion; Deterioration; Finite element method; Rebar; Accurate prediction; Corroded reinforced concrete beams; Finite element modelling; Reinforced concrete beams; Structural behaviour; Three-dimensional reinforcement; Transportation infrastructures; Visual measurements; Reinforced concrete",,,,,"Svenska Forskningsrådet Formas: 2017-01668","The work was supported by FORMAS under Grant number 2017-01668. The FE analyses were performed on resources provided by Chalmers Centre for Computational Science and Engineering (C3SE).",,,,,,,,,,"Arneth, A., Barbosa, H., Benton, T., Calvin, K., Calvo, E., Connors, S., Zommers, Z., (2019) Climate change and land: Summary for policymakers. an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems, , …; Berdica, K., An introduction to road vulnerability: What has been done, is done and should be done (2002) Transport Policy, 9, pp. 117-127; Biondini, F., Vergani, M., Deteriorating beam finite element for nonlinear analysis of concrete structures under corrosion (2015) Structure and Infrastructure Engineering, 11, pp. 519-532; Blomfors, M., Zandi, K., Lundgren, K., Coronelli, D., Engineering bond model for corroded reinforcement (2018) Engineering Structures, 156, pp. 394-410; Bradley, D., Hehenberger, P., (2016) Mechatronic futures: Challenges and solutions for mechatronic systems and their designers, , Springer International Publishing, &, Switzerland; Cavaco, E.S., Neves, L.A.C., Casas, J.R., On the robustness to corrosion in the life cycle assessment of an existing reinforced concrete bridge (2018) Structure and Infrastructure Engineering, 14, pp. 137-150; Christidis, P., Leduc, G., Longer and heavier vehicles for freight transport (2009) JRC Scientific and Technical Reports, 40. , EUR 23933; Cornelissen, H.A.W., Hordijk, D.A., Reinhardt, H.W., Experimental determination of crack softening characteristics of normalweight and lightweight (1986) Heron, 32, pp. 45-56; Coronelli, D., Gambarova, P., Structural assessment of corroded reinforced concrete beams: Modeling guidelines (2004) Journal of Structural Engineering, 130 (8), pp. 1214-1224; (2017) Fe-Software DIANA 10, p. 2. , Delft, The Netherlands; Feenstra, P.H., (1993) Computational aspects of biaxial stress in plain and reinforced concrete, , Delft University of Technology; (2013) Model Code 2010. FIB model code for concrete structures 2010, , Lausanne, Switzerland; Gálvez, J.C., Červenka, J., Cendón, D.A., Saouma, V., A discrete crack approach to normal/shear cracking of concrete (2002) Cement and Concrete Research, 32, pp. 1567-1585; Hanjari, Z., Kettil, P., Lundgren, K., Analysis of mechanical behavior of corroded reinforced concrete structures (2012) ACI Structural Journal, 108, pp. 532-541; Hendriks, M.A.N., de Boer, A., Belletti, B., Guidelines for Nonlinear Finite Element Analysis of Concrete Structures (2017) Rijkswaterstaat Centre for Infrastructure, , Report RTD:1016-1:2017; Jansson, A., Lofgren, I., Lundgren, K., Gylltoft, K., Bond of reinforcement in self-compacting steel-fibre-reinforced concrete (2012) Magazine of Concrete Research, 64, pp. 617-630; Jiradilok, P., Nagai, K., Matsumoto, K., Meso-scale modeling of non-uniformly corroded reinforced concrete using 3D discrete analysis (2019) Engineering Structures, 197, p. 109378; Jiradilok, P., Wang, Y., Nagai, K., Matsumoto, K., Development of discrete meso-scale bond model for corrosion damage at steel-concrete interface based on tests with/without concrete damage (2020) Construction and Building Materials, 236, p. 117615; Lundgren, K., Bond between ribbed bars and concrete (2005) Magazine of Concrete Research, 57, pp. 371-382; Lundgren, K., Kettil, P., Zandi Hanjari, K., Schlune, H., Roman, A.S.S., Analytical model for the bond-slip behaviour of corroded ribbed reinforcement (2012) Structure and Infrastructure Engineering, 8, pp. 157-169; Malm, R., Holmgren, J., Cracking in deep beams owing to shear loading (2008) Magazine of Concrete Research, 60, pp. 381-388; Muttoni, A., Fernández Ruiz, M., The levels-of-approximation approach in MC 2010: Application to punching shear provisions (2012) Structural Concrete, 13 (1), pp. 32-41; Nasr, A., Björnsson, I., Honfi, D., Larsson Ivanov, O., Johansson, J., Kjellström, E., A review of the potential impacts of climate change on the safety and performance of bridges (2019) Sustainable and Resilient Infrastructure, pp. 1-21; Ng, P.L., Ma, F.J., Kwan, A.K.H., Crack analysis of reinforced concrete members with and without crack queuing algorithm (2019) Structural Engineering and Mechanics, 70 (1), pp. 43-54; Rots, J.G., (1988) Computational modelling of concrete fracture, , Delft University; Rots, J.G., Blaauwendraad, J., Crack models for concrete: Discrete or smeared? Fixed Multi-Directional or Rotating? (1989) Heron, 34 (1), pp. 3-59; Saether, I., Bond deterioration of corroded steel bars in concrete (2011) Structure and Infrastructure Engineering, 7, pp. 415-429; Shu, J., Shear assessment of a reinforced concrete bridge deck slab according to level-of-approximation approach (2018) Structural Concrete, 19, pp. 1838-1850; Tahershamsi, M., Fernandez, I., Zandi, K., Lundgren, K., Four levels to assess anchorage capacity of corroded reinforcement in concrete (2017) Engineering Structures, 147, pp. 434-447; Vecchio, F., Collins, M., Compression response of cracked reinforced concrete (1993) Journal of Structural Engineering, 119, pp. 3590-3610; Wittmann, F.H., Rokugo, K., Brühwiler, E., Mihashi, H., Simonin, P., Fracture energy and strain softening of concrete as determined by means of compact tension specimens (1988) Materials and Structures, 21 (1), pp. 21-32; Zandi, K., Corrosion-induced cover spalling and anchorage capacity (2015) Structure and Infrastructure Engineering, 11, pp. 1518-1547; Zandi, K., Boubitsas, D., Fahimi, S., Johansson, M., Spetz, J., Flansbjer, M., (2019), Gothenburg:, &, Autonomous automated non-intrusive condition assessment of concrete structures,. Report ACE 2019:5; Zandi, K., Ransom, E.H., Topac, T., Chen, R., Beniwal, S., Blomfors, M., Chang, F.-K., A framework for digital twin of civil infrastructure - Challenges and opportunities (2019) The 12th International Workshop on Structural Health Monitoring, Stanford, California, USA, p. 7. , Lancaster, PA:, …, September 10-12, 2019 (p., : DEStech Publications, Inc","Blomfors, M.; Department of Architecture and Civil Engineering, Sweden; email: blomfors@chalmers.se",,,"Taylor and Francis Ltd.",,,,,15732479,,,,"English","Struct. Infrastructure Eng.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85087878859
"Jia W., Wang W., Zhang Z.","57738142000;57233076600;57828224000;","From simple digital twin to complex digital twin Part I: A novel modeling method for multi-scale and multi-scenario digital twin",2022,"Advanced Engineering Informatics","53",,"101706","","",,5,"10.1016/j.aei.2022.101706","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135285805&doi=10.1016%2fj.aei.2022.101706&partnerID=40&md5=82c8a2388c83beeb742a33aabaee560a","School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China","Jia, W., School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Wang, W., School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Zhang, Z., School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China","In recent years, the digital twin has attracted widespread attention as an important means of digitalization and intelligence. However, the digital twin is becoming more and more complex due to the expansion of need on the simulation of multi-scale and multi-scenario in reality. The instance of digital twin in references mostly concentrates a particular application, while it is still a lack of a method for constructing the complex digital twin in the total elements, the variable scale of working environments, changeable process, not even the coupling effects. In this paper, a novel modeling method for such a complex digital twin is proposed based on the standardized processing on the model division and assembly. Firstly, the complex model of digital twin is divided into several simple models according to the composition, context, component, and code in 4C architecture. Composition and context make the digital twin focus on the effective elements in a specific scale and scenario. Component and code develop the digital twin in standard-based modularization. Secondly, assemble the simple models of digital twins into the complex model through information fusion, multi-scale association and multi-scenarios iterations. Ontology establishes the complete information library of the entities on different digital twins. Knowledge graph bridges the structure relationship between the different scales of digital twins. The scenario iterations realize the behavior interaction and the accuracy calculation results. It provides an implementable method to construct a complex model of digital twin, and the reuse of components and code also enables rapid development of digital twins. © 2022","Complex digital twin; Digital twin modeling; Simple digital twin; Smart manufacturing","Digital libraries; Knowledge graph; Complex digital twin; Complex model; Digital twin modeling; Model method; Multi scenarios; Multi-scales; Simple digital twin; Simple modeling; Simple++; Smart manufacturing; Modular construction",,,,,"Sichuan Province Science and Technology Support Program: 2021YFG0052,2022YFG0059; National Natural Science Foundation of China, NSFC: 52175456; Fundamental Research Funds for the Central Universities: ZYGX2019J032","This work is supported by the National Natural Science Foundation of China (No. 52175456), Sichuan Science and Technology Program (2021YFG0052,2022YFG0059), and the Fundamental Research Funds for the Central Universities (ZYGX2019J032).",,,,,,,,,,"Grieves, M., Digital twin: manufacturing excellence through virtual factory replication, White paper (2014), US Florida Technology Melbourne; Glaessgen, E., Stargel, D., (2012), https://arc.aiaa.org/doi/pdf/10.2514/6.2012-1818, The digital twin paradigm for future NASA and U.S. Air Force vehicles, in: Proc. 53rd IAA/ASME/ASCE/AHS/ASC Struct. 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Manuf., 34, pp. 567-597; https://www.iso.org/standard/78744.html, ISO23247. 2020. “ISO/AWI 23247 - Digital Twin Manufacturing Framework.” Accessed 03 October 2020. Part3:; Rožanec, J.M., Jinzhi, L., Košmerlj, A., Kenda, K., Dimitris, K., Jovanoski, V., Rupnik, J., Antoniou, G., Towards actionable cognitive digital twins for manufacturing (2020), C. Fortuna (Eds.) 2020 International Workshop on Semantic Digital Twins, SeDiT 2020, CEUR-WS","Wang, W.; School of Mechanical and Electrical Engineering, China; email: wangwhit@163.com",,,"Elsevier Ltd",,,,,14740346,,,,"English","Adv. Eng. Inf.",Article,"Final","",Scopus,2-s2.0-85135285805
"Dang H., Tatipamula M., Nguyen H.X.","57617394600;55900222400;57199967463;","Cloud-Based Digital Twinning for Structural Health Monitoring Using Deep Learning",2022,"IEEE Transactions on Industrial Informatics","18","6",,"3820","3830",,5,"10.1109/TII.2021.3115119","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115797070&doi=10.1109%2fTII.2021.3115119&partnerID=40&md5=a431312923e914a6a6c0b148e9a7e83c","Faculty of Building and Industrial Construction, Hanoi University of Civil Engineering, Hanoi, 100000, Viet Nam; Ericsson Silicon Valley, Santa Clara, CA  95054, United States; London Digital Twin Research Centre, Faculty of Science and Technology, Middlesex University, London, NW4 4BT, United Kingdom","Dang, H., Faculty of Building and Industrial Construction, Hanoi University of Civil Engineering, Hanoi, 100000, Viet Nam; Tatipamula, M., Ericsson Silicon Valley, Santa Clara, CA  95054, United States; Nguyen, H.X., London Digital Twin Research Centre, Faculty of Science and Technology, Middlesex University, London, NW4 4BT, United Kingdom","Digital twin (DT) technology has recently gathered pace in the engineering communities as it allows for the convergence of the real structure and its digital counterpart throughout their entire life-cycle. With the rapid development of supporting technologies, including machine learning (ML), 5G/6G, cloud computing, and Internet of Things, DT has been moving progressively from concept to practice. In this article, a DT framework based on cloud computing and deep learning (DL) for structural health monitoring is proposed to efficiently perform real-time monitoring and proactive maintenance. The framework consists of structural components, device measurements, and digital models formed by combining different submodels, including mathematical, finite element, and ML ones. The data interaction among physical structure, digital model, and human interventions are enhanced by using cloud computing infrastructure and a user-friendly web application. The feasibility of the proposed framework is demonstrated via case studies of damage detection of model bridge and real bridge structures using DL algorithms, with high accuracy of 92%. © 2005-2012 IEEE.","Cloud computing; deep learning (DL); digital twin (DT); Internet of Things (IoT); structural health monitoring (SHM)","ABAQUS; Damage detection; Deep learning; E-learning; Engineering education; Finite element method; Internet of things; Learning algorithms; Life cycle; Structural health monitoring; Cloud-based; Cloud-computing; Computational modelling; Deep learning; Digital counterparts; Digital modeling; Engineering community; Real structure; Solid modelling; Cloud computing",,,,,,"This work was supported in part by an Institutional Links under Grant ID 429715093.",,,,,,,,,,"Grieves, M., Digital twin: Manufacturing excellence through virtual factory replication (2014) White Paper, 1, pp. 1-7; Nguyen, H.X., Trestian, R., To, D., Tatipamula, M., Digital twin for 5G and beyond (2021) Ieee Commun. 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TMCE. Las Palmas, pp. 209-217; Tao, F., Zhang, H., Liu, A., Nee, A.Y.C., Digital twin in industry: Stateof-the-art (2019) Ieee Trans. Ind. Informat., 15 (4), pp. 2405-2415. , Apr; Callaham, J.L., Koch, J.V., Brunton, B.W., Kutz, J.N., Brunton, S.L., Learning dominant physical processes with data-driven balance models (2021) Nat. Commun., 12 (1), pp. 1-10; Tran-Ngoc, H., Khatir, S., De Roeck, G., Bui-Tien, T., Nguyen-Ngoc, L., AbdelWahab, M., Model updating for nam O bridge using particle swarm optimization algorithm and genetic algorithm (2018) Sensors, 18 (12), pp. 4131-4150; Kumari, A., Tanwar, S., Tyagi, S., Kumar, N., Parizi, R.M., Choo, K.-K.R., Fog data analytics: A taxonomy and process model (2019) J. Netw. Comput. Appl., 128, pp. 90-104; Darwin, D., Dolan, C.W., Nilson, A.H., (2016) Design of Concrete Structures, , New York, NY, USA: McGraw-Hill Education; Kennedy, M.C., O'Hagan, A., Bayesian calibration of computer models (2001) J. Roy. Stat. Soc., Ser. B. (Stat. 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Eng., 63 (3), pp. 664-675. , Mar; He, K., Zhang, X., Ren, S., Sun, J., Delving deep into rectifiers: Surpassing human-level performance on imagenet classification (2015) Proc. Ieee Int. Conf. Comput. Vis., pp. 1026-1034; Qian, P., Zhang, D., Tian, X., Si, Y., Li, L., A novel wind turbine condition monitoring method based on cloud computing (2019) Renewable Energy, 135, pp. 390-398; Jeong, S., Hou, R., Lynch, J.P., Sohn, H., Law, K.H., A scalable cloud-based cyberinfrastructure platform for bridge monitoring (2019) Struct. Infrastruct. Eng., 15 (1), pp. 82-102","Nguyen, H.X.; London Digital Twin Research Centre, United Kingdom; email: H.Nguyen@mdx.ac.uk",,,"IEEE Computer Society",,,,,15513203,,,,"English","IEEE Trans. Ind. Inf.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85115797070
"Mohammadi M., Rashidi M., Mousavi V., Karami A., Yu Y., Samali B.","57210426046;36350170200;24462551500;57225367591;56430081600;7003397589;","Case study on accuracy comparison of digital twins developed for a heritage bridge via UAV photogrammetry and terrestrial laser scanning",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"1713","1720",,5,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125655909&partnerID=40&md5=1974fb7b12d50de23388c740e9db4036","Centre for Infrastructure Engineering, Western Sydney University, Penrith, Australia; Department of Information Engineering and Computer Science, University of Trento, Italy","Mohammadi, M., Centre for Infrastructure Engineering, Western Sydney University, Penrith, Australia; Rashidi, M., Centre for Infrastructure Engineering, Western Sydney University, Penrith, Australia; Mousavi, V., Centre for Infrastructure Engineering, Western Sydney University, Penrith, Australia; Karami, A., Department of Information Engineering and Computer Science, University of Trento, Italy; Yu, Y., Centre for Infrastructure Engineering, Western Sydney University, Penrith, Australia; Samali, B., Centre for Infrastructure Engineering, Western Sydney University, Penrith, Australia","Over the last decade, advanced remote technologies have been considerably utilised for digitisation of civil infrastructure assets, particularly bridges, and providing accurate data for indirect inspection and assessment through digital twins of their physical counterparts. Although advanced emerging technologies such as Unmanned Aerial Vehicles (UAVs) photogrammetry and Terrestrial Laser Scanning (TLS) established a suitable alternative against labour-intensive and expensive methods of direct inspections, the research is still lacking a comparative analysis of accuracy and reliability of the associated digital twins. This paper serves to investigate and evaluate the geometric accuracy of two 3D reality models generated from an existing heritage bridge in Australia extracted from both UAV-based photogrammetry and TLS based point clouds. The comparative results show the capability of both UAV photogrammetry and TLS based point clouds for bridge inspection; however, TLS expresses a higher level of points' density with suitable accuracy subject to implementation of precise as-built 3D reconstruction method. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","Accuracy; Bridge; Digital twin; Terrestrial Laser Scanning (TLS); UAV-based photogrammetry","Antennas; Bridges; Laser applications; Photogrammetry; Reliability analysis; Scanning; Steel beams and girders; Surveying instruments; Unmanned aerial vehicles (UAV); Accuracy; Accuracy comparisons; Case-studies; Civil infrastructures; Digitisation; Infrastructure assets; Point-clouds; Terrestrial laser scanning; Unmanned aerial vehicle-based photogrammetry; Structural health monitoring",,,,,,"The authors would like to acknowledge the technical support from Transport for New South Wales (TfNSW), Australia, and greatly appreciate the valuable advice and support of Houman Hatamian, Syed F. Nowmani, and Bradley Edwards during this research project.",,,,,,,,,,"Rashidi, M., Mohammadi, M., Sadeghlou Kivi, S., Abdolvand, M. M., Truong-Hong, L., Samali, B., A Decade of Modern Bridge Monitoring Using Terrestrial Laser Scanning: Review and Future Directions (2020) Remote Sensing, 12 (22), p. 3796; Rashidi, M., Ghodrat, M., Samali, B., Kendall, B., Zhang, C., Remedial Modelling of Steel Bridges through Application of Analytical Hierarchy Process (AHP) (2017) Applied Sciences, 7, p. 168; Chen, S., Laefer, D. F., Mangina, E., Zolanvari, S. M. I., Byrne, J., UAV Bridge Inspection through Evaluated 3D Reconstructions (2019) Journal of Bridge Engineering, 24 (4). , Articl; Jahanshahi, M. R., Masri, S. F., Sukhatme, G. S., Multi-image stitching and scene reconstruction for evaluating defect evolution in structures (2011) Structural Health Monitoring, 10 (6), pp. 643-657; Rashidi, M., Samali, B., Health Monitoring of Bridges Using RPAs (2021) East Asia-Pacific Conference on Structural Engineering and Construction, pp. 209-218. , Singapore, Springer; Pepe, M., Costantino, D., Crocetto, N., Restuccia Garofalo, A., 3D modeling of roman bridge by the integration of terrestrial and UAV photogrammetric survey for structural analysis purpose (2019) International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42, pp. 249-255; Remondino, F., Del Pizzo, S., Kersten, T. 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A., Kheyroddin, A., Ronagh, H., Performance of innovative composite buckling-restrained fuse for concentrically braced frames under cyclic loading (2020) Steel and Composite Structures, An International Journal; Rashidi, M., Ghodrat, M., Samali, B., Mohammadi, M., Decision Support Systems (2018) Management of information systems: IntechOpen; Mohammadi, M., Kafi, M. A., Kheyroddin, A., Ronagh, H. R., Rashidi, M., Experimental and Numerical Investigation of Innovative Composite Buckling-Restrained Fuse (2020) ACMSM25, Lecture Notes in Civil Engineering, Queensland, Australia, 37, pp. 113-121. , Springer Singapore; Mohammadi, M., Kafi, M. A., Kheyroddin, A., Ronagh, H. R., Experimental and numerical investigation of an innovative buckling-restrained fuse under cyclic loading (2019) Structures, 22, pp. 186-199; Tang, P., Akinci, B., Garrett, J., Laser Scanning for Bridge Inspection and Management (2007) IABSE Symposium Report, 93, pp. 17-24; Tang, P., Akinci, B., Automatic execution of workflows on laser-scanned data for extracting bridge surveying goals (2012) Advanced Engineering Informatics, Conference Paper, 26 (4), pp. 889-903; Gyetvai, N., Truong-Hong, L., Laefer, D. F., Laser scan-based structural assessment of wrought iron bridges: Guinness Bridge, Ireland (2018) Proceedings of the Institution of Civil Engineers - Engineering History and Heritage, 171 (2), pp. 76-89. , Articl; Gawronek, P., Makuch, M., TLS Measurement during Static Load Testing of a Railway Bridge (2019) ISPRS International Journal of Geo-Information, 8 (44); Lu, R., Rausch, C., Bolpagni, M., Brilakis, I., Haas, C. T., Geometric Accuracy of Digital Twins for Structural Health Monitoring (2020) IntechOpen; Kubota, S., Ho, C., Nishi, K., Construction and usage of three-dimensional data for road structures using terrestrial laser scanning and UAV with photogrammetry (2019) Proceedings of the 36th International Symposium on Automation and Robotics in Construction, ISARC 2019, pp. 136-143; Moon, D., Chung, S., Kwon, S., Seo, J., Shin, J., Comparison and utilization of point cloud generated from photogrammetry and laser scanning: 3D world model for smart heavy equipment planning (2019) Automation in Construction, 98, pp. 322-331. , Article; Ruggles, S., Comparison of SfM Computer Vision Point Clouds of a Landslide Derived from Multiple Small UAV Platforms and Sensors to a TLS based Model (2016) Journal of Unmanned Vehicle Systems, 4 (4), pp. 1-13; Koutsoudis, A., Vidmar, B., Ioannakis, G., Arnaoutoglou, F., Pavlidis, G., Chamzas, C., Multi-image 3D reconstruction data evaluation (2014) Journal of Cultural Heritage, 15 (1), pp. 73-79; Mousavi, V., The performance evaluation of multi-image 3D reconstruction software with different sensors (2018) Measurement, 120, pp. 1-10; Leica ScanStation P50/P40/P30, Laser Scanner User Manual Leica Geosystems, Heerbrugg, Switzerland2018, Version 6.0.1; (2008) Optical 3d-Measuring Systems (Multiple View Systems Based On Area Scanning), p. 20. , VDI/VDE 2634, Germany: Verlag des Vereins Deutscher Ingenieure; Gom Inspect Suite sotware, , https://support.gom.com, GOM GmbH. Available",,,,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021","30 June 2021 through 2 July 2021",,179266,25643738,,,,"English","Int. Conf. Struct. Health. Monit. Intelligent Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85125655909
"Ahuett-Garza H., Coronado P.D.U.","13104740900;56151369900;","A reference model for evolving digital twins and its application to cases in the manufacturing floor",2019,"Smart and Sustainable Manufacturing Systems","3","2",,"1","13",,5,"10.1520/SSMS20190049","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091530072&doi=10.1520%2fSSMS20190049&partnerID=40&md5=8767fceea92bbef341013ae780a95303","Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey, N.L.  64849, Mexico","Ahuett-Garza, H., Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey, N.L.  64849, Mexico; Coronado, P.D.U., Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey, N.L.  64849, Mexico","Cyberphysical systems (CPSs) represent the major contribution of Industry 4.0 to modern manufacturing systems. Once implemented, CPS will increase the efficiency of operations, improve product quality, reduce waste, and optimize the use of resources and assets. A critical element for the realization of CPSs is the Digital Twin (DT), a concept that bridges the gap between the physical and digital realms. Although significant strides have been made to establish the nature of DT, there is still a need for comprehensive reference models and test cases that can help guide the design and deployment of DT. This work presents a reference model for the development of DT. The model considers factors such as stage in the life cycle of a product, the purpose of the digital representation, and the use of the information that the digital representation provides. Cases are presented and analyzed in terms of the proposed model. © 2019 by ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959.","Cyber-physical systems; Digital Twin; Industry 4.0; Reference models","Digital twin; Embedded systems; Manufacture; Critical elements; Cyber physical systems (CPSs); Digital representations; ITS applications; Manufacturing floor; Reference modeling; Reference models; Test case; Life cycle",,,,,"Instituto Tecnológico y de Estudios Superiores de Monterrey, ITESM","The authors thank the Automotive Consortium for Cyber-Physical Systems at Tecnol?gico de Monterrey for the support of this work.",,,,,,,,,,"Ahuett-Garza, H., Kurfess, T., A brief discussion on the trends of habilitating technologies for industry 4.0 and smart manufacturing (2018) Manufacturing Letters, 15, pp. 60-63. , https://doi.org/10.1016/j.mfglet.2018.02.011, January; Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., Sauer, O., Cyber-physical systems in manufacturing (2016) CIRP Annals, 65 (2), pp. 621-641. , https://doi.org/10.1016/j.cirp.2016.06.005; Lee, J., Bagheri, B., Kao, H.-A., Recent advances and trends of cyber-physical systems and big data analytics in industrial informatics (2014) INDIN 2014: 12th IEEE International Conference on Industrial Informatics (INDIN), , paper presentation, Porto Alegre, Brazil, July 27-30; Wu, D., Rosen, D.W., Wang, L., Schaefer, D., Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation (2015) Computer-Aided Design, 59, pp. 1-14. , https://doi.org/10.1016/j.cad.2014.07.006, February; Monostori, L., Cyber-physical production systems: Roots, expectations and R&D challenges (2014) Procedia CIRP, 17, pp. 9-13. , https://doi.org/10.1016/j.procir.2014.03.115; Lee, J., Bagheri, B., Kao, H.-A., A cyber-physical systems architecture for industry 4.0-based manufacturing systems (2015) Manufacturing Letters, 3, pp. 18-23. , https://doi.org/10.1016/j.mfglet.2014.12.001, January; Rosen, R., von Wichert, G., Lo, G., Bettenhausen, K.D., About the importance of autonomy and digital twins for the future of manufacturing (2015) IFAC-PapersOnLine, 48 (3), pp. 567-572. , https://doi.org/10.1016/j.ifacol.2015.06.141; Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W., Digital twin in manufacturing: A categorical literature review and classification (2018) IFAC-PapersOnLine, 51 (11), pp. 1016-1022. , https://doi.org/10.1016/j.ifacol.2018.08.474; Negri, E., Fumagalli, L., Macchi, M., A review of the roles of digital twin in CPS-based production systems (2017) Procedia Manufacturing, 11, pp. 939-948. , https://doi.org/10.1016/j.promfg.2017.07.198; Schleich, B., Anwer, N., Mathieu, L., Wartzack, S., Shaping the digital twin for design and production engineering (2017) CIRP Annals, 66 (1), pp. 141-144. , https://doi.org/10.1016/j.cirp.2017.04.040; Tao, F., Zhang, M., Digital twin shop-floor: A new shop-floor paradigm towards smart manufacturing (2017) IEEE Access, 5, pp. 20418-20427. , https://doi.org/10.1109/ACCESS.2017.2756069; Qi, Q., Tao, F., Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison (2018) IEEE Access, 6, pp. 3585-3593. , https://doi.org/10.1109/ACCESS.2018.2793265; Lohtander, M., Ahonen, N., Lanz, M., Ratava, J., Kaakkunen, J., Micro manufacturing unit and the corresponding 3D-model for the digital twin (2018) Procedia Manufacturing, 25, pp. 55-61. , https://doi.org/10.1016/j.promfg.2018.06.057; Wang, L., Törngren, M., Onori, M., Current status and advancement of cyber-physical systems in manufacturing (2015) Journal of Manufacturing Systems, 37, pp. 517-527. , https://doi.org/10.1016/j.jmsy.2015.04.008, October; Bonnard, R., Hascoët, J.-Y., Mognol, P., Zancul, E., Alvares, A.J., Hierarchical object-oriented model (HOOM) for additive manufacturing digital thread (2019) Journal of Manufacturing Systems, 50, pp. 36-52. , https://doi.org/10.1016/j.jmsy.2018.11.003, January; Singh, V., Willcox, K.E., Engineering design with digital thread (2018) AIAA Journal, 56 (11), pp. 4515-4528. , https://doi.org/10.2514/1.J057255, November; Bone, M., Blackburn, M., Kruse, B., Dzielski, J., Hagedorn, T., Grosse, I., Toward an interoperability and integration framework to enable digital thread (2018) Systems, 6 (4), p. 46. , https://doi.org/10.3390/systems6040046; García-Garza, M.A., Ahuett-Garza, H., Lopez, M.G., Orta-Castañón, P., Kurfess, T.R., Coronado, P.D.U., Güemes-Castorena, D., Salinas, S., A case about the upgrade of manufacturing equipment for insertion into an industry 4.0 environment (2019) Sensors, 19 (15), p. 3304. , https://doi.org/10.3390/s19153304, August","Ahuett-Garza, H.; Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501, Mexico; email: horacio.ahuett@tec.mx",,,"ASTM International",,,,,25206478,,,,"English","Smart Sustain. Manufact. Syst.",Article,"Final","",Scopus,2-s2.0-85091530072
"Woitsch R., Sumereder A., Falcioni D.","6507234671;57203983141;36605874300;","Model-based data integration along the product & service life cycle supported by digital twinning",2022,"Computers in Industry","140",,"103648","","",,4,"10.1016/j.compind.2022.103648","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127693291&doi=10.1016%2fj.compind.2022.103648&partnerID=40&md5=8541f71e865936089352bbfdb4d89ef6","BOC Group, Operngasse 20B, Vienna, 1040, Austria","Woitsch, R., BOC Group, Operngasse 20B, Vienna, 1040, Austria; Sumereder, A., BOC Group, Operngasse 20B, Vienna, 1040, Austria; Falcioni, D., BOC Group, Operngasse 20B, Vienna, 1040, Austria","Currently, a mega-trend on digitization and servitization using digital technologies and digital twins to support digital (business) transformation can be observed. In literature, emerging digital technologies are considered as an enabler for the creation of additional value, the strengthening of the customer relationship, and as an accelerator of the servitization process in manufacturing. The introduction of such technologies may result in adaptations of the product & service life cycle as well as the business model. A generic product & service life cycle consisting of four phases – design, simulation, manufacturing, and usage of products and services – serves as a foundation for the integration of digital technologies and related data. It can be observed that there is a gap with respect to data integration between the manufacturing and the usage of products and services. A model-based approach including digital twinning is applied to bridge this gap and to show how data can be integrated along the product & service life cycle. The conceptual approach of such a model-based digital twin environment is presented in form of a meta model. To depict how the integration could be eased and guided in manufacturing, findings of the European project Change2Twin, where a digital twin is established for a paint production pilot, are introduced. This industrial manufacturing pilot was supported by production process models. Additionally, a physical experiment was created to raise awareness for the challenges of digitization and servitization. © 2022 Elsevier B.V.","Digital twin; Model-based approach; Product & service life cycle; Product service system","Manufacture; Product design; Public relations; Service life; Digital technologies; Digitisation; Model based approach; Model-based OPC; Product and services; Product life cycles; Product service life; Product-service systems; Service life cycle; Servitization; Data integration",,,,,"951956","We thank Francisco Perez (GRAPHENSTONE) and Eugenio Jose Quintero Carrion from (CT-Ingenieros) from the Change2Twin project for their cooperation to establish the physical experiment of the paint production process. This work was supported by the European H2020 project Change2Twin [grant number 951956 ]. Webpage: https://www.change2twin.eu .","We thank Francisco Perez (GRAPHENSTONE) and Eugenio Jose Quintero Carrion from (CT-Ingenieros) from the Change2Twin project for their cooperation to establish the physical experiment of the paint production process. This work was supported by the European H2020 project Change2Twin [grant number 951956]. Webpage: https://www.change2twin.eu.",,,,,,,,,"(2021), https://adoxx.org/live/web/change2twin/downloads, ADOxx.org Paint Production Process. 〈〉; Augustine, P., Chapter four – the industry use cases for the digital twin idea (2020) Advances in Computers, pp. 79-105. , Pethuru Raj Preetha Evangeline Elsevier; Baines, T.S., Lightfoot, H., Evans, S., Neely, A.D., Greenough, R., Peppard, J., Roy, R., Wilson, H., State-of-the-art in product-service systems (2007) Proc. Inst. Mech. Eng. Part B J. Eng. 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In: Proceedings of the 2015 International Conference on Enterprise Systems (ES); (2017), http://www3.weforum.org/docs/WEF_White_Paper_Technology_Innovation_Future_of_Production_2017.pdf, World Economic Forum Technology and Innovation for the Future of Production: Accelerating Value Creation. White Paper. 〈〉; (2021), http://reports.weforum.org/digital-transformation/wp-content/blogs.dir/94/mp/files/pages/files/dti-executive-summary-20180510.pdf, World Economic Forum Digital Transformation Initiative. 〈〉 (Slide 1, 15, 20, 27); Zhang, C., Zhou, G., He, J., Li, Z., Cheng, W., A data- and knowledge-driven framework for digital twin manufacturing cell (2019) Procedia CIRP; Zhou, M.C., Venkatesh, K., (1999), Modeling, Simulation, and Control of Flexible Manufacturing Systems: A Petri Net Approach","Woitsch, R.; BOC Group, Operngasse 20B, Austria; email: robert.woitsch@boc-eu.com
Sumereder, A.; BOC Group, Operngasse 20B, Austria; email: anna.sumereder@boc-eu.com",,,"Elsevier B.V.",,,,,01663615,,CINUD,,"English","Comput Ind",Article,"Final","",Scopus,2-s2.0-85127693291
"Omer M., Margetts L., Mosleh M.H., Cunningham L.S.","57209220242;25636044400;57189458030;8879063800;","Inspection of Concrete Bridge Structures: Case Study Comparing Conventional Techniques with a Virtual Reality Approach",2021,"Journal of Bridge Engineering","26","10","05021010","","",,4,"10.1061/(ASCE)BE.1943-5592.0001759","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111924311&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001759&partnerID=40&md5=ea034ea2892f88daefe6f59ec2cefb4f","Dept. of Mechanical, Aerospace and Civil Engineering, Univ. of Manchester, Manchester, M13 9PL, United Kingdom","Omer, M., Dept. of Mechanical, Aerospace and Civil Engineering, Univ. of Manchester, Manchester, M13 9PL, United Kingdom; Margetts, L., Dept. of Mechanical, Aerospace and Civil Engineering, Univ. of Manchester, Manchester, M13 9PL, United Kingdom; Mosleh, M.H., Dept. of Mechanical, Aerospace and Civil Engineering, Univ. of Manchester, Manchester, M13 9PL, United Kingdom; Cunningham, L.S., Dept. of Mechanical, Aerospace and Civil Engineering, Univ. of Manchester, Manchester, M13 9PL, United Kingdom","Recent high-profile collapses coupled with an aging bridge stock, increased loading, and the pressures of climate change have led to a greater focus on bridge management by policymakers. To prevent any negative socioeconomic impacts, timely inspection of bridges becomes of prime importance. Visual inspection is standard practice around the world but is subjective in nature and is influenced by many factors that can affect the accuracy of results and future decisions. The research presented here critically compares the conventional visual inspection approach with a virtual reality (VR) inspection technique that combines Lidar and VR, applied for the first time to bridges made of reinforced concrete. Inspection of the Mancunian Way, an elevated motorway in Manchester, UK, is performed by the conventional visual approach and the VR approach. Digital virtual twins of the bridge are developed. Lidar is used to capture a 3D image of the geometric surface of the bridge incorporating all its defects. The image is postprocessed and a VR application is created using Unity, a software development kit, for inspection of bridges in an immersive 3D virtual environment. The resulting VR app is evaluated subjectively by conducting a critical comparison between both methods. The results demonstrate promising improvements over the conventional inspection technique. It is intended that this research will benefit civil engineers in inspecting bridges as well as policymakers who may revise bridge inspection codes and procedures. © 2021 American Society of Civil Engineers.","Bridge; Concrete; Digital twin; Inspection; Lidar; Virtual reality","Application programs; Binary alloys; Climate change; Inspection; Optical radar; Potassium alloys; Reinforced concrete; Software design; Uranium alloys; 3-D virtual environment; Concrete bridge structures; Conventional techniques; Geometric surfaces; Inspection technique; Socio-economic impacts; Software development kit; Standard practices; Virtual reality",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/N026136/1; University of Manchester","The authors would like to thank the School of Engineering at the University of Manchester for providing funding to Mr. Muhammad Omer to carry out his Ph.D. research and also for arranging all the hardware and software for this research project. Lee Margetts was supported by EPSRC grant EP/N026136/1.",,,,,,,,,,"Afana, A., Solé-Benet, A., Pérez, J.C., (2010) Determination of Soil Erosion Using Laser Scanners, pp. 39-42. , In Proc. 19th World Congress of Soil Science: Soil Solutions for a Changing World, edited by R. J. Gilkes, and N. Prakongkep, Brisbane, Australia: International Union of Soil Sciences; (2017) 2017 Report Card for American's Infrastructure, , ASCE. Reston, VA: ASCE; Ayres, G., Mehmood, R., (2009) On Discovering Road Traffic Information Using Virtual Reality Simulations, pp. 411-416. , In Proc. 11th Int. Conf. on Computer Modelling and Simulation, Piscataway, NJ: IEEE; Baimas, N., McClean, J., Mancunian way bearing replacements (1998) Proc. Inst. Civ. Eng. Munic. Eng., 127 (3), pp. 124-131. , https://doi.org/10.1680/imuen.1998.30988; Bellian, J.A., Kerans, C., Jennette, D.C., Digital outcrop models: Applications of terrestrial scanning Lidar technology in stratigraphic modeling (2005) J. Sediment. 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"Baisthakur S., Chakraborty A.","57216491890;14015058600;","Experimental verification for load rating of steel truss bridge using an improved Hamiltonian Monte Carlo-based Bayesian model updating",2021,"Journal of Civil Structural Health Monitoring","11","4",,"1093","1112",,4,"10.1007/s13349-021-00495-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108359338&doi=10.1007%2fs13349-021-00495-8&partnerID=40&md5=57c8aa55f2a65a13b3c903cc43d6d32b","Larson & Toubro ECC, Chennai, 600089, India; Civil Engineering Department, Indian Institute of Technology Guwahati, Assam, India","Baisthakur, S., Larson & Toubro ECC, Chennai, 600089, India; Chakraborty, A., Civil Engineering Department, Indian Institute of Technology Guwahati, Assam, India","The load rating of a steel truss bridge is experimentally identified in this study using an improved Bayesian model updating algorithm. The initial element model is sequentially updated to match the static and dynamic characteristics of the bridge. For this purpose, a modified version of the Hamiltonian Monte Carlo (HMC) simulation is adopted for closed-form candidate generation that helps in faster convergence compared to the Markov Chain Monte Carlo simulation. The updated model works as a digital twin of the original structure to predict its load-carrying capacity and performance under proof or design load. The proposed approach incorporates in-situ conditions in its formulation and helps to reduce the risk involved in bridge load testing at its full capacity. The rating factor for each member is estimated from the updated model, which also indicates the weak links and possible failure mechanism. The efficiency of the improved HMC-based algorithm is demonstrated using limited sensor data, which can be easily adopted for other existing bridges. © 2021, Springer-Verlag GmbH Germany, part of Springer Nature.","Bayesian Inference; Bridge rating; Finite element model updating; Hamiltonian Monte Carlo Simulation; Markov Chain Monte Carlo Simulation","Bayesian networks; Digital twin; Failure (mechanical); Hamiltonians; Load testing; Markov chains; Monte Carlo methods; Trusses; Bayesian model updating; Candidate generation; Experimental verification; Faster convergence; Markov chain monte carlo simulation; Original structures; Static and dynamic characteristics; Steel truss bridge; Steel bridges",,,,,"Department of Science and Technology, Ministry of Science and Technology, India, डीएसटी; Science and Engineering Research Board, SERB: CRG/2020/005090","This publication resulted from the research supported by the DST, Govt. of India, Science and Engineering Research Board Grant No. CRG/2020/005090.",,,,,,,,,,"(2000) Manual for condition evaluation of bridges, 1994, , American Association of State Highway and Transportation Officials, Washington; Phares, B.M., Washer, G.A., Rolander, D.D., Graybeal, B.A., Moore, M., Routine highway bridge inspection condition documentation accuracy and reliability (2004) J Bridge Eng, 9 (4), pp. 403-413; Breña, S.F., Jeffrey, A.E., Civjan, S.A., Evaluation of a noncomposite steel girder bridge through live-load field testing (2013) J Bridge Eng, 18 (7), pp. 690-699; Moses, F., Lebet, J.P., Bez, R., Applications of field testing to bridge evaluation (1994) J Struct Eng, 120 (6), pp. 1745-1762; Saraf, V., Nowak, A.S., Proof load testing of deteriorated steel girder bridges (1998) J Bridge Eng, 3 (2), pp. 82-89; Casas, J.R., Gómez, J.D., Load rating of highway bridges by proof-loading (2013) KSCE J Civ Eng, 17 (3), pp. 556-567; Boothby, T.E., Craig, R.J., Experimental load rating study of a historic truss bridge (1997) J Bridge Eng, 2 (1), pp. 18-26; Cheung, M.S., Tadros, G., Brown, T., Dilger, W., Field monitoring and research on performance of the confederation bridge (1997) Can J Civ Eng, 24 (6), p. 951; Bakht, B., Testing of the manitou bridge to determine its safe load carrying capacity (1981) Can J Civ Eng, 8 (2), pp. 218-229; Lantsoght, E., van der Veen, C., de Boer, A., Hordijk, D.A., Proof load testing of reinforced concrete slab bridges in the Netherlands (2017) Struct Concr, 18 (4), pp. 597-606; Stewart, M.G., Val, D.V., Role of load history in reliability-based decision analysis of aging bridges (1999) J Struct Eng, 125 (7), pp. 776-783; Faber, M.H., Val, D.V., Stewart, M.G., Proof load testing for bridge assessment and upgrading (2000) Eng Struct, 22 (12), pp. 1677-1689; Fu, G., Tang, J., Risk-based proof-load requirements for bridge evaluation (1995) J Struct Eng, 121 (3), pp. 542-556; Shah, S.P., Popovics, J.S., Subramaniam, K.V., Aldea, C.-M., New directions in concrete health monitoring technology (2000) J Eng Mech, 126 (7), pp. 754-760; Doebling, S.W., Farrar, C.R., Prime, M.B., A summary review of vibration-based damage identification methods (1998) Shock Vib Dig, 30 (2), pp. 91-105; Wong, K.-Y., Instrumentation and health monitoring of cable-supported bridges (2004) Struct Control Health Monit, 11 (2), pp. 91-124; Al-Khateeb, H.T., Shenton, H.W., Chajes, M.J., Computing continuous load rating factors for bridges using structural health monitoring data (2018) J Civ Struct Health Monit, 8 (5), pp. 721-735; Bakht, B., Jaeger, L.G., Bridge testing—a surprise every time (1990) J Struct Eng, 116 (5), pp. 1370-1383; Bakht, B., Jaeger, L.G., Bearing restraint in slab-on-girder bridges (1988) J Struct Eng, 114 (12), pp. 2724-2740; Bakht, B., Jaeger, L.G., Ultimate load test of slab-on-girder bridge (1992) J Struct Eng, 118 (6), pp. 1608-1624; Chajes, M.J., Mertz, D.R., Commander, B., Experimental load rating of a posted bridge (1997) J Bridge Eng, 2 (1), pp. 1-10; Jáuregui, D.V., (1999) Measurement-Based Evaluation of Non-Composite, , PhD Thesis, University of Texas, Austin, USA; Wang, N., O’Malley, C., Ellingwood, B.R., Zureick, A.-H., Bridge rating using system reliability assessment. 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I: Bayesian statistical framework (1998) J Eng Mech, 124 (4), pp. 455-461; Katafygiotis, L.S., Papadimitriou, C., Lam, H.-F., A probabilistic approach to structural model updating (1998) Soil Dyn Earthq Eng, 17 (7-8), pp. 495-507; Beck, J.L., Au, S.-K., Bayesian updating of structural models and reliability using Markov Chain Monte Carlo simulation (2002) J Eng Mech, 128 (4), pp. 380-391; Ching, J., Chen, Y.-C., Transitional Markov Chain Monte Carlo method for bayesian model updating, model class selection, and model averaging (2007) J Eng Mech, 133 (7), pp. 816-832; Cheung, S.H., Beck, J.L., Bayesian model updating using hybrid Monte Carlo simulation with application to structural dynamic models with many uncertain parameters (2009) J Eng Mech, 135 (4), pp. 243-255; Duane, S., Kennedy, A.D., Pendleton, B.J., Roweth, D., Hybrid Monte Carlo (1987) Phys Lett B, 195 (2), pp. 216-222; Wang, Z., Broccardo, M., Song, J., Hamiltonian Monte Carlo methods for subset simulation in reliability analysis (2019) Struct Saf, 76, pp. 51-67; Baisthakur, S., Chakraborty, A., Modified Hamiltonian Monte Carlo-based Bayesian finite element model updating of steel truss bridge (2020) Struct Control Health Monit, 27 (8); Hasançebi, O., Dumlupınar, T., Detailed load rating analyses of bridge populations using nonlinear finite element models and artificial neural networks (2013) Comput Struct, 128, pp. 48-63; Alipour, M., Harris, D.K., Barnes, L.E., Ozbulut, O.E., Carroll, J., Load-capacity rating of bridge populations through machine learning: application of decision trees and random forests (2017) J Bridge Eng, 22 (10), p. 04017076; Seo, J., Czaplewski, T.M., Kimn, J.-H., Hatfield, G., Integrated structural health monitoring system and multi-regression models for determining load ratings for complex steel bridges (2015) Measurement, 75, pp. 308-319; Kim, Y.J., Queiroz, L.B., Big data for condition evaluation of constructed bridges (2017) Eng Struct, 141, pp. 217-227; Seo, J., Phares, B., Lu, P., Wipf, T., Dahlberg, J., Bridge rating protocol using ambient trucks through structural health monitoring system (2013) Eng Struct, 46, pp. 569-580; Akgül, F., Frangopol, D.M., Time-dependent interaction between load rating and reliability of deteriorating bridges (2004) Eng Struct, 26 (12), pp. 1751-1765; Akgül, F., Frangopol, D.M., Bridge rating and reliability correlation: comprehensive study for different bridge types (2004) J Struct Eng, 130 (7), pp. 1063-1074; Wang, N., Ellingwood, B.R., Zureick, A.-H., Bridge rating using system reliability assessment. II: improvements to bridge rating practices (2011) J Bridge Eng, 16 (6), pp. 863-871; Alampalli, S., Frangopol, D.M., Grimson, J., Halling, M.W., Kosnik, D.E., Lantsoght, E.O., Yang, D., Zhou, Y.E., Bridge load testing: state-of-the-practice (2021) J Bridge Eng, 26 (3), p. 03120002; Alampalli, S., Frangopol, D.M., Grimson, J., Kosnik, D., Halling, M., Lantsoght, E.O., Weidner, J.S., Zhou, Y.E., (2019) Primer on Bridge Load Testing, , . 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Highway Research Board Bulletin, Technical Report No, p. 124. , Massachusetts Institute of Technology, Cambridge, USA; Wright, D.T., Green, R., (1964) Highway bridge vibrations: part II, Ontario Test Programme, , Queen’s University, Belfast; Cantieni, R., (1983) Dynamic load tests on highway bridges in Switzerland, 60 years of experience of empa, report n. 211, , EMPA; Sanayei, M., Reiff, A.J., Brenner, B.R., Imbaro, G.R., Load rating of a fully instrumented bridge: comparison of lrfr approaches (2016) J Perform Constr Facil, 30 (2), p. 04015019","Chakraborty, A.; Civil Engineering Department, India; email: arunasis@iitg.ac.in",,,"Springer Science and Business Media Deutschland GmbH",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Final","",Scopus,2-s2.0-85108359338
"Benbara N., Rebillat M., Mechbal N.","57218454843;35093066100;55946633000;","Bending waves focusing in arbitrary shaped plate-like structures: Application to spatial audio in cars",2020,"Journal of Sound and Vibration","487",,"115587","","",,4,"10.1016/j.jsv.2020.115587","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089246544&doi=10.1016%2fj.jsv.2020.115587&partnerID=40&md5=c711075a68bc1b8c857d2eeddc86ae55","Processes and Engineering in Mechanics and Materials laboratory (PIMM) ENSAM, CNRS, CNAM, HESAM Université, 151 Boulevard de l'Hôpital, Paris, 75013, France","Benbara, N., Processes and Engineering in Mechanics and Materials laboratory (PIMM) ENSAM, CNRS, CNAM, HESAM Université, 151 Boulevard de l'Hôpital, Paris, 75013, France; Rebillat, M., Processes and Engineering in Mechanics and Materials laboratory (PIMM) ENSAM, CNRS, CNAM, HESAM Université, 151 Boulevard de l'Hôpital, Paris, 75013, France; Mechbal, N., Processes and Engineering in Mechanics and Materials laboratory (PIMM) ENSAM, CNRS, CNAM, HESAM Université, 151 Boulevard de l'Hôpital, Paris, 75013, France","Advanced automotive audio applications are more and more demanding with respect to the visual impact of loudspeakers while still requiring more and more channels for high quality spatial sound rendering. The use of arbitrary plate-like structures driven by electromagnetic actuators or by piezoelectric elements appears as a promising solution to tackle both issues. However, to meet spatial rendering audio constraints (i.e. to be as close as possible to omnidirectional piston-like sources), the generated bending waves must be focused at a given position and to a certain extent within the host plate which can be of arbitrary shape, material, and thickness. Theoretically, this means being able to invert the spatio-temporal wave propagation operator for the generated bending waves to fit a given target shape. There are several methods (modal control, time-reversal, and propagating waves operator inversion) that allow to focus bending waves in a media. However, there is scarce work on their adaption and performances assessment in the context of audio applications. These methods depend differently on the available knowledge of wave propagation in the plate (theoretical, partial spatial or full spatial knowledge) and are here investigated to perform this task. Their performances are assessed with respect to several aspects: geometrical complexity, thickness, and material damping of the host structure, number and type of actuators, position and extent of the focusing area. The various methods are presented in a unified theoretical framework and they are compared by means of two key performance indexes (focus localization error and spatial contrast). An experimental validation on a relevant industrial case is also carried out and learning through a digital twin instead of time consuming experimental data investigated. This work falls within the framework of research which tries to bridge the gap between laboratory research and industrial deployment of this kind of technologies. © 2020","Advanced signal processing; Bending wave focusing; Digital twin; Inverse problems; Multifunctional materials; Spatial vibration control","Audio acoustics; Audio systems; Digital twin; Industrial research; Loudspeakers; Piezoelectric actuators; Wave propagation; Electromagnetic actuators; Experimental validations; Geometrical complexity; Industrial deployment; Performances assessment; Piezoelectric elements; Propagation operators; Theoretical framework; Plates (structural components)",,,,,"Agence Nationale de la Recherche, ANR: ANR-17-CE33-0004; Commissariat à l'Énergie Atomique et aux Énergies Alternatives, CEA","This work was financially supported by the French National Research Agency (ANR, contract ANR-17-CE33-0004). The authors wish to thank Christian Bolzmacher from CEA and Jean-Christophe Chamard from PSA for their help in providing the car and some hardware for experiments.","This work was financially supported by the French National Research Agency (ANR, contract ANR-17-CE33-0004 ). The authors wish to thank Christian Bolzmacher from CEA and Jean-Christophe Chamard from PSA for their help in providing the car and some hardware for experiments.",,,,,,,,,"Berkhout, A.J., de Vries, D., Vogel, P., Acoustic control by wave field synthesis (1993) J. Acoust. Soc. Am., 93 (5), pp. 2764-2778; Rabenstein, R., Spors, S., Spatial aliasing artifacts produced by linear and circular loudspeaker arrays used for wave field synthesis (2006) Audio Engineering Society Convention 120, , http://www.aes.org/e-lib/browse.cfm?elib=13515; Harris, N., Hawksford, M.J., The distributed-mode loudspeaker (DML) as a broad-band acoustic radiator (1997) Audio Engineering Society Convention 103, , http://www.aes.org/e-lib/browse.cfm?elib=7253; Boone, M.M., de Bruijn, W.P.J., On the applicability of distributed mode loudspeaker panels for wave field synthesis-based sound reproduction (2000) Audio Engineering Society Convention 108, , http://www.aes.org/e-lib/browse.cfm?elib=9173; Horbach, U., de Vries, D., Corteel, E., Spatial audio reproduction using distributed mode loudspeaker arrays (2002) Audio Engineering Society Conference: 21st International Conference: Architectural Acoustics and Sound Reinforcement, , http://www.aes.org/e-lib/browse.cfm?elib=11196; Kuster, M., De Vries, D., Beer, D., Brix, S., Structural and acoustic analysis of multiactuator panels (2006) J. Audio Eng. Soc, 54 (11), pp. 1065-1076. , http://www.aes.org/e-lib/browse.cfm?elib=13887; Rébillat, M., (2011) Vibrations of large multi-actuator panels for the creation of audio-visual virtual environments: acoustical, mechanical and perceptual approaches, , https://pastel.archives-ouvertes.fr/pastel-00657634, Ecole Polytechnique X Theses; Escolano, J., Lpez, J.J., Pueo, B., Ramos, G., On large multiactuator panels for wave field synthesis applications (2008) Audio Engineering Society Convention 124, , http://www.aes.org/e-lib/browse.cfm?elib=14584; Pueo, B., Lpez, J.J., Escolano, J., Hȵrchens, L., Multiactuator panels for wave field synthesis: Evolution and present developments (2011) J. Audio Eng. Soc, 58 (12), pp. 1045-1063. , http://www.aes.org/e-lib/browse.cfm?elib=15745; Preumont, A., Vibration Control of Active Structures: An Introduction (2011) Solid Mechanics and Its Applications, , https://books.google.fr/books?id=MUQUQyB4bEUC, Springer Netherlands; Enferad, E., giraud audine, C., FrȨdȨric, G., Amberg, M., Semail, B., Generating controlled localized stimulations on haptic displays by modal superimposition (2019) J. Sound Vib., 449; Woo, J.-H., Ih, J.-G., Vibration rendering on a thin plate with actuator array at the periphery (2015) J. Sound. Vib., 349, pp. 150-162; Heilemann, M.C., Anderson, D., Bocko, M.F., Sound-source localization on flat-panel loudspeakers (2017) J. Audio Eng. Soc, 65 (3), pp. 168-177. , http://www.aes.org/e-lib/browse.cfm?elib=18552; Fink, M., Time reversal of ultrasonic fields. i. basic principles (1992) IEEE Trans. Ultrason. Ferroelectr. Freq. 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Am., 110 (1), pp. 37-47; Aubry, J.-F., Tanter, M., Gerber, J., Thomas, J.-L., Fink, M., Optimal focusing by spatio-temporal inverse filter. ii. experiments. application to focusing through absorbing and reverberating media (2001) J. Acoust. Soc. Am., 110 (1), pp. 48-58; Yon, S., Tanter, M., Fink, M., Sound focusing in rooms. ii. the spatio-temporal inverse filter (2003) J. Acoust. Soc. Am., 114 (6), pp. 3044-3052; Woo, J.-H., Ih, J.-G., Park, Y., Comparison of two vibro-acoustic inverse methods to radiate a uniform sound field from a plate (2019) J. Sound Vib., 458, pp. 445-457; Woo, J., Ih, J., Generation of a virtual speaker and baffle on a thin plate controlled by an actuator array at the boundary (2019) IEEE/ASME Trans. Mechatron., 24 (3), pp. 1197-1207; , pp. 1995-2019. , https://www.sdtools.com/, Structural Dynamics Toolbox (for use with MALTAB)], SDTools, Paris, France, Sep","Benbara, N.; Processes and Engineering in Mechanics and Materials laboratory (PIMM) ENSAM, France; email: nassim.benbara@ensam.eu",,,"Academic Press",,,,,0022460X,,JSVIA,,"English","J Sound Vib",Article,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85089246544
"Hu Z., Lou S., Xing Y., Wang X., Cao D., Lv C.","57198788271;57838435000;57194045082;57005000100;12785706100;36860537400;","Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles",2022,"IEEE Transactions on Intelligent Vehicles","7","3",,"417","440",,3,"10.1109/TIV.2022.3195635","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135736888&doi=10.1109%2fTIV.2022.3195635&partnerID=40&md5=1b15aa5de45613a65cbcd6f09974aa4e","Nanyang Technological University, School of Mechanical and Aerospace Engineering, Singapore, 637460, Singapore; Cranfield University, Centre for Autonomous and Cyber-Physical Systems, Cranfield, MK430AL, United Kingdom; Chinese Academy of Sciences, Qingdao Academy of Intelligent Industries, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Beijing, 100190, China; Tsinghua University, School of Vehicle and Mobility, Beijing, 100084, China","Hu, Z., Nanyang Technological University, School of Mechanical and Aerospace Engineering, Singapore, 637460, Singapore; Lou, S., Nanyang Technological University, School of Mechanical and Aerospace Engineering, Singapore, 637460, Singapore; Xing, Y., Cranfield University, Centre for Autonomous and Cyber-Physical Systems, Cranfield, MK430AL, United Kingdom; Wang, X., Chinese Academy of Sciences, Qingdao Academy of Intelligent Industries, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Beijing, 100190, China; Cao, D., Tsinghua University, School of Vehicle and Mobility, Beijing, 100084, China; Lv, C., Nanyang Technological University, School of Mechanical and Aerospace Engineering, Singapore, 637460, Singapore","Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system © 2016 IEEE.","cyber-physical systems; Driver digital twin; human-centric deisgn; human-machine interactions; intelligent vehicles","Autonomous vehicles; Behavioral research; Data structures; Embedded systems; Intelligent vehicle highway systems; Automated vehicles; Autonomous Vehicles; Behavioral science; Cybe-physical systems; Cyber-physical systems; Driver digital twin; Human machine interaction; Human-centric; Human-centric deisgn; Intelligent driving systems; Cyber Physical System",,,,,,,,,,,,,,,,"Grieves, M., Digital twin: Manufacturing excellence through virtual factory replication (2014) Florida Inst. Technol., 1, pp. 1-7. , Melbourne, FL, USA, White Paper; Tuegel, E.J., Ingraffea, A.R., Eason, T.G., Spottswood, S.M., Reengineering aircraft structural life prediction using a digital twin (2011) Int. J. Aerosp. 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Automatica Sinica, 7 (6), pp. 1489-1497. , Nov; Ghahramani, M., Zhou, M., Wang, G., Urban sensing based on mobile phone data: Approaches, applications, and challenges (2020) IEEE/CAA J. Automatica Sinica, 7 (3), pp. 627-637. , May; Nie, J., Yan, J., Yin, H., Ren, L., Meng, Q., A multimodality fusion deep neural network and safety test strategy for intelligent vehicles (2021) IEEE Trans. Intell. Veh., 6 (2), pp. 310-322. , Jun; Xu, C., Wu, H., Liu, H., Gu, W., Li, Y., Cao, D., Blockchain-oriented privacy protection of sensitive data in the internet of vehicles (2022) IEEE Trans. Intell. Veh., Early Access, , Apr. 6; Hasan, M., Mohan, S., Shimizu, T., Lu, H., Securing vehicle-toeverything (V2X) communication platforms (2020) IEEE Trans. Intell. Veh., 5 (4), pp. 693-713. , Dec","Lv, C.; Nanyang Technological University, Singapore; email: lyuchen@ntu.edu.sg",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23798858,,,,"English","IEEE Trans. Intell. Veh.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85135736888
"Li J., Zhang Y., Qian C.","57210390858;8305738300;57002246000;","The enhanced resource modeling and real-time transmission technologies for Digital Twin based on QoS considerations",2022,"Robotics and Computer-Integrated Manufacturing","75",,"102284","","",,3,"10.1016/j.rcim.2021.102284","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122518647&doi=10.1016%2fj.rcim.2021.102284&partnerID=40&md5=e9d2a188de070594ed876bb1e825df3c","School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, China","Li, J., School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, China; Zhang, Y., School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, China; Qian, C., School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, China","Digital Twin (DT) bridges the physical and virtual worlds and provides technical support to the virtualization of the real production scene. However, with the ubiquitous upgrading of the industrial infrastructure, DT systems present an independent and self-contained pattern that causes repeated constructions, stove-piped projects, and cumbersome communication. Based on the Quality of Service (QoS) considerations, this paper proposes an enabling technology framework combining semantic resource modeling with real-time industrial object transmission to improve the performance of DT and overcome these challenges. Firstly, an enhanced modeling methodology including the industrial object meta-model and the industrial object model is designed. Heterogeneous devices are modeled comprehensively, ranging from states, functions, events, interoperability, and flexibility. By formulating the unified specification to achieve standardization and generalization, workers can customize and reuse the models flexibly according to the volatile working environment. Secondly, a real-time industrial object transmission mechanism ensures high fidelity to the physical world and provides unified interfaces for value-added services. While realizing these primary communication functions in real-time, QoS factors and other advanced performances (i.e., reliability, adaptability, robustness) are fulfilled by introducing a decoupling model, a dual-channel network, and a backup strategy. Under the orchestration of all these works, a robust DT system could be developed readily. Finally, a case study is presented to verify the effectiveness of the proposed framework. The results show that interoperability, scalability, microsecond latency, and other strict requirements are realized. © 2021","Data Transmission; Digital Twin; Industrial Object Model; Manufacturing System; QoS","Data transfer; Interoperability; Manufacture; Semantics; Virtual reality; Data-transmission; Industrial object model; Modelling time; Object modeling; Performance; Physical world; Quality-of-service; Real- time; Real-time transmissions; Resource modelling; Quality of service",,,,,"National Natural Science Foundation of China, NSFC: U2001201","The authors would like to acknowledge the financial support of the Key Project of National Natural Science Foundation of China (U2001201). The authors sincerely thank the editors and reviewers for their selfless suggestions to this paper. The device manufacturer also deserves our gratitude, and this research could not be finished without their workshop and devices.",,,,,,,,,,"Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Nee, A.Y.C., Digital twin-driven product design framework (2019) Int. J. Prod. Res., 57, pp. 3935-3953; Zhang, G., Wang, G., Chen, C.-H., Cao, X., Zhang, Y., Zheng, P., Augmented Lagrangian coordination for energy-optimal allocation of smart manufacturing services (2021) Robot. Comput. Integr. Manuf., 71; Ma, S., Zhang, Y., Liu, Y., Yang, H., Lv, J., Ren, S., Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries (2020) J. Clean. Prod., 274; Wang, W., Zhang, Y., Zhong, R.Y., A proactive material handling method for CPS enabled shop-floor (2020) Robot. Comput. Integr. 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"Casini M.","57190936970;","Extended Reality for Smart Building Operation and Maintenance: A Review",2022,"Energies","15","10","3785","","",,3,"10.3390/en15103785","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130580934&doi=10.3390%2fen15103785&partnerID=40&md5=75dfd6edb20d6469f99ca032dfb676dc","Department of Planning, Design, and Technology of Architecture (PDTA), Sapienza University of Rome, Roma, 00185, Italy","Casini, M., Department of Planning, Design, and Technology of Architecture (PDTA), Sapienza University of Rome, Roma, 00185, Italy","The operation and maintenance (O&M) of buildings and infrastructure represent a strategic activity to ensure they perform as expected over time and to reduce energy consumption and maintenance costs at the urban and building scale. With the increasing diffusion of BIM, IoT devices, and AI, the future of O&M is represented by digital twin technology. To effectively take advantage of this digital revolution, thus enabling data‐driven energy control, proactive maintenance, and predictive daily operations, it is vital that smart building management exploits the opportunities offered by the extended reality (XR) technologies. Nevertheless, in consideration of the novelty of XR in the AECO sector and its rapid and ongoing evolution, knowledge of the specific possibilities and the methods of integration into the building process workflow is still piecemeal and sparse. With the goal to bridge this gap, the article presents a thorough review of virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies and applications for smart building operation and maintenance. After defining VR, AR, and MR, the article provides a detailed review that analyzes, categorizes, and summarizes state‐of‐the‐art XR technologies and their possible applications for building O&M along with their relative advantages and disadvantages. The article concludes that the application of XR in building and city management is showing promising results in enhancing human performance in technical O&M tasks, in understanding and controlling the energy efficiency, comfort, and safety of building and infrastructures, and in supporting strategic decision making for the future smart city. © 2022 by the author. Licensee MDPI, Basel, Switzerland.","augmented reality; building operation and maintenance; digital twins; extended reality; immersive technologies; metaverse; mixed reality; virtual reality","Architectural design; Bridges; Decision making; Digital devices; Energy efficiency; Energy utilization; Historic preservation; Intelligent buildings; Maintenance; Mixed reality; Building maintenance; Building management; Building operations; Extended reality; Immersive technologies; Metaverses; Mixed reality; Operations and maintenance; Reduce energy consumption; Strategic activities; Augmented reality",,,,,,,,,,,,,,,,"Casini, M., (2022) Construction 4.0. 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"Gowda V.K., Rosén T., Roth S.V., Söderberg L.D., Lundell F.","57214971495;49561784300;7402433421;7006259311;6603287980;","Nanofibril Alignment during Assembly Revealed by an X-ray Scattering-Based Digital Twin",2022,"ACS Nano","16","2",,"2120","2132",,3,"10.1021/acsnano.1c07769","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124313849&doi=10.1021%2facsnano.1c07769&partnerID=40&md5=57251a2ec6f30ab9a02626eaa378f27a","Department of Engineering Mechanics, Royal Institute of Technology, Stockholm, 100 44, Sweden; FLOW, Royal Institute of Technology, Stockholm, 100 44, Sweden; Treesearch, Royal Institute of Technology, Stockholm, 100 44, Sweden; Wallenberg Wood Science Center, Royal Institute of Technology, Stockholm, 100 44, Sweden; Department of Fibre and Polymer Technology, Royal Institute of Technology, Stockholm, 100 44, Sweden; Deutches Elektronen-Synchrotron DESY, Hamburg, 22607, Germany","Gowda, V.K., Department of Engineering Mechanics, Royal Institute of Technology, Stockholm, 100 44, Sweden, FLOW, Royal Institute of Technology, Stockholm, 100 44, Sweden; Rosén, T., Treesearch, Royal Institute of Technology, Stockholm, 100 44, Sweden, Wallenberg Wood Science Center, Royal Institute of Technology, Stockholm, 100 44, Sweden, Department of Fibre and Polymer Technology, Royal Institute of Technology, Stockholm, 100 44, Sweden; Roth, S.V., Treesearch, Royal Institute of Technology, Stockholm, 100 44, Sweden, Wallenberg Wood Science Center, Royal Institute of Technology, Stockholm, 100 44, Sweden, Department of Fibre and Polymer Technology, Royal Institute of Technology, Stockholm, 100 44, Sweden, Deutches Elektronen-Synchrotron DESY, Hamburg, 22607, Germany; Söderberg, L.D., FLOW, Royal Institute of Technology, Stockholm, 100 44, Sweden, Treesearch, Royal Institute of Technology, Stockholm, 100 44, Sweden, Wallenberg Wood Science Center, Royal Institute of Technology, Stockholm, 100 44, Sweden, Department of Fibre and Polymer Technology, Royal Institute of Technology, Stockholm, 100 44, Sweden; Lundell, F., Department of Engineering Mechanics, Royal Institute of Technology, Stockholm, 100 44, Sweden, FLOW, Royal Institute of Technology, Stockholm, 100 44, Sweden, Wallenberg Wood Science Center, Royal Institute of Technology, Stockholm, 100 44, Sweden","The nanostructure, primarily particle orientation, controls mechanical and functional (e.g., mouthfeel, cell compatibility, optical, morphing) properties when macroscopic materials are assembled from nanofibrils. Understanding and controlling the nanostructure is therefore an important key for the continued development of nanotechnology. We merge recent developments in the assembly of biological nanofibrils, X-ray diffraction orientation measurements, and computational fluid dynamics of complex flows. The result is a digital twin, which reveals the complete particle orientation in complex and transient flow situations, in particular the local alignment and spatial variation of the orientation distributions of different length fractions, both along the process and over a specific cross section. The methodology forms a necessary foundation for analysis and optimization of assembly involving anisotropic particles. Furthermore, it provides a bridge between advanced in operandi measurements of nanostructures and phenomena such as transitions between liquid crystal states and in silico studies of particle interactions and agglomeration. © 2022 The Authors. Published by American Chemical Society","alignment; assembly; cellulose nanofibrils; flow-focusing; rotary diffusion; X-ray scattering","Computational fluid dynamics; Liquid crystals; Nanofibers; Nanostructures; Cellulose nanofibrils; Complex flow; Flow focusing; Mechanical; Mouthfeel; Nano-fibrils; Orientation control; Particle orientation; Rotary diffusion; X -ray scattering; X ray scattering; nanomaterial; chemistry; hydrodynamics; radiography; X ray; X ray diffraction; Hydrodynamics; Nanostructures; Radiography; X-Ray Diffraction; X-Rays",,,,,"Svenska Forskningsrådet Formas; Knut och Alice Wallenbergs Stiftelse; Vetenskapsrådet, VR; Energimyndigheten; Wallenberg Wood Science Center, WWSC","The authors are grateful for the experimental assistance during the SAXS measurement from Dr. J. McKenzie, Dr. N. Mittal, and Dr. P. 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Synchrotron Radiat., 19, pp. 647-653","Lundell, F.; Department of Engineering Mechanics, Sweden; email: frlu@kth.se",,,"American Chemical Society",,,,,19360851,,,"35104107","English","ACS Nano",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85124313849
"Zhao H., Tan C., Obrien E.J., Zhang B., Uddin N., Guo H.","55652511100;56144513100;57218648462;57196052582;7003593965;57343858000;","Developing Digital Twins to Characterize Bridge Behavior Using Measurements Taken under Random Traffic",2022,"Journal of Bridge Engineering","27","1","04021101","","",,3,"10.1061/(ASCE)BE.1943-5592.0001814","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119283909&doi=10.1061%2f%28ASCE%29BE.1943-5592.0001814&partnerID=40&md5=e4eecbd322cfaf2c2e1f7686cccb11a3","Key Laboratory for Wind and Bridge Engineering of Hunan Province, College of Civil Engineering, Hunan Univ., Changsha, 410082, China; Dept. of Civil Engineering, Univ. College Dublin, Dublin, D04 V1W8, Ireland; Dept. of Civil, Construction, and Environmental Engineering, Univ. of Alabama at Birmingham, 1075 13th St S, Birmingham, AL  35205, United States","Zhao, H., Key Laboratory for Wind and Bridge Engineering of Hunan Province, College of Civil Engineering, Hunan Univ., Changsha, 410082, China; Tan, C., Key Laboratory for Wind and Bridge Engineering of Hunan Province, College of Civil Engineering, Hunan Univ., Changsha, 410082, China; Obrien, E.J., Dept. of Civil Engineering, Univ. College Dublin, Dublin, D04 V1W8, Ireland; Zhang, B., Key Laboratory for Wind and Bridge Engineering of Hunan Province, College of Civil Engineering, Hunan Univ., Changsha, 410082, China; Uddin, N., Dept. of Civil, Construction, and Environmental Engineering, Univ. of Alabama at Birmingham, 1075 13th St S, Birmingham, AL  35205, United States; Guo, H., Key Laboratory for Wind and Bridge Engineering of Hunan Province, College of Civil Engineering, Hunan Univ., Changsha, 410082, China","This paper presents a method of developing digital twins (DTs) of road bridges directly from field measurements taken under random traffic loading. In a physics-based approach, the full three-dimensional behavior of the bridge is represented using response functions and distribution factors. In contrast to conventional finite-element analysis, this approach focuses on the relationship between the applied loads and the measured responses, given the limitations on the information about the applied loads due to random passing traffic. At the same time, it takes advantage of some key features of bridge traffic loading that are consistent, regardless of the weights of the passing vehicles. The nature of traffic loading is that axles travel from one end of a bridge to the other and the response is a linear combination of axle weights and ordinates of the same influence line function, adjusted for relative axle locations. Small/medium span concrete slab-girder decks are the target structures of the study. The three-dimensional nature of such structures is a particular challenge, especially in the case of multiple vehicle presence. While these bridges are strongly orthotropic, there is a significant degree of load distribution between the girders immediately under the passing vehicle and girders under adjacent lanes. This is addressed using an iterative approach that uses transverse distribution factors. The proposed DT model is verified using both numerical simulation and field tests. © 2021 American Society of Civil Engineers.","Axle weight; Digital twin; Influence line; Multiple presence; Response function; Signal separation; Transverse load distribution","Axles; Concrete slabs; Distribution functions; Electric power plant loads; Highway bridges; Iterative methods; Traffic surveys; Applied loads; Axle weights; Distribution factor; Influence lines; Multiple presence; Response functions; Road bridge; Signal separation; Traffic loading; Transverse load distributions; Vehicles",,,,,"National Natural Science Foundation of China, NSFC: 52008162; China Postdoctoral Science Foundation: 2020M680114; Guangxi Key Research and Development Program: 2019SK2172; Science and Technology Program of Hunan Province: 2020RC2018","This research was supported by the National Natural Science Foundation of China (Grant No. 52008162), the Key Research and Development Program of Hunan Province (Grant No. 2019SK2172), the Science and Technology Innovation Program of Hunan Province (Grant No. 2020RC2018), and the Fellowship of China Postdoctoral Science Foundation (Grant No. 2020M680114).",,,,,,,,,,"(2012) Bridge Design Specifications, , AASHTO. 8th ed. 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Workshop on Structural Health Monitoring 2019, , Stanford, CA: Stanford Univ; Yu, Y., Cai, C., Deng, L., Nothing-on-road bridge weigh-in-motion considering the transverse position of the vehicle (2018) Struct. Infrastruct. Eng., 14 (8), pp. 1108-1122. , https://doi.org/10.1080/15732479.2017.1401095; Zhao, H., Tan, C., Obrien, E.J., Uddin, N., Zhang, B., Wavelet-based optimum identification of vehicle axles using bridge measurements (2020) Appl. Sci., 10 (21), p. 7485. , https://doi.org/10.3390/app10217485; Zhao, H., Uddin, N., O'Brien, E., Shao, X., Zhu, P., Identification of vehicular axle weights with a bridge weigh-in-motion system considering transverse distribution of wheel loads (2014) J. Bridge Eng., 19 (3), pp. 6-10. , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000533; Znidarič, A., Influence of number and quality of weigh-in-motion data on evaluation of load effects on bridges (2017) Ph.D. Thesis, , Univerza v Ljubljani, Fakulteta za gradbeništvo in geodezijo; Znidaric, A., Lavric, I., Kalin, J., The next generation of bridge weigh-in-motion systems (2002) Proc. 3rd Int. Conf. On Weigh-in-Motion, , Ames, IA: Iowa State Univ","Tan, C.; Key Laboratory for Wind and Bridge Engineering of Hunan Province, China; email: tchj0219@hnu.edu.cn",,,"American Society of Civil Engineers (ASCE)",,,,,10840702,,JBENF,,"English","J Bridge Eng",Article,"Final","",Scopus,2-s2.0-85119283909
"Meixedo A., Ribeiro D., Santos J., Calçada R., Todd M.D.","56940709200;24476782300;36810314200;7801603531;7202805915;","Real-Time Unsupervised Detection of Early Damage in Railway Bridges Using Traffic-Induced Responses",2022,"Structural Integrity","21",,,"117","142",,3,"10.1007/978-3-030-81716-9_6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117968122&doi=10.1007%2f978-3-030-81716-9_6&partnerID=40&md5=a29b09b8a94ae4fa9bc32865915f4edb","CONSTRUCT-LESE, Faculty of Engineering, University of Porto, Porto, Portugal; CONSTRUCT-LESE, School of Engineering, Polytechnic of Porto, Porto, Portugal; LNEC, Laboratório Nacional de Engenharia Civil, Lisbon, Portugal; Department of Structural Engineering, University California San Diego, San Diego, CA, United States","Meixedo, A., CONSTRUCT-LESE, Faculty of Engineering, University of Porto, Porto, Portugal; Ribeiro, D., CONSTRUCT-LESE, School of Engineering, Polytechnic of Porto, Porto, Portugal; Santos, J., LNEC, Laboratório Nacional de Engenharia Civil, Lisbon, Portugal; Calçada, R., CONSTRUCT-LESE, Faculty of Engineering, University of Porto, Porto, Portugal; Todd, M.D., Department of Structural Engineering, University California San Diego, San Diego, CA, United States","This chapter addresses unsupervised damage detection in railway bridges by presenting a novel AI-based SHM strategy using traffic-induced dynamic responses. To achieve this goal a hybrid combination of wavelets, PCA, and cluster analysis is implemented. Damage-sensitive features from train-induced dynamic responses are extracted and allow taking advantage not only of the repeatability of the loading, but also, of its large magnitude, thus enhancing sensitivity to small-magnitude structural changes. The effectiveness of the proposed methodology is validated in a long-span bowstring-arch railway bridge with a permanent structural monitoring system installed. A digital twin of the bridge was used, along with experimental values of temperature, noise, trains loadings, and speeds, to realistically simulate baseline and damage scenarios. The methodology proved highly sensitive in detecting early damage, even in case of small stiffness reductions that do not impair structural safety, as well as highly robust to false detections. The ability to identify early damage, imperceptible in the original signals, while avoiding observable changes induced by environmental and operational variations, is achieved by carefully defining the modelling and fusion sequence of the information. A damage detection strategy capable of characterizing multi-sensor data while being sensitive to identify local changes is proposed as a tool for real-time structural assessment of bridges without interfering with the normal service condition. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Artificial intelligence; Damage detection; Data-driven; Railway bridges; Structural health monitoring; Traffic-induced dynamic responses; Unsupervised learning",,,,,,"Horizon 2020 Framework Programme, H2020; Fundação para a Ciência e a Tecnologia, FCT: SFRH/BD/93201/2013; Ministério da Ciência, Tecnologia e Ensino Superior, MCTES; Institute of Research and Development in Structures and Construction","Acknowledgements This work was financially supported by the Portuguese Foundation for Science and Technology (FCT) through the PhD scholarship SFRH/BD/93201/2013. The authors would like to acknowledge the support of the R&D project RISEN through the H2020|ES|MSC— H2020|Excellence Science|Marie Curie programme, the Portuguese Road and Railway Infrastructure Manager (I.P), the Portuguese National Laboratory for Civil Engineering (LNEC), and the Base Funding—UIDB/04708/2020 of the CONSTRUCT—Instituto de I&D em Estruturas e Construções—financed by national funds through the FCT/MCTES (PIDDAC).","This work was financially supported by the Portuguese Foundation for Science and Technology (FCT) through the PhD scholarship SFRH/BD/93201/2013. The authors would like to acknowledge the support of the R&D project RISEN through the H2020|ES|MSC? H2020|Excellence Science|Marie Curie programme, the Portuguese Road and Railway Infrastructure Manager (I.P), the Portuguese National Laboratory for Civil Engineering (LNEC), and the Base Funding?UIDB/04708/2020 of the CONSTRUCT?Instituto de I&D em Estruturas e Constru??es?financed by national funds through the FCT/MCTES (PIDDAC).",,,,,,,,,"Melo, L.R.T., Ribeiro, D., Calçada, R., Bittencourt, T.N., Validation of a vertical train–track– bridge dynamic interaction model based on limited experimental data (2020) Struct Infrastruct Eng, 16 (1), pp. 181-201. , https://doi.org/10.1080/15732479.2019.1605394; Meixedo, A., Ribeiro, D., Calçada, R., Delgado, R., Global and local dynamic effects on a railway viaduct with precast deck. In: Proceedings of the second international conference on railway technology: Research (2014) Development and Maintenance, , https://doi.org/10.4203/ccp.104.77, Civil-Comp Press, Stirlingshire; Rytter, A., Vibrational based inspection of civil engineering structures. Dept (1993) Of Building Technology and Structural Engineering, , Aalborg University, Aalborg; Meixedo A, Alves V, Ribeiro D, Cury A, Calçada R (2016) Damage identification of a railway bridge based on genetic algorithms. In: Maintenance, monitoring, safety, risk and resilience of bridges and bridge networks—proceedings of the 8th international conference on bridge maintenance, safety and management, IABMAS 2016, Foz Do Iguaçu; Brazil; Cury A, Cremona C (2012) Assignment of structural behaviours in long-term monitoring: application to a strengthened railway bridge. Struct Health Monit 11(4):422–441. https://doi. org/10.1177/1475921711434858; Posenato, D., Kripakaran, P., Smith, I.F.C., Methodologies for model-free data interpretation of civil engineering structures (2010) Comput Struct, 88 (7-8), pp. 467-482. , https://doi.org/10.1016/j.com pstruc.2010.01.001; Meixedo, A., Santos, J., Ribeiro, D., Calçada, R., Todd, M., Damage detection in railway bridges using traffic-induced dynamic responses (2021) Eng Struct, 238. , https://doi.org/10. 1016/j.engstruct.2021.112189; Mujica, L.E., Gharibnezhad, F., Rodellar, J., Todd, M., Considering temperature effect on robust principal component analysis orthogonal distance as a damage detector (2020) Struct Health Monit, 19 (3), pp. 781-795. , https://doi.org/10.1177/1475921719861908; Cavadas, F., Smith, I.F.C., Figueiras, J., Damage detection using data-driven methods applied to moving-load responses (2013) Mech Syst Signal Process, 39 (1-2), pp. 409-425. , https://doi.org/10. 1016/j.ymssp.2013.02.019; Santos, J.P., Crémona, C., Orcesi, A.D., Silveira, P., Multivariate statistical analysis for early damage detection (2013) Eng Struct, 56, pp. 273-285. , https://doi.org/10.1016/j.engstruct.2013.05.022; Hu, W.H., Moutinho, C., Caetano, E., Magalhães, F., Cunha, Á., Continuous dynamic monitoring of a lively footbridge for serviceability assessment and damage detection (2012) Mech Syst Signal Process, 33 (November), pp. 38-55. , https://doi.org/10.1016/j.ymssp.2012.05.012; Farrar, C.R., Worden, K., (2013) Structural Health Monitoring: A Machine Learning Perspective, pp. 1-45. , Wiley, New York, pp; De, L.O.R., Omenzetter, P., Damage classification and estimation in experimental structures using time series analysis and pattern recognition (2010) Mech Syst Signal Process, 24 (5), pp. 1556-1569. , https://doi.org/10.1016/j.ymssp.2009.12.008; Gonzalez, I., Karoumi, R., BWIM aided damage detection in bridges using machine learning (2015) J Civ Struct Heal Monit, 5 (5), pp. 715-725. , https://doi.org/10.1007/s13349-015-0137-4; Cardoso, R., Cury, A., Barbosa, F., Automated real-time damage detection strategy using raw dynamic measurements (2019) Eng Struct, 196. , https://doi.org/10.1016/j.engstruct.2019. 109364; Azim R, Gül M (2019) Damage detection of steel girder railway bridges utilizing operational vibration response. Struct Control Health Monit 26(e2447):1–15. https://doi.org/10.1002/stc. 2447; Nie, Z., Lin, J., Li, J., Hao, H., Ma, H., Bridge condition monitoring under moving loads using two sensor measurements (2019) Struct Health Monit, 19 (3), pp. 917-937. , https://doi.org/10.1177/1475921719868930; Farrar, C.R., Doebling, S.W., Nix, D.A., Vibration–based structural damage identification (2001) Philos Trans R Soc London A: Math Phys Eng Sci, 359 (1778), pp. 131-149. , https://doi.org/10. 1098/rsta.2000.0717; Academic Research, A.N.S.Y.S., (2016) Release, 17, p. 1; Meixedo, A., Ribeiro, D., Santos, J., Calçada, R., Todd, M., Progressive numerical model validation of a bowstring-arch railway bridge based on a structural health monitoring system (2021) J Civ Struct Heal Monit, 11 (2), pp. 421-449. , https://doi.org/10.1007/s13349-020-00461-w; Min, X., Santos, L., (2011) Ensaios dinâmicos Da Ponte ferroviária Sobre O Rio Sado Na Variante De alcácer, , Lisboa [Portuguese; Meixedo, A., Gonçalves, A., Calçada, R., Gabriel, J., Fonseca, H., Martins, R., On-line monitoring system for tracks (2016) Exp.At 2015—3rd Experiment International Conference, , https://doi.org/10.1109/EXPAT.2015.7463240, Sao Miguel Island, Azores; Pimentel, R., Ribeiro, D., Matos, L., Mosleh, A., Calçada, R., Bridge weigh-in-motion system for the identification of train loads using fiber-optic technology (2020) Structures, 2021 (30), pp. 1056-1070. , https://doi.org/10.1016/j.istruc.2021.01.070; Ren, W.X., Sun, Z.S., Structural damage identification by using wavelet entropy (2008) Eng Struct, 30, pp. 2840-2849. , https://doi.org/10.1016/j.engstruct.2008.03.013; Cohen, A., Ryan, R.D., Wavelets and multiscale signal processing (1995) Chapman & Hall, , Boundary Row, London; Cantero, D., Ülker-Kaustell, M., Karoumi, R., Time–frequency analysis of railway bridge response in forced vibration (2016) Mech Syst Signal Process, 76-77, pp. 518-530; Ülker-Kaustell, M., Karoumi, R., Influence of non-linear stiffness and damping on the train-bridge resonance of a simply supported railway bridge (2012) Eng Struct, 41, pp. 350-355. , https://doi.org/10.1016/j.engstruct.2012.03.060; Teolis, A., (1998) Computational Signal Processing with Wavelets, , Birkhauser; Ribeiro, D., Leite, J., Meixedo, A., Pinto, N., Calçada, R., Todd, M., Statistical methodologies for removing the operational effects from the dynamic responses of a high-rise telecommunications tower (2021) Struct Control Health Monit, 28 (4), p. e2700. , https://doi.org/10.1002/stc.2700; Yan, A., Kerschen, G., De, B.P., Golinval, J., Structural damage diagnosis under varying environmental conditions—Part I: A linear analysis (2005) Mech Syst Signal Process, 19 (4), pp. 847-864. , https://doi.org/10.1016/j.ymssp.2004.12.002; Jolliffe, I.T., (2002) Principal Component Analysis, pp. 112-147. , 2nd edn. Springer, New York, pp; Hastie, T., Tibshirani, R., Friedman, J., (2011) The Elements of Statistical Learning, Data Mining Inference, and Prediction, pp. 460-462. , 2nd edn. Springer, Stanford, pp; Santos, J., Crémona, C., Calado, L., Real-time damage detection based on pattern recognition (2016) Struct Concrete, 17 (3), pp. 338-354. , https://doi.org/10.1002/suco.201500092","Meixedo, A.; CONSTRUCT-LESE, Portugal; email: ameixedo@fe.up.pt",,,"Springer Science and Business Media Deutschland GmbH",,,,,2522560X,,,,"English","Structur. Integr.",Book Chapter,"Final","All Open Access, Green",Scopus,2-s2.0-85117968122
"Gawade V., Singh V., Guo W.“.","57194425152;57226743277;57217496903;","Leveraging simulated and empirical data-driven insight to supervised-learning for porosity prediction in laser metal deposition",2022,"Journal of Manufacturing Systems","62",,,"875","885",,3,"10.1016/j.jmsy.2021.07.013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112571515&doi=10.1016%2fj.jmsy.2021.07.013&partnerID=40&md5=d01cd5a515d0334fbe3ae9345b0ef2b3","Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, 96 Frelinghuysen Rd, Piscataway, NJ  08854, United States","Gawade, V., Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, 96 Frelinghuysen Rd, Piscataway, NJ  08854, United States; Singh, V., Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, 96 Frelinghuysen Rd, Piscataway, NJ  08854, United States; Guo, W.“., Department of Industrial and Systems Engineering, Rutgers, The State University of New Jersey, 96 Frelinghuysen Rd, Piscataway, NJ  08854, United States","The advent of digital-twin manufacturing in additive manufacturing (AM) is to integrate the physical world of real-time 3D printing with the digital world of a simulated print. This paper contributes to digital-twin manufacturing in laser-based additive manufacturing by combining melt pools’ simulated thermal behavior via finite element analysis (FEA) and melt pools’ empirical thermal behavior via pyrometry-based sensors. Studying the thermal behavior of melt pools based on heat transfer characteristics determines melt pools’ porosity and part quality. FEA uses Godak's moving heat flux to capture the melt pools’ physically bound temperature profile in three dimensions. Simulated data helps to mitigate the influence of measuring errors from real-world data and provides non-observable data such as gradient changes of thermal behavior at the curvature of the 3D melt pool. The pyrometer captures empirical temperature behavior, including uncertainty and randomness introduced to the process. A significant knowledge gap exists when predicting melt pool porosity accurately with theoretical FEA and empirical in situ evidence alone. The gap is bridged by combining the data sources, specifically, feature engineering via functional principal component analysis (empirical data source) and capturing the melt pool's 3-D temperature shape profile via FEA (simulated data source). A hybrid model predicts melt pool porosity by capturing the strengths of prior simulated and posterior in situ empirical data by matching simulated melt pools to real-world empirical melt pools. Moreover, comparing predicted porosity labels with true porosity labels of Ti–6Al–4V thin-wall structure from laser metal deposition verified the proposed interpretable and robust supervised-learning model's validity. This methodology can apply to other materials and part shapes printed under various additive-manufactured printers. © 2021 The Society of Manufacturing Engineers","Additive manufacturing; Machine learning; Porosity prediction","Additives; Deposition; Digital twin; Heat flux; Heat transfer; Lakes; Porosity; Pyrometers; Pyrometry; Supervised learning; Empirical temperatures; Functional principal component analysis; Heat transfer characteristics; Laser metal deposition; Laser-based additive manufacturing; Porosity predictions; Temperature profiles; Thin-wall structures; 3D printers",,,,,"Mississippi State University, MSU; University College London, UCL","The authors would like to thank Dr. Linkan Bian of Mississippi State University, Dr. Yuebin Guo of Rutgers University, and Dr. Yuanchang Liu of University College London for their valuable input to this paper. This work is partially supported by the International Collaborative Research Grant from Rutgers Global at Rutgers, The State University of New Jersey. Conflict of interest: None declared.","The authors would like to thank Dr. Linkan Bian of Mississippi State University, Dr. Yuebin Guo of Rutgers University, and Dr. Yuanchang Liu of University College London for their valuable input to this paper. This work is partially supported by the International Collaborative Research Grant from Rutgers Global at Rutgers, The State University of New Jersey.",,,,,,,,,"Wang, Q., Jiao, W., Zhang, Y., Deep learning-empowered digital twin for visualized weld joint growth monitoring and penetration control (2020) J Manuf Syst, 57, pp. 429-439; Psarommatis, F., A generic methodology and a digital twin for zero defect manufacturing (zdm) performance mapping towards design for zdm (2021) J Manuf Syst, 59, pp. 507-521; Liu, M., Fang, S., Dong, H., Xu, C., Review of digital twin about concepts, technologies, and industrial applications (2020) J Manuf Syst; Zhang, L., Chen, X., Zhou, W., Cheng, T., Chen, L., Guo, Z., Digital twins for additive manufacturing: a state-of-the-art review (2020) Appl Sci, 10 (23), p. 8350; Liu, C., Le Roux, L., Körner, C., Tabaste, O., Lacan, F., Bigot, S., Digital twin-enabled collaborative data management for metal additive manufacturing systems (2020) J Manuf Syst; Yi, L., Gläßner, C., Aurich, J.C., How to integrate additive manufacturing technologies into manufacturing systems successfully: a perspective from the commercial vehicle industry (2019) J Manuf Syst, 53, pp. 195-211; 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"Hu Z., Fang X., Zhang J.","57304840000;11241266900;57276820000;","A digital twin-based framework of manufacturing workshop for marine diesel engine",2021,"International Journal of Advanced Manufacturing Technology","117","11-12",,"3323","3342",,3,"10.1007/s00170-021-07891-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113635604&doi=10.1007%2fs00170-021-07891-w&partnerID=40&md5=1a9456f38c7576b8288214d3a4610b89","Institute of Advanced Manufacturing Technolog, Jiangsu University of Science and Technology, Zhenjiang, 212003, China","Hu, Z., Institute of Advanced Manufacturing Technolog, Jiangsu University of Science and Technology, Zhenjiang, 212003, China; Fang, X., Institute of Advanced Manufacturing Technolog, Jiangsu University of Science and Technology, Zhenjiang, 212003, China; Zhang, J., Institute of Advanced Manufacturing Technolog, Jiangsu University of Science and Technology, Zhenjiang, 212003, China","The research and application of digital twin (DT) technology have brought new technical means to the development of many fields. Many problems still exist in the manufacturing process of large and complex products, and the manufacturing industry seeks new breakthroughs. Research on DT workshops provides new ideas for technological innovation in manufacturing. To improve the production quality and efficiency of marine diesel engines (MDE), in this paper, a DT workshop is constructed for MDE manufacturing. A DT-based application framework is proposed, and based on it, physical workshop, bridge module, and virtual workshop are illustrated in detail. In the process of building the DT workshop, an object-oriented DT modeling method and a virtual and real data-fusion method are proposed. Besides the method of virtual workshop construction, the DT-based service platform, and the data interaction mechanism within the physical-virtual workshop are applied in the case study. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.","Data interaction; Digital twin;; Intelligent manufacturing workshop;; Marine diesel engine;","Data fusion; Diesel engines; Digital twin; Industrial research; Marine engines; Marine industry; Application frameworks; Data fusion methods; Manufacturing industries; Manufacturing process; Marine Diesel Engines; Production quality; Research and application; Technological innovation; Manufacture",,,,,,,,,,,,,,,,"Tao, F., Zhang, M., Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing (2017) IEEE Access, 5, pp. 20418-20427; Brödner, P., Skill based manufacturing vs. “unmanned factory”—which is superior? 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"Wang W.Y., Li P., Lin D., Tang B., Wang J., Guan Q., Ye Q., Dai H., Gao J., Fan X., Kou H., Song H., Zhou F., Ma J., Liu Z.-K., Li J., Liu W.","35728031900;57208017823;57200110154;57661383800;57200019555;57207944687;35847853300;57216851913;57207948513;57688887000;54894142600;9633072300;55630030200;55246686100;56888222100;8724861300;56095527300;","DID Code: A Bridge Connecting the Materials Genome Engineering Database with Inheritable Integrated Intelligent Manufacturing",2020,"Engineering","6","6",,"612","620",,3,"10.1016/j.eng.2020.05.001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085043207&doi=10.1016%2fj.eng.2020.05.001&partnerID=40&md5=f731be6f91914dad0bd2cf08f245980b","State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, China; CAEP Software Center for High Performance Numerical Simulation, Institute of Applied Physics and Computational Mathematics, Beijing, 100088, China; CRRC Tangshan Co., Ltd., Tangshan, 063035, China; Beijing Star Travel Space Technology Co., Ltd., Beijing, 100013, China; Department of Materials Science and Engineering, Pennsylvania State University, University ParkPA  16802, United States","Wang, W.Y., State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, China; Li, P., State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, China; Lin, D., CAEP Software Center for High Performance Numerical Simulation, Institute of Applied Physics and Computational Mathematics, Beijing, 100088, China; Tang, B., State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, China; Wang, J., State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, China; Guan, Q., CRRC Tangshan Co., Ltd., Tangshan, 063035, China; Ye, Q., State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, China; Dai, H., Beijing Star Travel Space Technology Co., Ltd., Beijing, 100013, China; Gao, J., CRRC Tangshan Co., Ltd., Tangshan, 063035, China; Fan, X., State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, China; Kou, H., State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, China; Song, H., CAEP Software Center for High Performance Numerical Simulation, Institute of Applied Physics and Computational Mathematics, Beijing, 100088, China; Zhou, F., State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, China; Ma, J., CRRC Tangshan Co., Ltd., Tangshan, 063035, China; Liu, Z.-K., Department of Materials Science and Engineering, Pennsylvania State University, University ParkPA  16802, United States; Li, J., State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, China; Liu, W., State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi'an, 710072, China","A data identifier (DID) is an essential tag or label in all kinds of databases—particularly those related to integrated computational materials engineering (ICME), inheritable integrated intelligent manufacturing (I3M), and the Industrial Internet of Things. With the guidance and quick acceleration of the development of advanced materials, as envisioned by official documents worldwide, more investigations are required to construct relative numerical standards for material informatics. This work proposes a universal DID format consisting of a set of build chains, which aligns with the classical form of identifier in both international and national standards, such as ISO/IEC 29168-1:2000, GB/T 27766–2011, GA/T 543.2–2011, GM/T 0006–2012, GJB 7365–2011, SL 325–2014, SL 607–2018, WS 363.2–2011, and QX/T 39–2005. Each build chain is made up of capital letters and numbers, with no symbols. Moreover, the total length of each build chain is not restricted, which follows the formation of the Universal Coded Character Set in the international standard of ISO/IEC 10646. Based on these rules, the proposed DID is flexible and convenient for extending and sharing in and between various cloud-based platforms. Accordingly, classical two-dimensional (2D) codes, including the Hanxin Code, Lots Perception Matrix (LP) Code, Quick Response (QR) code, Grid Matrix (GM) code, and Data Matrix (DM) Code, can be constructed and precisely recognized and/or decoded by either smart phones or specific machines. By utilizing these 2D codes as the fingerprints of a set of data linked with its cloud-based platforms, progress and updates in the composition–processing–structure–property–performance workflow process can be tracked spontaneously, paving a path to accelerate the discovery and manufacture of advanced materials and enhance research productivity, performance, and collaboration. © 2020 THE AUTHORS","Data identifier; Database; Digital twin; Integrated computational materials engineering","Bridges; Character sets; Genes; Industrial internet of things (IIoT); ISO Standards; Processing; Smartphones; Cloud based platforms; Computational materials; Genome engineering; Intelligent Manufacturing; International standards; Material Informatics; Research productivity; Two-dimensional (2-D) codes; Codes (symbols)",,,,,"201750463031; Shanghai Jiao Tong University, SJTU; National Key Research and Development Program of China, NKRDPC: 2016YFB0701303, 2016YFB0701304, 2018YFB0703801, 2018YFB0703802","This work was financially supported by the National Key Research and Development Program of China (2018YFB0703801, 2018YFB0703802, 2016YFB0701303, and 2016YFB0701304) and CRRC Tangshan Co. Ltd. (201750463031). Special thanks to Professor Hong Wang at Shanghai Jiao Tong University for the fruitful discussions and the constructive suggestions/comments. William Yi Wang, Peixuan Li, Deye Lin, Bin Tang, Jun Wang, Quanmei Guan, Qian Ye, Haixing Dai, Jun Gao, Xiaoli Fan, Hongchao Kou, Haifeng Song, Feng Zhou, Jijun Ma, Zi-Kui Liu, Jinshan Li, and Weimin Liu declare that they have no conflict of interest or financial conflicts to disclose.","This work was financially supported by the National Key Research and Development Program of China ( 2018YFB0703801 , 2018YFB0703802 , 2016YFB0701303 , and 2016YFB0701304 ) and CRRC Tangshan Co., Ltd. ( 201750463031 ). Special thanks to Professor Hong Wang at Shanghai Jiaotong University for the fruitful discussions and the constructive suggestions/comments.",,,,,,,,,"Zhou, J., Li, P., Zhou, Y., Wang, B., Zang, J., Meng, L., Toward new-generation intelligent manufacturing (2018) Engineering, 4 (1), pp. 11-20; Zhong, R.Y., Xu, X., Klotz, E., Newman, S.T., Intelligent manufacturing in the context of Industry 4.0: a review (2017) Engineering, 3 (5), pp. 616-630; (2017), National Academies of Sciences, Engineering, and Medicine. The Fourth Industrial Revolution: proceedings of a workshop—in brief. 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"Kyvelou P., Buchanan C., Gardner L.","56610306300;56868250600;55421071800;","Numerical simulation and evaluation of the world's first metal additively manufactured bridge",2022,"Structures","42",,,"405","416",,2,"10.1016/j.istruc.2022.06.012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131968333&doi=10.1016%2fj.istruc.2022.06.012&partnerID=40&md5=80bb00a8e324c999f3c1678f4ef56e3c","Department of Civil and Environmental Engineering, Imperial College London, UK, United Kingdom; Data Centric Engineering Programme, The Alan Turing Institute, UK, London, United Kingdom","Kyvelou, P., Department of Civil and Environmental Engineering, Imperial College London, UK, United Kingdom, Data Centric Engineering Programme, The Alan Turing Institute, UK, London, United Kingdom; Buchanan, C., Department of Civil and Environmental Engineering, Imperial College London, UK, United Kingdom, Data Centric Engineering Programme, The Alan Turing Institute, UK, London, United Kingdom; Gardner, L., Department of Civil and Environmental Engineering, Imperial College London, UK, United Kingdom, Data Centric Engineering Programme, The Alan Turing Institute, UK, London, United Kingdom","Recent disruptive technological advances, including wire arc additive manufacturing (WAAM) and the concept of digital twins, have the potential to fundamentally transform the way in which we design, build and manage structures. WAAM is a method of metal 3D printing that is well suited to the price-sensitive construction industry and has been used to manufacture the MX3D bridge – the world's first metal additively manufactured bridge. The intricate geometry, undulating surface finish and particular material properties rendered the bridge outside the scope of any existing structural design standards; hence, physical testing and advanced numerical modelling were carried out for its safety assessment. The key features of the finite element model of the bridge, and its validation against in-situ structural tests, are described herein. Subsequent numerical studies undertaken to verify the structural performance of the bridge under various loading scenarios are presented, while the basis for the development of the smart digital twin of the bridge is also introduced. The presented research provides insight into the use of advanced computational simulations for the verification and ongoing assessment of structures produced using new methods of manufacture. © 2022 Institution of Structural Engineers","Digital twin; Finite element simulation; Metal 3D printing; Numerical modelling",,,,,,"Lloyd's Register Foundation, LRF; Alan Turing Institute, ATI","The research presented in this paper was possible thanks to funding and support from the Data Centric Engineering programme at the Alan Turing Institute (ATI), funded by the Lloyd’s Register Foundation.",,,,,,,,,,"(2016), ABAQUS CAE 2016, Dassault Systèmes Simulia Corp; Afshan, S., Zhao, O., Gardner, L., Standardised material properties for numerical parametric studies of stainless steel structures and buckling curves for tubular columns (2019) J Constr Steel Res, 152, pp. 2-11; Arrayago, I., Real, E., Gardner, L., Description of stress-strain curves for stainless steel alloys (2015) Mater Des, 87, pp. 540-552; Becque, J., Rasmussen, K.J.R., A numerical investigation of local–overall interaction buckling of stainless steel lipped channel columns (2009) J Constr Steel Res, 65 (8-9), pp. 1685-1693; Bevilacqua, M., Bottani, E., Ciarapica, F.E., Costantino, F., (2020), Di Donato, L., Ferraro, A., Mazzuto, G., Monteriù, A., Nardini, G., Ortenzi, M., Paroncini, M., Pirozzi, M., Prist, M., Quatrini, E., Tronci, M. and Vignali, G. 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"Chen B.-Q., Videiro P.M., Guedes Soares C.","56046456500;6506656321;56978160800;","Opportunities and Challenges to Develop Digital Twins for Subsea Pipelines",2022,"Journal of Marine Science and Engineering","10","6","739","","",,2,"10.3390/jmse10060739","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131519514&doi=10.3390%2fjmse10060739&partnerID=40&md5=f1c485c7021b84f071f41e74ccd5e5ac","Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Lisbon, 1049-001, Portugal; Laboratory for Relaibility Analysis of Offshore Structures (LACEO), COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 21941-596, Brazil","Chen, B.-Q., Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Lisbon, 1049-001, Portugal; Videiro, P.M., Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Lisbon, 1049-001, Portugal, Laboratory for Relaibility Analysis of Offshore Structures (LACEO), COPPE, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 21941-596, Brazil; Guedes Soares, C., Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Lisbon, 1049-001, Portugal","A vision of the digital twins of the subsea pipelines is provided in this paper, with a coverage of the current applications and the challenges of the digital twins in the design, construction, service life, and assessments of life extension. Digital twins are described as a paradigm combining multiphysics modelling with data-driven analytics, which are used to mirror the life of its corresponding twin. Realistic virtual models of structural systems are shown to bridge the gap between design and construction and to mirror the real and virtual worlds. Challenges in properly using the new tools and how to create accurate digital twins considering data acquired during the construction phase are discussed. The key opportunities for improved integrity management using the digital twin are data contextualization, standardization, automated anomaly detection, and learning through sharing. The collection, interpretation and sharing of data, and cyber-security are identified as some of the main challenges. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","digital twin; IoT; maintenance; subsea pipeline",,,,,,"Fundação para a Ciência e a Tecnologia, FCT: 02/SAICT/032108/2017, UIDB/UIDP/00134/2020; European Regional Development Fund, ERDF","Funding: This work was developed in the scope of the project “Cementitious cork composites for improved thermal performance of pipelines for ultradeep waters—SUBSEAPIPE”, with reference no. POCI-01-0145-FEDER-031011 funded by European Regional Development Fund (FEDER) through COMPETE2020—Operational Program Competitive-ness and Internationalization (POCI) and with financial support from the Portuguese Foundation for Science and Technology (Fundação para a Ciência e Tecnologia—FCT), under contract 02/SAICT/032108/2017. 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"Karagiannis D., Buchmann R.A., Utz W.","35573472500;16030647900;24766862500;","The OMiLAB Digital Innovation environment: Agile conceptual models to bridge business value with Digital and Physical Twins for Product-Service Systems development",2022,"Computers in Industry","138",,"103631","","",,2,"10.1016/j.compind.2022.103631","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125407746&doi=10.1016%2fj.compind.2022.103631&partnerID=40&md5=0fe01d448a66f5e1ca8d32aeb1352f5e","Research Group Knowledge Engineering, University of Vienna, Währinger Straße 29, Wien, 1090, Austria; Business Informatics Research Center, Faculty of Economics and Business Administration, Babeș-Bolyai University, str. T.H. Mihali 58-60, Cluj-Napoca, 400591, Romania; OMiLAB NPO, Lützowufer 1, Berlin, 10785, Germany","Karagiannis, D., Research Group Knowledge Engineering, University of Vienna, Währinger Straße 29, Wien, 1090, Austria; Buchmann, R.A., Business Informatics Research Center, Faculty of Economics and Business Administration, Babeș-Bolyai University, str. T.H. Mihali 58-60, Cluj-Napoca, 400591, Romania; Utz, W., OMiLAB NPO, Lützowufer 1, Berlin, 10785, Germany","OMiLAB is a community of practice which offers a digital ecosystem bringing together open technologies to investigate and apply conceptual modeling methods for varying purposes and domains. One of the core value propositions is a dedicated Digital Innovation environment comprising several toolkits and workspaces, designed to support Product-Service Systems (PSS) prototyping – a key ingredient for PSS lifecycle management. At the core of this environment is a notion of Agile Digital Twin – a conceptual representation that can be tailored with knowledge engineering means to bridge the semantic and functional gap between a business perspective (focusing on value creation) and an engineering perspective (focusing on cyber-physical proofs-of-concept). To facilitate this bridging, the hereby proposed environment orchestrates, across three abstraction layers, methods such as Design Thinking, Agile Modeling Method Engineering and Model-driven Engineering to turn Ideation into smart Product-Service Systems experiments, in a laboratory setting. The proposed environment was built following Design Science principles. It addresses the problem of historically-disconnected skills required for Digital Innovation projects and it provides a testbed for feasibility experimentation. For design-oriented, artifact building research, a higher Technology Readiness Level can thus be achieved (compared to the level that idea development methods typically attain). © 2022 Elsevier B.V.","Agile modeling method engineering; Digital twin; Domain-specific conceptual modeling; OMiLAB; Physical twin; Smart Product-service Systems","Agile manufacturing systems; Bridges; Life cycle; Product design; Semantics; Agile modeling method engineering; Agile modeling methods; Conceptual model; Domain specific; Domain-specific conceptual modeling; Method engineering; OMiLAB; Physical twin; Product-service systems; Smart product-service system; Smart products; Abstracting",,,,,,"The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The work presented in this paper was supported by the Erasmus + Knowledge Alliance project DIGIFoF (Digital Design Skills for Factories of the Future - Project Nr. 601089-EPP-1–2018–1-RO-EPPKA2-KA) which also supported the realization of the special issue on Digital Technologies to Support Lifecycle Management of Smart Product-Service Solutions. 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Manag.; Zacharewicz, G., Daclin, N., Doumeingts, G., Haidar, H., Model driven interoperability for system engineering (2020) Modelling, 1 (2), pp. 94-121; Zhou, G., Zhang, C., Li, Z., Ding, K., Wang, C., Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing (2020) Int. J. Prod. Res., 58 (4), pp. 1034-1051","Karagiannis, D.; Research Group Knowledge Engineering, Währinger Straße 29, Austria; email: dk@dke.univie.ac.at",,,"Elsevier B.V.",,,,,01663615,,CINUD,,"English","Comput Ind",Article,"Final","",Scopus,2-s2.0-85125407746
"Ghahari F., Malekghaini N., Ebrahimian H., Taciroglu E.","57444742200;57445607700;57112070500;6602889035;","Bridge Digital Twinning Using an Output-Only Bayesian Model Updating Method and Recorded Seismic Measurements",2022,"Sensors","22","3","1278","","",,2,"10.3390/s22031278","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124304449&doi=10.3390%2fs22031278&partnerID=40&md5=a94857cf5d412b0ac40c919eebdda28a","Department of Civil & Environmental Engineering, University of California, Los Angeles, CA  90095, United States; Department of Civil & Environmental Engineering, University of Nevada, Reno, NV  89557, United States","Ghahari, F., Department of Civil & Environmental Engineering, University of California, Los Angeles, CA  90095, United States; Malekghaini, N., Department of Civil & Environmental Engineering, University of Nevada, Reno, NV  89557, United States; Ebrahimian, H., Department of Civil & Environmental Engineering, University of Nevada, Reno, NV  89557, United States; Taciroglu, E., Department of Civil & Environmental Engineering, University of California, Los Angeles, CA  90095, United States","Rapid post-earthquake damage diagnosis of bridges can guide decision-making for emer-gency response management and recovery. This can be facilitated using digital technologies to re-move the barriers of manual post-event inspections. Prior mechanics-based Finite Element (FE) models can be used for post-event response simulation using the measured ground motions at nearby stations; however, the damage assessment outcomes would suffer from uncertainties in structural and soil material properties, input excitations, etc. For instrumented bridges, these uncertainties can be reduced by integrating sensory data with prior models through a model updating approach. This study presents a sequential Bayesian model updating technique, through which a linear/nonlinear FE model, including soil-structure interaction effects, and the foundation input motions are jointly identified from measured acceleration responses. The efficacy of the presented model updating technique is first examined through a numerical verification study. Then, seismic data recorded from the San Rogue Canyon Bridge in California are used for a real-world case study. Comparison between the free-field and the foundation input motions reveals valuable information regarding the soil-structure interaction effects at the bridge site. Moreover, the reasonable agree-ment between the recorded and estimated bridge responses shows the potentials of the presented model updating technique for real-world applications. The described process is a practice of digital twinning and the updated FE model is considered as the digital twin of the bridge and can be used to analyze the bridge and monitor the structural response at element, section, and fiber levels to diagnose the location and severity of any potential damage mechanism. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","Bayesian inference; Damage diagnosis; Digital twin; Finite element model updating; Foundation input motion; Rapid post-earthquake assessment; Soil-structure interaction; Structural health monitoring","Bayesian networks; Damage detection; Decision making; Earthquakes; Finite element method; Inference engines; Soils; Structural health monitoring; Bayesian inference; Bayesian model updating; Damage diagnosis; Finite element modelling (FEM); Finite-element model updating; Foundation input motion; Model updating techniques; Rapid post-earthquake assessment; Soil-structure interaction; Uncertainty; Soil structure interactions; acceleration; adult; article; California; digital twin; earthquake; finite element analysis; human; motion; soil structure; uncertainty",,,,,"1014-963; California Department of Transportation, CT: 65A0450","The work presented in this manuscript was funded, in part, by the California Geological Survey (Contract No. 1014-963) and by the California Department of Transportation (Grant No. 65A0450). 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"Dohale V., Akarte M., Gunasekaran A., Verma P.","57215001940;6506739908;56238759300;57200371157;","Exploring the role of artificial intelligence in building production resilience: learnings from the COVID-19 pandemic",2022,"International Journal of Production Research",,,,"","",,2,"10.1080/00207543.2022.2127961","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139955676&doi=10.1080%2f00207543.2022.2127961&partnerID=40&md5=5acf0cfcffb1f5422201be6ba7c66cff","Goldratt Consulting, India; Department of Operations and Supply Chain Management (O&SCM), National Institute of Industrial Engineering (NITIE), Mumbai, India; School of Business Administration, Middletown, PA, United States","Dohale, V., Goldratt Consulting, India; Akarte, M., Department of Operations and Supply Chain Management (O&SCM), National Institute of Industrial Engineering (NITIE), Mumbai, India; Gunasekaran, A., School of Business Administration, Middletown, PA, United States; Verma, P., Department of Operations and Supply Chain Management (O&SCM), National Institute of Industrial Engineering (NITIE), Mumbai, India","The ever-happening disruptive events interrupt the operationalisation of manufacturing organisations resulting in stalling the production flow and depleting societies with products. Advancements in cutting-edge technologies, viz. blockchain, artificial intelligence, virtual reality, digital twin, etc. have attracted the practitioners’ attention to overcome such saddled conditions. This study attempts to explore the role of artificial intelligence (AI) in building the resilience of production function at manufacturing organisations during a COVID-19 pandemic. In this regard, a decision support system comprising an integrated voting analytical hierarchy process (VAHP) and Bayesian network (BN) method is developed. Initially, through a comprehensive literature review, the critical success factors (CSFs) for implementing AI are determined. Further, using a multi-criteria decision-making (MCDM) based VAHP, CSFs are prioritised to determine the prominent ones. Finally, the machine learning based BN method is adopted to predict and understand the influential CSFs that help achieve the highest production resilience. The present research is one of the early attempts to know the essence of AI and bridge the interplay between AI and production resilience during COVID-19. This study can support academicians, practitioners, and decision-makers in assessing the AI adoption in manufacturing organisations and evaluate the impact of different CSFs of AI on production resilience. © 2022 Informa UK Limited, trading as Taylor & Francis Group.","artificial intelligence; Bayesian network; manufacturing strategy; production competence; production resilience; voting AHP","Barium compounds; COVID-19; Decision making; Decision support systems; Knowledge based systems; Manufacture; Uncertainty analysis; Virtual reality; Analytical Hierarchy Process; Bayesia n networks; Bayesian network methods; In-buildings; Manufacturing organizations; Manufacturing strategy; Production competence; Production resilience; Success factors; Voting AHP; Bayesian networks",,,,,"National Institute of Industrial Engineering, NITIE","The authors express their sincere gratitude to the anonymous reviewers, guest editors, and editor-in-chief for their valuable comments on improving this manuscript. The authors would like to acknowledge the support provided by Digital Manufacturing Lab (DML), National Institute of Industrial Engineering (NITIE), Mumbai, for providing suitable facilities to conduct this work. Finally, the authors are immensely grateful to the experts for participating in this study.",,,,,,,,,,"(2018), https://resources.agenarisk.com/download/archive/AgenaRisk10DesktopUserManual.pdf, AGENARISK 10 Desktop User Manual; Alexander, H., (2020), https://www.smh.com.au/national/nsw/every-hand-sanitiser-maker-on-earth-is-fighting-for-the-same-materials-20200414-p54jpu.html, Every Hand Sanitiser Maker on Earth Is Fighting for the Same Materials. 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CBS News","Dohale, V.; Department of Operations and Supply Chain Management (O&SCM), Powai, India; email: Vishwas.Dohale.2017@nitie.ac.in",,,"Taylor and Francis Ltd.",,,,,00207543,,IJPRB,,"English","Int J Prod Res",Article,"Article in Press","",Scopus,2-s2.0-85139955676
"Futai M.M., Bittencourt T.N., Santos R.R., Araújo C.R.R., Ribeiro D.M., Rocha A.R., Ellis R.","12142761800;6603036318;57224158372;57388497300;25930078000;57543974800;57651684700;","Utilization of Digital Twins for Bridge Inspection, Monitoring and Maintenance",2022,"Lecture Notes in Civil Engineering","200 LNCE",,,"166","173",,2,"10.1007/978-3-030-91877-4_20","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121932756&doi=10.1007%2f978-3-030-91877-4_20&partnerID=40&md5=28443052ae1cce6b4e1feddd9c45fdbc","University of São Paulo, SP, São Paulo, 055089000, Brazil; PhDsoft, Houston, TX  77025, United States","Futai, M.M., University of São Paulo, SP, São Paulo, 055089000, Brazil; Bittencourt, T.N., University of São Paulo, SP, São Paulo, 055089000, Brazil; Santos, R.R., University of São Paulo, SP, São Paulo, 055089000, Brazil; Araújo, C.R.R., University of São Paulo, SP, São Paulo, 055089000, Brazil; Ribeiro, D.M., PhDsoft, Houston, TX  77025, United States; Rocha, A.R., PhDsoft, Houston, TX  77025, United States; Ellis, R., PhDsoft, Houston, TX  77025, United States","New communication and information systems and technologies (ICT - Information and Communication Technologies) have great potential to aggregate new functionalities and services to inspect, monitor, and manage bridges and other infrastructure assets. Digital Transformation can reduce maintenance costs (avoiding unnecessary maintenance events) and improve system availability, reducing operational losses. The use of Big Data Analytics, incorporating Artificial Intelligence and Machine Learning, are innovative solutions that can be introduced. The adoption of Digital Twins, which incorporate all these elements, can lead to a reduction in the total cost, allowing predictive and proactive maintenance. The implementation of any digital twin requires the construction of a 3D model with the most efficient representation of geometry, material properties, internal connections, and boundary conditions, as well as ground localization information. In the particular case of a bridge, the digital twin may incorporate and manage drawings, spreadsheets, documents, and technical reports, regarding its design and construction, as well as regulatory provisions utilized, in an easy and comprehensive way. All the data is stored and managed using Cloud Computing. All the information is provided graphically to the users in real-time, providing important subsidies for the decision-making and prioritization processes involved in the bridge maintenance. Risk-based predictive maintenance helps to determine the optimal time and date when repairing activities should be performed, offering cost savings over routine or preventive maintenance. The development of a Digital Twin for a railway bridge (incorporating the described features) is illustrated in this paper. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Bridges; Digital transformation; Inspection; Management; Monitoring","3D modeling; Artificial intelligence; Availability; Cost reduction; Data Analytics; Information management; Inspection; Preventive maintenance; Bridge inspection; Bridge monitoring; Bridges maintenance; Communication and information systems; Digital transformation; ICT-information and communication technologies; Information systems and technologies; Infrastructure assets; Maintenance cost; Predictive maintenance; Decision making",,,,,"Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP: 2020/02350-2; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq","Acknowledgments. The authors would like to acknowledge CNPq, and FAPESP (grant nº 2020/02350-2) for providing financial support to develop this paper. The work described in this paper has been partially supported by VLI and VALE Railway Companies. The interaction and support provided by PhDsoft are also deeply appreciated.",,,,,,,,,,"Bittencourt, T.N., Futai, M.M., Conceição Neto, A.P., Ribeiro, D.M., Digital transformation of bridges inspection, monitoring and maintenance processes. In: Bridge Maintenance, Safety, Management (2021) Life-Cycle Sustainability and Innovations, , London: Taylor & Francis Group; Glaessgen, E., Stargel, D., The digital twin paradigm for future NASA and U.S. Air Force vehicles (2012) 53Rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, pp. 1-14. , pp, Honolulu: AIAA; Witte, C.C., Ribeiro, D.M., A statistical time dependent degradation curve analysis for marine structures (2012) Struct Saf Reliab, 2, pp. 695-699; Witte, C.C., Ribeiro, D.M., Structural integrity management: Painting predictive control (2012) SPE International Conference and Exhibition Proceedings, pp. 1-5. , pp, Aberdeen: SPE; Goodfellow, I.J., Benfio, Y., Courville, A., (2016) Deep Learning, , MIT Press, Cambridge; Paltrinieri, N., Comfort, L., Reniers, G., Learning about risk: Machine learning for risk assessment (2019) Saf Sci, 118, pp. 475-486; Hinchy, E.P., Carcagno, C., O’Dowd, N.P., McCarthy, C.T., Using finite element analysis to develop a digital twin of a manufacturing bending operation (2020) 53Rd CIRP Conference on Manufacturing Systems, pp. 568-574. , pp, Chicago: Elsevier; Dang, N., Shim, C., Nguyen, D., Bridge assessment for PSC Girder Bridge using Digital Twins Model (2019) Lecture Notes in Civil Engineering, pp. 1241-1246. , pp, Singapore: Springer; Straub, D., (2004) Generic Approaches to Risk Based Inspection Planning for Steel Structures, , Zürich: ETH Zürich; Washer, G., Connor, R., Nasrollahi, M., Reising, R., Verification of the framework for risk-based bridge inspection (2016) J Bridg Eng, 24 (4), pp. 1-11; Faridafshin, F., Anvari, M., Hellevig, N., Risk-based approaches for planning the inspections of large-scale topside structural systems (2019) SPE Offshore Europe Conference and Exhibition, pp. 1-14. , pp, Aberdeen","Futai, M.M.; University of São Paulo, SP, Brazil; email: futai@usp.br","Pellegrino C.Faleschini F.Zanini M.A.Matos J.C.Casas J.R.Strauss A.",,"Springer Science and Business Media Deutschland GmbH","1st Conference of the European Association on Quality Control of Bridges and Structures, EUROSTRUCT 2021","29 August 2021 through 1 September 2021",,269849,23662557,9783030918767,,,"English","Lect. Notes Civ. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85121932756
"Dong Q., He B., Qi Q., Xu G.","55915323400;57214888752;56941419100;7404264994;","Real-time prediction method of fatigue life of bridge crane structure based on digital twin",2021,"Fatigue and Fracture of Engineering Materials and Structures","44","9",,"2280","2306",,2,"10.1111/ffe.13489","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105644141&doi=10.1111%2fffe.13489&partnerID=40&md5=c72a14e988008ee652d8b26986516bdc","School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, China","Dong, Q., School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, China; He, B., School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, China; Qi, Q., School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, China; Xu, G., School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, China","The comprehensive effect of multiple factors that include the geometric characteristics, load status, service characteristics, and failure mechanism will affect the safety of bridge crane structure. To evaluate the security of the bridge crane structure, the real-time prediction method of fatigue life of the bridge structure based on digital twin is proposed. The specific type of general bridge crane is selected as the physical entity of the research object, and the information acquisition system is utilized to get the current service status information about the physical entity. On this basis, combined with the historical service information and inherent information of physical entity, the fuzzy database is established. Meanwhile, twin data are formed by the clear quantification of fuzzy information and data processing technology. In accordance with structural characteristic and work cycle process of bridge crane, the analytical models of load, strength, defect, and fatigue life are established, respectively. The multi-theoretical calculation model is completed by encapsulating the analysis models and transmitting information, and then the main factors affecting the fatigue life of bridge crane structure are determined. With that, the comprehensive evaluation coefficient is calculated by the fuzzy comprehensive evaluation theory. The response relationship between the information data and the fatigue life of bridge crane structure is described by the Kriging surrogate model constructed with experimental design. Real-time prediction of fatigue life of bridge crane structure is realized in a virtual space to depict the life cycle process. Taking QD20/10 t × 43 m × 12 m general bridge crane as an example, the feasibility and applicability of the proposed method are verified, which provides a strong theoretical basis for dependable service and timely scrapping of cranes. © 2021 John Wiley & Sons, Ltd.","bridge structure; digital twin; fatigue life; real-time prediction; surrogate model","Bridge cranes; Bridges; Data handling; Digital twin; Failure (mechanical); Forecasting; Life cycle; Structural design; Comprehensive evaluation; Data processing technologies; Fuzzy comprehensive evaluation; Geometric characteristics; Information acquisition system; Service characteristics; Structural characteristics; Theoretical calculation model; Fatigue of materials",,,,,"201801D221218, 201901D211287; National Natural Science Foundation of China, NSFC: 51805348; Shaanxi University of Science and Technology, SUST: 20172015, 20192019","This paper is based on the extensive investigation and testing results finished by experts and testers from Guangdong Institute of Special Equipment Inspection and Research Zhongshan branch and Weite Technologies Co., Ltd. The author cordially thanks experts and testers for on-site survey and real-time detection. At the same time, this paper is sponsored by the National Natural Science Foundation of China (51805348), the Basic Applied Research Projects in Shanxi Province (201801D221218 and 201901D211287), and PhD Programs of Taiyuan University of Science and Technology (20192019 and 20172015).","This paper is based on the extensive investigation and testing results finished by experts and testers from Guangdong Institute of Special Equipment Inspection and Research Zhongshan branch and Weite Technologies Co., Ltd. The author cordially thanks experts and testers for on‐site survey and real‐time detection. At the same time, this paper is sponsored by the National Natural Science Foundation of China (51805348), the Basic Applied Research Projects in Shanxi Province (201801D221218 and 201901D211287), and PhD Programs of Taiyuan University of Science and Technology (20192019 and 20172015).",,,,,,,,,"Akhtulov, A., Ivanova, L., Kirasirov, O., Kirasirov, M., (2020) Application of the substructure method to assess the vibration state of the bridge crane, , Proceedings of 14th International Conference on Electromechanics and Robotics ‘Zavalishin's Readings’; Jin, Y.F., Inspection for the damaged bridge crane (2015) J Adv Manuf Technol, (6), pp. 313-314; Xu, G.N., (2018) Mechanical Equipment Metal Structure Design 3rd Edition, , Beijing, Chain Machine Press; Li, W., Liu, L., Analysis and repairing of cracks of universal bridge crane with 10t (2020) Energy Saving Nonferr Metall, 201 (2), pp. 63-65; Yao, R., Research on repairing method of main beam crack of bridge crane (2018) Saf Technol Special Equip, (6), pp. 33-34+37; 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Leser, P.E., Warner, J.E., Leser, W.P., Bomarito, G.F., A digital twin feasibility study (Part II): non-deterministic predictions of fatigue life using in-situ diagnostics and prognostics (2020) Eng Fract Mech, 229, pp. 1-16; https://wenku.baidu.com/view/cafdaef5f705cc1755270933.html; http://www.nihonbenyi.com/proshow.asp?id=697; Shen, J., Xiaoning, S., Lidan, L., Risk prediction of construction projects under the “Internet + PPP” model (2017) J Civil Eng Manag, (5), pp. 31-35+49; Ying, Z., Jinwu, Z., Yueqing, B., (2017) Big Data Mining System Method and Case Analysis, , Beijing, Mechanical Industry Press; ISO8686-5: 2017 Crane – Design principles for loads and load combinations – Part 5: Overhead travelling and portal bridges cranes; Dong, Q., Xu, G.N., Ren, H.L., Wang, A.H., Fatigue remaining life estimation for remanufacturing truck crane Jib structure based on random load spectrum (2017) Fatigue Fract Eng Mater Struct, 40 (5), pp. 706-731; ISO 20332: 2016 Crane – Proof of competence of steel structures; Dong, Q., (2017) Risk, Life Assessment and Repairable Decision-making of Mobile Crane Jib Structure in Service, , Taiyuan, Taiyuan University of Science and Technology; ISO8686-1: 2012 Crane – Design principles for loads and load combinations – Part 1: General; BS EN: 13001–2:2014 Crane safety– General design – Part 2: Load actions; Carpinteri, A., Spagnoli, A., Vantadori, S., A multiaxial fatigue criterion for random loading (2003) Fatigue Fract Eng Mater Struct, 26 (6), pp. 515-522; Spagnoli, A., A new high-cycle fatigue criterion applied to out-of-phase biaxial stress state (2001) Int J Mech Sci, 43 (11), pp. 2581-2595; Socie, D.F., Hua, C.T., Mixed model small crack growth (1987) Fatigue Fract Mater Struct, 10 (1), pp. 1-16; Cheng, X., Zhao, S.S., (2006) Fracture Mechanics, , Beijing, Science Press; Gao, H.F., Zio, E., Wang, A., Bai, G.C., Fei, C.W., Probabilistic-based combined high and low cycle fatigue assessment for turbine blades using a substructure-based kriging surrogate model (2020) Aerospace Sci Technol, 104, pp. 1-18; Gaspar, B., Teixeira, A.P., Soares, C.G., Assessment of the efficiency of Kriging surrogate models for structural reliability analysis (2014) Probab Eng Mech, 37, pp. 24-34; Sheikholeslami, R., Razavi, S., Progressive Latin hypercube sampling: an efficient approach for robust sampling-based analysis of environmental models (2017) Environ Model Software, 93 (Jul.), pp. 109-126; Du, Y.W., Wang, S.S., Wang, Y.M., Group fuzzy comprehensive evaluation method under ignorance (2019) Expert Syst Appl, 126, pp. 92-111; Cui, S.M., Wang, R.D., You, X., Liu, Y.J., Wang, Q.Y., On the low cycle fatigue behavior and fatigue life prediction of Q345 steel (2014) J Exper Mech, 29 (5), pp. 537-542; Zhao, S.B., (2015) Fatigue Design Manual, , Beijing, Chain Machine Press; Wang, A.H., Xu, G.N., Gao, Y.S., Random stress spectrum acquisition and fatigue residual life estimate for overhead travelling crane (2012) J Mech Eng, 48 (18), pp. 192-198; Ding, J., Wang, M., Ping, Z., An integrated method based on relevance vector machine for short-term load forecasting (2020) Eur J Oper Res, 287 (2), pp. 497-510; Wadkar, M., Troia, F.D., Stamp, M., Detecting malware evolution using support vector machines (2019) Expert Syst Appl, 143, pp. 1-10; Xu, B., Dan, H.C., Li, L., Temperature prediction model of asphalt pavement in cold regions based on an improved BP neural network (2017) Appl Therm Eng, 120, pp. 568-580; Dong, Q., He, B., Xu, G., Fatigue life evaluation method for foundry crane metal structure considering load dynamic response and crack closure effect (2020) Comput Model Eng Sci, 122 (1), pp. 525-553; Qiang, B.M., Yuan, R.W., Lu, F., Liu, T., Fatigue life estimation of bridge crane main girder based on online monitoring system (2015) Mach Tool Hydraul, 43 (15), pp. 207-210","Dong, Q.; School of Mechanical Engineering, China; email: qingdong@tyust.edu.cn",,,"John Wiley and Sons Inc",,,,,8756758X,,FFESE,,"English","Fatigue Fract Eng Mater Struct",Article,"Final","",Scopus,2-s2.0-85105644141
"Choi J.-S., Kim C.-M., Jang H.-I., Kim E.-J.","57201666011;57221217100;57192402749;55477775700;","Detailed and fast calculation of wall surface temperatures near thermal bridge area",2021,"Case Studies in Thermal Engineering","25",,"100936","","",,2,"10.1016/j.csite.2021.100936","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102854363&doi=10.1016%2fj.csite.2021.100936&partnerID=40&md5=bd7a83781b687622b3750beba4f9b358","Department of Architectural Engineering, Inha University, Incheon, 22212, South Korea; Institute of Green Building and New Technology, Mirae Environment Plan, Seoul, 01905, South Korea","Choi, J.-S., Department of Architectural Engineering, Inha University, Incheon, 22212, South Korea; Kim, C.-M., Institute of Green Building and New Technology, Mirae Environment Plan, Seoul, 01905, South Korea; Jang, H.-I., Institute of Green Building and New Technology, Mirae Environment Plan, Seoul, 01905, South Korea; Kim, E.-J., Department of Architectural Engineering, Inha University, Incheon, 22212, South Korea","The popularity of digital-twin technology has increased the demand for a fast and accurate model for prompt analysis. This work proposes a low-order but accurate numerical model for the thermal analysis of wall-window joint surfaces. A simple method to develop such a model is presented. An ABAQUS-based extraction method was developed to quasi-automatically define a state-space model for the target cases used, which may reduce the amount of elaborate programming work required. Then, an order reduction technique was applied to the state-space model. The results showed that the state-space model obtained from ABAQUS describes almost the same thermal responses as the reference ABAQUS simulation model. After reduction, the proposed model with an order of 10, equivalent to 10 equations, sufficiently described the dynamics of temperature variations, with an error of less than 1%. The model conversion to state-space formulism and reduction technique significantly decreased the CPU time by more than 30,000 times (from 79.5 s to 0.002 s). © 2021 The Authors.","FEM; Numerical model; Reduction technique; State-space model","Digital twin; Thermoanalysis; ABAQUS simulations; Accurate modeling; Extraction method; Order reduction techniques; Reduction techniques; State - space models; Temperature variation; Wall surface temperature; State space methods",,,,,"Ministry of Land, Infrastructure and Transport, MOLIT: 20CTAP-C152248-02; Korea Agency for Infrastructure Technology Advancement, KAIA","This work was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 20CTAP-C152248-02 ).",,,,,,,,,,"Lydon, G.P., Caranovic, S., Hischier, I., Schlueter, A., Coupled simulation of thermally active building systems to support a digital twin (2019) Energy Build., 202, p. 109298; Lu, Q., Xie, X., Parlikad, A.K., Schooling, J.M., Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance (2020) Autom. ConStruct., 118, p. 103277; Cui, P., Yang, H., Fang, Z., Numerical analysis and experimental validation of heat transfer in ground heat exchangers in alternative operation modes (2008) Energy Build., 40 (6), pp. 1060-1066; Kim, D., Braun, J.E., Reduced-order building modeling for application to model-based predictive control (2012) Proc. SimBuild, 5 (1), pp. 554-561; Hudobivnik, B., Pajek, L., Kunic, R., Košir, M., FEM thermal performance analysis of multi-layer external walls during typical summer conditions considering high intensity passive cooling (2016) Appl. Energy, 178, pp. 363-375; Miasik, P., Licholai, L., The influence of a thermal bridge in the corner of the walls on the possibility of water vapour condensation (2018) E3S Web of Conferences, 49, p. 00072. , EDP Sciences; Santos, P., Gonçalves, M., Martins, C., Soares, N., Costa, J.J., Thermal transmittance of lightweight steel framed walls: Experimental versus numerical and analytical approaches (2019) J. Build Eng., 25, p. 100776; Nagy, B., Stocker, G., Numerical analysis of thermal and moisture bridges in insulation filled masonry walls and corner joints (2019) Period. Polytech. Civ. Eng., 63 (2), pp. 446-455; Ascione, F., Bianco, N., De Masi, R.F., Mauro, G.M., Musto, M., Vanoli, G.P., Experimental validation of a numerical code by thin film heat flux sensors for the resolution of thermal bridges in dynamic conditions (2014) Appl. Energy, 124, pp. 213-222; Hibbitt, H., Karlsson, B., Sorensen, P., (2011) Abaqus Analysis User's Manual Version 6.10, , Dassault Systèmes Simulia Corp. Providence, RI, USA; Marshall, S.A., An approximate method for reducing the order of a linear system (1966) Control, 10, pp. 642-643; Gao, Y., Roux, J.J., Teodosiu, C., Zhao, L.H., Reduced linear state model of hollow blocks walls, validation using hot box measurements (2004) Energy Build., 36 (11), pp. 1107-1115; Gao, Y., Roux, J.J., Zhao, L.H., Jiang, Y., Dynamical building simulation: A low order model for thermal bridges losses (2008) Energy Build., 40 (12), pp. 2236-2243; Kim, E.J., Plessis, G., Hubert, J.L., Roux, J.J., Urban energy simulation: Simplification and reduction of building envelope models (2014) Energy Build., 84, pp. 193-202; Moore, B., Principal component analysis in linear systems: Controllability, observability, and model reduction (1981) IEEE Trans. Automat. Contr., 26 (1), pp. 17-32; Laub, A., Heath, M.T., Paige, C., Ward, R., Computation of system balancing transformations and other applications of simultaneous diagonalization algorithms (1987) IEEE Trans. Automat. Contr., 32 (2), pp. 115-122; Kim, E.J., Roux, J.J., Bernier, M.A., Cauret, O., Three-dimensional numerical modeling of vertical ground heat exchangers: Domain decomposition and state model reduction (2011) HVAC R Res., 17 (6), pp. 912-927; Kim, E.J., He, X., Roux, J.J., Johannes, K., Kuznik, F., Is it possible to use a single reduced model for a number of buildings in urban energy simulation (2015) 14th Conference of International Building Performance, , 2015, December; Kim, E.J., He, X., Roux, J.J., Johannes, K., Kuznik, F., Fast and accurate district heating and cooling energy demand and load calculations using reduced-order modelling (2019) Appl. Energy, 238, pp. 963-971","Kim, E.-J.; Department of Architectural Engineering, South Korea; email: ejkim@inha.ac.kr",,,"Elsevier Ltd",,,,,2214157X,,,,"English","Case Stud. Therm. Eng.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85102854363
"de Freitas Bello V.S., Popescu C., Blanksvärd T., Täljsten B., Popescu C.","57338405600;56272949500;20336636900;8703323300;56272949500;","Bridge management systems: Overview and framework for smart management",2021,"IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs",,,,"1014","1022",,2,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119042894&partnerID=40&md5=75c4a853545c7db896e261b5018e91e0","Luleå University of Technology (LTU), Luleå, Sweden; SINTEF Narvik AS, Narvik, 8517, Norway","de Freitas Bello, V.S., Luleå University of Technology (LTU), Luleå, Sweden; Popescu, C., Luleå University of Technology (LTU), Luleå, Sweden, SINTEF Narvik AS, Narvik, 8517, Norway; Blanksvärd, T., Luleå University of Technology (LTU), Luleå, Sweden; Täljsten, B., Luleå University of Technology (LTU), Luleå, Sweden; Popescu, C., Luleå University of Technology (LTU), Luleå, Sweden, SINTEF Narvik AS, Narvik, 8517, Norway","Throughout the world, many medieval and historic bridges remain in operation. Deterioration and failures have increased in the already aging bridges due to consistent growth in traffic volume and axle loads. Therefore, the importance of Bridge Management Systems (BMS) to ensure safety of operation and maximize maintenance investments has also increased. Recent improvements in technology also contribute to the demand for optimized and more resource-efficient BMS. In this study, a literature review was performed to map current bridge management practices and systems in operation in the world. The outcomes identified Bridge Information Modelling (BrIM) and Digital Twins as novel approaches that enable efficient management of the whole lifecycle of a bridge. From these outcomes, a framework of an ideal BMS is proposed to achieve automated and smart management of bridges. © 2021 IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs. All rights reserved.","BMS; Bridge management systems; Bridges; BrIM; Review","Deterioration; Life cycle; Maintenance; Structural design; Axle loads; Bridge information modeling; Bridge management system; Historic bridges; Information Modeling; Maintenance investments; Resource-efficient; Traffic volumes; Volume loads; Bridges",,,,,"Energimyndigheten","This work was carried out within the strategic innovation program InfraSweden2030, a joint venture by Vinnova, Formas and The Swedish Energy Agency, the work is also funded by SBUF (construction industry's organisation for research and development in Sweden) and Skanska Sweden.",,,,,,,,,,"Hurt, M., Schrock, S., Chapter 1 - Introduction (2016) Highway Bridge Maintenance Planning and Scheduling, pp. 1-30; Khan, M. A., (2015) Accelerated Bridge Construction, pp. 53-102. , Boston: Butterworth-Heinemann; Powers, N., Frangopol, D. M., Al-Mahaidi, R., Caprani, C., (2018) Maintenance, safety, risk, management and life-cycle performance of bridges, pp. 219-225. , London: CRC Press; Darbani, B. M., Hammad, A., Critical review of new directions in bridge management systems (2007) Computing in Civil Engineering, pp. 330-337; (2005) Bridge preservation and maintenance in Europe and South Africa, , FHWA; Mirzaei, Z., Adey, B. T., Klatter, L., Thompson, P., (2014) The IABMAS bridge management committee overview of existing bridge management systems; Helmerich, R., Niederleithinger, E., Algernon, D., Streicher, D., Wiggenhauser, H., Bridge inspection and condition assessment in Europe (2008) Transportation Research Record, 2044, pp. 31-38; Thompson, P. D., Small, E. P., Johnson, M., Marshall, A. R., The Pontis Bridge Management System (1998) Structural Engineering International, 8 (4), pp. 303-308; Gholami, M., Sam, A. R. B. M., Yatim, J. M., Assessment of bridge management system in Iran (2013) Procedia Engineering, 54, pp. 573-583; Mendonça, T., Brito, V., Milhazes, F., (2010) Aplicação de Gestão de Obras de Arte - GOA - nova geração; Moscoso, Y. F. M., (2017) Modelos de Degradação para Aplicação em Sistemas de Obras de Arte Especiais - OAEs, , PhD thesis Universidade de Brasília. Faculdade de Tecnologia; Liao, H.-K., Yau, N.-J., Development of various bridge condition indices for Taiwan bridge management system (2011) 28th International Symposium on Automation and Robotics in Construction, pp. 911-916; Safi, M., Sundquist, H., Karoumi, R., Racutanu, G., Development of the Swedish bridge management system by upgrading and expanding the use of LCC (2013) Structure and Infrastructure Engineering, 9 (12), pp. 1240-1250; Woodward, R., Cullington, D. W., Daly, A. F., Vassie, P. R., Haardt, P., Kashner, R., Astudillo, R., Cremona, C., (2001) Bridge management in Europe (BRIME) - Deliverable D14-Final Report; Brady, K. C., O'Reilly, M., Bevc, L., Znidaric, A., O'Brien, E., Jordan, R., Cost 345 - Procedures required for the assessment of highway structures - Final report European Co-operation in the Field of Scientific and Technical Research; Hallberg, D., Racutanu, G., Development of the Swedish bridge management system by introducing a LMS concept (2007) Materials and Structures, 40, pp. 627-639; Isailovic, D., Stojanovic, V., Trapp, M., Richter, R., Hajdin, R., Döllner, J., Bridge damage: Detection, IFC-based semantic enrichment and visualization (2020) Automation in Construction, 112. , (103088); Marzouk, M. M., Hisham, M., Bridge information modelling in sustainable bridge management (2011) Proceedings of the 2011 International Conference on Sustainable Design and Construction - ICSDC 2011: Integrating Sustainability Practices in the Construction Industry, pp. 457-466; Dibernardo, S., Integrated modelling systems for bridge asset management - case study (2012) Proceedings of the 2012 Structures Congress, pp. 483-493; Wan, C., Zhou, Z., Li, S., Ding, Y., Xu, Z., Yang, Z., Xia, Y., Yin, F., Development of a bridge management system based on the building information modelling technology (2019) Sustainability, 11 (4583); Zhao, Z., Gao, Y., Hu, X., Zhou, Y., Zhao, L., Qin, G., Guo, J., Han, D., Integrating BIM and IoT for smart bridge management (2019) IOP Conference Series: Earth and Environmental Science, 371. , (022034); Zhu, J., Tan, Y., Wang, X., Wu, P., BIM/GIS integration for web GIS-based bridge management (2020) Annals of GIS, 27 (1), pp. 99-109; Boddupalli, C., Sadhu, A., Rezazadeh Azar, E., Pattyson, S., Improved visualization of infrastructure monitoring data using building information modelling (2019) Structure and Infrastructure Engineering, 15 (9), pp. 1247-1263; Riveiro, B., Jauregui, D. V., Arias, P., Armesto, J., Jiang, R., An innovative method for remote measurement of minimum vertical underclearance in routine bridge inspection (2012) Automation in Construction, 25, pp. 34-40; Huthwohl, P., Brilakis, I., Borrmann, A., Sacks, R., Integrating RC bridge defect information into BIM models (2018) Journal of Computing in Civil Engineering, 32 (3), p. 04018013. , 1-04018013-14; Lu, R., Brilakis, I., Digital twinning of existing reinforced concrete bridges from labelled point clusters (2019) Automation in Construction, 105. , (102837); Borin, P., Cavazzini, F., Condition assessment of RC bridges - Integrating machine learning, photogrammetry and BIM (2019) International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, pp. 201-208. , XLII-2/W15; Morgenthal, G., Hallermann, N., Kersten, J., Taraben, J., Debus, P., Helmrich, M., Rodehorst, V., Framework for automated UAS-based structural condition assessment of bridges (2019) Automation in Construction, 97, pp. 77-95; Khajavi, S. H., Motlagh, N. H., Jaribion, A., Werner, L. C., Holmstrom, J., Digital twin: Vision, benefits, boundaries, and creation for buildings (2019) IEEE Access, 7, pp. 147406-147419; Zou, Y., Kiviniemi, A., Jones, S. W., Developing a tailored RBS linking to BIM for risk management of bridge projects (2016) Engineering, Construction and Architectural Management, 23 (6), pp. 727-750; Zou, Y., Kiviniemi, A., Jones, S. W., Walsh, J., Risk information management for bridges by integrating risk breakdown structure into 3D/4D BIM (2019) KSCE Journal of Civil Engineering, 23 (2), pp. 467-480","de Freitas Bello, V.S.; Luleå University of Technology (LTU)Sweden; email: vanessa.saback.de.freitas@ltu.se","Snijder H.H.De Pauw B.De Pauw B.van Alphen S.F.C.Mengeot P.","Allplan;et al.;Greisch;Infrabel;Royal HaskoningDHV;TUC RAIL","International Association for Bridge and Structural Engineering (IABSE)","IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs","22 September 2021 through 24 September 2021",,172892,,,,,"English","IABSE Congr., Ghent: Struct. Eng. Future Soc. Needs",Conference Paper,"Final","",Scopus,2-s2.0-85119042894
"Jiang F., Ding Y., Song Y., Geng F., Wang Z.","57204694266;55768944900;55494118800;36637279300;36723167900;","An Architecture of Lifecycle Fatigue Management of Steel Bridges Driven by Digital Twin",2021,"Structural Monitoring and Maintenance","8","2",,"187","201",,2,"10.12989/smm.2021.8.2.187","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108567225&doi=10.12989%2fsmm.2021.8.2.187&partnerID=40&md5=38cc93207aceb3f7548c44ade03cb1e3","Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, No. 2 Sipailou, Xuanwu District, Nanjing, 210096, China; School of Architecture Engineering, Jinling Institute of Technology, No. 99 Hongjing Avenue, Jiangning District, Nanjing, 211169, China; School of Architecture Engineering, Nanjing Institute of Technology, No. 1 Hongjing Avenue, Jiangning District, Nanjing, 211167, China; Shenzhen Express Engineering Consulting Co. Ltd., 268 Meiao 1st Road, Futian District, Shenzhen, 518000, China","Jiang, F., Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, No. 2 Sipailou, Xuanwu District, Nanjing, 210096, China; Ding, Y., Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, No. 2 Sipailou, Xuanwu District, Nanjing, 210096, China; Song, Y., School of Architecture Engineering, Jinling Institute of Technology, No. 99 Hongjing Avenue, Jiangning District, Nanjing, 211169, China; Geng, F., School of Architecture Engineering, Nanjing Institute of Technology, No. 1 Hongjing Avenue, Jiangning District, Nanjing, 211167, China; Wang, Z., Shenzhen Express Engineering Consulting Co. Ltd., 268 Meiao 1st Road, Futian District, Shenzhen, 518000, China","The fatigue of steel bridges poses a great threat to their safety and functionality. However, current approaches for fatigue management are largely based on heuristic design philosophies, physical testing, and bridge managers’ experience. This paper proposes a closed lifecycle fatigue management driven by Digital Twin for steel bridges. To provide clarity around the concept, the definition of Digital Twin for steel bridges is given at first. Then eight functional modules supporting Digital Twin are outlined in detail, aiming to provide a reference for the future development of Digital Twin in fatigue management. Finally, the implementation mechanism of Digital Twin is further described over different phases during the bridge lifecycle. This paper also identifies two main obstacles for the development of Digital Twin: i) the lack of understanding of steel bridge fatigue, and ii) the insufficiency of the present technologies. © 2021 Techno-Press, Ltd. http://www.techno-press.org/?journal=smm&subpage=7","bridge maintenance; Digital Twin; fatigue life evaluation; lifecycle management; steel bridges",,,,,,"BK20190013; National Natural Science Foundation of China, NSFC: 51608258, 51978154","The research described in this paper was financially supported by the Fund for Distinguished Young Scientists of Jiangsu Province (grant no. BK20190013) and the Program of National Natural Science Foundation of China (grant no. 51978154, 51608258).",,,,,,,,,,"(2017) AASHTO LRFD Bridge Design Specification, , AASHTO LRFD-8 American Association of State Highway and Transportation Officials; Washington, USA; Ajmal, P.C.H., Mohammed, A., Finite element analysis based fatigue life evaluation approach for railway bridges: a study in Indian scenario (2018) Struct. Monit. Maint., Int. J, 5 (4), pp. 429-443. , https://doi.org/10.12989/smm.2018.5.4.429; Autiosalo, J., Vepsalainen, J., Viitala, R., Tammi, K., A feature-based framework for structuring industrial digital twins (2019) IEEE Access, 8, pp. 1193-1208. , https://doi.org/10.1109/ACCESS.2019.2950507; Bruynseels, K., Santoni de Sio, F., van den Hoven, J., Digital twins in health care: ethical implications of an emerging engineering paradigm (2018) Front. 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Steel Struct, 16 (3), pp. 735-742. , https://doi.org/10.1007/s13296-015-0089-x; Zhu, J., Zhang, W., Probabilistic fatigue damage assessment of coastal slender bridges under coupled dynamic loads (2018) Eng. Struct, 166, pp. 274-285. , https://doi.org/10.1016/j.engstruct.2018.03.073; Zhu, Z., Xiang, Z., Fatigue cracking investigation on diaphragm cutout in a self-anchored suspension bridge with orthotropic steel deck (2019) Struct. Infrastruct. Eng, 15 (10), pp. 1279-1291. , https://doi.org/10.1080/15732479.2019.1609528","Ding, Y.; Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, No. 2 Sipailou, Xuanwu District, China; email: civilding@seu.edu.cn",,,"Techno-Press",,,,,22886605,,,,"English","Struct. Monit. Maintenance",Article,"Final","",Scopus,2-s2.0-85108567225
"Ghita M., Siham B., Hicham M., Abdelhafid A., Laurent D.","57215578471;35323735800;24725091300;57218127198;57218127892;","Digital twins: development and implementation challenges within Moroccan context",2020,"SN Applied Sciences","2","5","885","","",,2,"10.1007/s42452-020-2691-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100793653&doi=10.1007%2fs42452-020-2691-6&partnerID=40&md5=fc219736a42546bba474c9b7b69e1524","Engineering Research Laboratory (LRI), System Architecture Team (EAS), National and High School of Electricity and Mechanic (ENSEM), Hassan II University Casablanca, Casablanca, Morocco; Research Foundation for Development and Innovation in Science and Engineering, Casablanca, 8118, Morocco; L.I.S.S.I.E.E ENSAM, Hassan II University Casablanca, Casablanca, Morocco; ILO, Polytechnic University of Mohammed VI, Benguerir, Morocco","Ghita, M., Engineering Research Laboratory (LRI), System Architecture Team (EAS), National and High School of Electricity and Mechanic (ENSEM), Hassan II University Casablanca, Casablanca, Morocco, Research Foundation for Development and Innovation in Science and Engineering, Casablanca, 8118, Morocco; Siham, B., Engineering Research Laboratory (LRI), System Architecture Team (EAS), National and High School of Electricity and Mechanic (ENSEM), Hassan II University Casablanca, Casablanca, Morocco, Research Foundation for Development and Innovation in Science and Engineering, Casablanca, 8118, Morocco; Hicham, M., Engineering Research Laboratory (LRI), System Architecture Team (EAS), National and High School of Electricity and Mechanic (ENSEM), Hassan II University Casablanca, Casablanca, Morocco, Research Foundation for Development and Innovation in Science and Engineering, Casablanca, 8118, Morocco; Abdelhafid, A., L.I.S.S.I.E.E ENSAM, Hassan II University Casablanca, Casablanca, Morocco; Laurent, D., ILO, Polytechnic University of Mohammed VI, Benguerir, Morocco","Industrial world today is experiencing its fourth industrial revolution. Predecessor of the automation industry, Industry-4.0 adds a layer of autonomy, intelligence and advanced connectivity to complex industrial systems. This layer creates an interactive and dynamic bridge between virtual systems and physical systems with their upward constraints and requirements in a constantly changing physical environment. Digital twins fall into the category of advanced concepts and technologies that enhance this connectivity. Several projects have been launched in Morocco by the government, industrialists and research communities, initiating the digital transformation of Moroccan industrial manufactories and companies. This paper comes within this framework, firstly to highlight the great potential that digital twins can offer for Moroccan industrial context enhancement, secondly to identify the challenges that can hinder the integration and development of digital twins in Moroccan industrial environment. © 2020, Springer Nature Switzerland AG.","Digital twins; Digital twins’ challenges and opportunities; Industry 4.0; Moroccan industrial context","Digital twin; Automation industry; Complex industrial systems; Digital transformation; Industrial context; Industrial environments; Industrial revolutions; Physical environments; Research communities; Industrial research",,,,,,"This research was supported by Mohammed VI Polytechnic University of Benguerir, Morocco and OCP group. We thank our colleagues from university who provided insight and expertise that greatly assisted the research. We thank our colleagues from university who provided insight and expertise that greatly assisted the research.",,,,,,,,,,"Xu, L.D., Xu, E.L., Li, L., Industry 4.0: state of the art and future trends (2018) Int J Prod Res, 56 (8), pp. 2941-2962; (2019) Vision stratégique 2016–2020, , http://www.ompic.org.ma/fr/content/vision-strategique-2016-2020, V. fr-Agence de production digitale, [En ligne]. Disponible sur, Consult 09 Nov 2019; El Saddik, A., Digital twins: the convergence of multimedia technologies (2018) IEEE Multimedia, 25 (2), pp. 87-92; Shao, G., Kibira, D., Digital manufacturing: Requirements and challenges for implementing digital surrogates (2018) 2018 Winter Simulation Conference (WSC). 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Accessed 25 Dec 2019; El Hamdi, S., Oudani, M., Abouabdellah, A., Morocco’s readiness to industry 4.0 (2020) Proceedings of the 8Th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18, 146, pp. 463-472. , Bouhlel MS, Rovetta S, Springer, Cham; http://mooc.um5.ac.ma/, UM5MOOC. [En ligne]. Disponible sur, Consult 09 Nov 2019; Chinesta, F., Cueto, E., Abisset-Chavanne, E., Virtual, digital and hybrid twins: a new paradigm in data-based engineering and engineered data (2020) Arch Comput Methods Eng, 27, pp. 105-134","Ghita, M.; Engineering Research Laboratory (LRI), Morocco; email: mezzzourghita1@gmail.com",,,"Springer Nature",,,,,25233971,,,,"English","SN Appl. Sci.",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85100793653
"Frostad A.I.T., Singer T., Fløttum L., Rikheim T.A., Balas R., Haaheim S.A.","57217108494;57217102936;57217100538;57203150772;57217102121;57215880170;","Unmanned full processing platforms; Using subsea technology as enabler",2020,"Proceedings of the Annual Offshore Technology Conference","2020-May",,,"","",,2,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086270256&partnerID=40&md5=1bebc74f789578e8ccb0795e6a6209c7","Aker Solutions, United States","Frostad, A.I.T., Aker Solutions, United States; Singer, T., Aker Solutions, United States; Fløttum, L., Aker Solutions, United States; Rikheim, T.A., Aker Solutions, United States; Balas, R., Aker Solutions, United States; Haaheim, S.A., Aker Solutions, United States","Offshore full-processing platforms are permanently manned due to the large number of operational and maintenance tasks. Having these platforms unmanned and remotely operated would improve the field economy, reduce the personnel risk and minimize the environmental footprint. The use of subsea mindset and technology in the platform design can enable such a shift in the manning and operations regime. Unmanned platforms have been in operation for decades in the form of simple platforms without complex process functions. The frequency of visits has however, turned out to be quite high. Further, maintaining facilities as unmanned has proved challenging when processing functions are added. To enable full-processing platforms to operate as unmanned installations, a new approach to design can be adopted. This new approach is characterized by use of design principles for complex subsea processing facilities and benefitting from the digital revolution. A main difference when comparing subsea and topside processing facilities is the significant number of utilities, support and safety functions on a platform. For the new platform concept proposed, several of these functions are simplified or eliminated, which reduces complexity, the need for maintenance and cost. The platform concept is among others proposed without accommodation and helideck. Access by personnel and evacuation is via bridge to Service Operation Vessel. By examining the Mean Time Between Failure for topside vs subsea equipment, it is evident that the subsea equipment has higher availability and requires less maintenance. For the topside equipment which as of today is maintenance intensive, e.g. compressors and pumps, the concept includes using subsea derived equipment. Seal-less subsea derived compressors are already proven for topside application. The maintenance need is determined based on surveillance by sensors and predictive analytics, i.e. predictive maintenance. On a staffed facility there are cranes and trolleys for material handling and personnel are carrying out in-situ inspection and repairs, representing a significant number of offshore man-hours. For Subsea installations, the principle is plug & play replacement by use of intervention vessels. The layout of the new platform concept is arranged in a subsea derived manner adopting the replacement principle by use of vessels. Also, the platform is proposed with robotics tailored for intervention of minor items. New technologies as drones and crawlers are developing rapidly and are used for inspection tasks replacing personnel, in addition to sensors and cameras and in combination with a digital twin. These technologies are comparable to using ROV for inspection of subsea equipment. This paper will present the conceptual idea for the subsea derived full-processing unmanned platform in more detail, and discuss benefits compared to a conventional staffed full-processing platform. © 2020, Offshore Technology Conference.",,"Digital twin; Inspection; Materials handling; Offshore oil well production; Personnel; Predictive analytics; Predictive maintenance; Remotely operated vehicles; Digital revolution; Environmental footprints; Inspection and repair; Mean time between failures; Processing facilities; Processing platform; Service operations; Subsea installations; Offshore technology",,,,,,,,,,,,,,,,"https://www.akerbp.com, Hod, Tambar, Valhall Flank North, Valhall Flank South, Valhall Flank West; https://www.equinor.com, Oseberg H, Valemon, Huldra, Sleipner B; https://www.woodside.com.au, Angel; https://www.akersolutions.com, Digital Solutions-ix3, Aker Solutions to Develop Digital Twin for Wintershall's Nova Field; https://www.bhge.com, ICL-Integrated Compressor Line; https://new.siemens.com, Hermetically Sealed Compressors; https://turbomachinery.man-es.com, High-Speed Oil Free Compressor; https://turbomachinery.man-es.com/news/ivar-aasen; Yeaw, S.H., Storstenvik, A., Vesterkjær, R., (2018) Subsea COMPRESSION: Reliable and Proven Solutions to Increase Gas Fields Recovery, , Paper OTC-28527-MS, OTC Asia, Kuala Lumpur Malaysia, 20-23 March; (2015) NTNU: Oreda Handbook, , Sintef, 6th edition-Volume I Topside equipment; www.rockwellautomation.com, PowerFlex 7000 Direct-To-Drive; www.abb.com, ACS 2000 Direct-To-Line; https://www.gepowerconversion.com, MV7, Transformer-less; Oluwatoyin, S., Hashim, F., Hussin, H., Failure Mode and Effect Analysis of Subsea Multiphase Pump Equipment, , Web of Conferences. 13. 05001.10.1051/matecconf/20141305001; https://www.hugg.no/Prosjekter/Fjernstyrt-lasting-av-kjemikalier-for-NOAKA-feltet; www.gulfmark.com; www.sriemas.com; www.boa.no; www.deepoceangroup.com; https://www.sbmoffshore.com; https://www.macartney.com; Norwegian Petroleum Safety Authority, , www.psa.no; Facilities Regulation, §6 Design of Simpler Facilities, , https://www.ptil.no/en/regulations/allacts/?forskrift=634; (2009) Nopsema Offshore Petroleum and Greenhouse Gas Storage (Safety) Regulations, , https://www.legislation.gov.au/Details/F2013C00945; http://oilinwater.com; Llewelyn, D., Selnes, P.O., (2011) 10 Year Operability Survey of Norwegian Fpsos, , The Norwegian Oil Industry Association (OLF), 15 March; https://support.industry.siemens.com/cs/attachments/109746230/poster-SINAMICS-mvdrives_en.pdf, see SH150",,,,"Offshore Technology Conference","Offshore Technology Conference 2020, OTC 2020","4 May 2020 through 7 May 2020",,160167,01603663,9781613997079,OSTCB,,"English","Proc. Annu. Offshore Technol. Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85086270256
"Törmä S., Toivola P., Kiviniemi M., Puntila P., Lampi M., Mätäsniemi T.","6602149776;12782596400;6602426056;57215330840;55416083100;6504155486;","Ontology-based sharing of structural health monitoring data",2019,"20th Congress of IABSE, New York City 2019: The Evolving Metropolis - Report",,,,"2214","2221",,2,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074453225&partnerID=40&md5=f55afd0ddd1b66419b9819998cf10957","VisuaLynk, Espoo, Finland; Savcor Helsinki, Finland; VTT, Espoo, Finland; Trimble Solutions, Espoo, Finland; VTT, Tampere, Finland","Törmä, S., VisuaLynk, Espoo, Finland; Toivola, P., Savcor Helsinki, Finland; Kiviniemi, M., VTT, Espoo, Finland; Puntila, P., Trimble Solutions, Espoo, Finland; Lampi, M., Savcor Helsinki, Finland; Mätäsniemi, T., VTT, Tampere, Finland","A structural health monitoring system installed in a bridge produces a vast amount of sensor data that is analyzed and periodically reported to a bridge owner at an aggregate level. The data itself typically remains in the monitoring service of a service provider; it may be accessible to clients and third parties through a dedicated user interface and API. This paper presents an ontology to defining the monitoring model based on the Semantic Sensor Network Ontology by W3C. The goal is to enable an asset owner to utilize preferred tools to view and access monitoring data from different service providers, and in longer term, increase the utilization of monitoring data in facility management. The ultimate aim is to use BrIM as a digital twin of a bridge and to link external datasets to improve information management and maintenance over its lifecycle. © 20th Congress of IABSE, New York City 2019: The Evolving Metropolis - Report. All rights reserved.","Bridge information model; Facility management; Linked data; Monitoring; Ontology","Application programming interfaces (API); Information management; Life cycle; Linked data; Office buildings; Ontology; Semantics; Sensor networks; Structural health monitoring; User interfaces; Access monitoring; Different services; Facility management; Information Modeling; Monitoring models; Monitoring services; Service provider; Structural health monitoring systems; Monitoring",,,,,"City, University of London, City","This research belongs to a collaborative research project SmartBridgeFM (2017-2019), partially funded by Business Finland. Special thanks for City of Helsinki for providing the BrIMs of Crusell bridge.",,,,,,,,,,"Endsley, M.R., Toward a theory of situation awareness in dynamic systems (1995) Human Factors, 37 (1), pp. 85-104; W3C Data Activity, , https://www.w3.org/2013/data/, W3C; Dijkstra, E., On the role of scientific thought (1982) Selected Writings on Computing: A Personal Perspective, pp. 60-66. , NY, Springer; (2017) OPC Unified Architecture, , https://opcfoundation.org/ua/, OPC Foundation; Cyganiak, R., Wood, D., Lanthaler, M., (2014) RDF 1.1 Concepts and Abstract Syntax, W3C Recommendation, , https://www.w3.org/TR/rdf11-concepts/; (2012) OWL 2 Web Ontology LanguageDocument Overview, , https://www.w3.org/TR/owl-overview/, W3C Recommendation; Berners-Lee, T., (2006) Linked Data -Design Issues, W3C Note, , http://www.w3.org/DesignIssues/LinkedData.html; Linked Open Vocabularies, , https://lov.linkeddata.es/dataset/lov/; Törmä, S., Semantic linking of building information models (2013) IEEE Seventh International Conference on Semantic Computing, , Irvine, CA; Pauwels, P., Supporting decision-making in the building life-cycle using linked building data (2014) Buildings, 4 (3), pp. 549-579; Beetz, J., Van Leeuwen, J., De Vries, B., IFCOwl: A case of transforming EXPRESS schemas into ontologies (2009) AI EDAM, 23 (1), pp. 89-101; Pauwels, P., Terkaj, W., EXPRESS to OWL for construction industry: Towards a recommendable and usable ifcOWL ontology (2016) Automation in Construction, 63, pp. 100-133; Hoang, N.V., Törmä, S., Implementation and experiments with an IFC-to-linked data converter (2015) 32nd International Conference of CIB W78; Bonduel, M., Oraskari, J., Pauwels, P., Vergauwen, M., Klein, R., The IFC to linked building data converter - Current status (2018) 6th Linked Data in Architecture and Construction Workshop, , London; Haller, A., Janowicz, K., Cox, S., Le Phuoc, D., Taylor, K., Lefrançois, M., Semantic Sensor Network Ontology, W3C Recommendation, , https://www.w3.org/TR/vocab-ssn/, W3C; Cox, S., (2018) Extensions to the Semantic Sensor Network Ontology, , https://www.w3.org/TR/vocab-ssn-ext/, W3C; Turunen, M., Pulkkinen, P., Toivola, P., Structural health monitoring of Crusell bridge (2016) 19th IABSE Congress, , Stockholm; Crusell Bridge Trimble, , https://www.tekla.com/references/crusellbridge","Kiviniemi, M.; VTTFinland; email: markku.kiviniemi@vtt.fi",,"Allplan (Gala);et al.;Hardesty and Hanover;Silman;Wiss, Janney, Elstner Associates, Inc.;WSP","International Association for Bridge and Structural Engineering (IABSE)","20th IABSE Congress, New York City 2019: The Evolving Metropolis","4 September 2019 through 6 September 2019",,152767,,9783857481659,,,"English","Congr. IABSE, New York City: Evol. Metropolis - Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85074453225
"Andersen J.E., Rex S.","7403571045;57211567139;","Structural health monitoring of henry Hudson I89",2019,"20th Congress of IABSE, New York City 2019: The Evolving Metropolis - Report",,,,"2121","2131",,2,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074449290&partnerID=40&md5=c7cb2a2736050aa2c3864840846e6b29","COWI, Lyngby, Denmark","Andersen, J.E., COWI, Lyngby, Denmark; Rex, S., COWI, Lyngby, Denmark","To increase construction safety during and after establishing an alternative load path for the arch supports of the Henry Hudson I89 Bridge in New York a Structural Health Monitoring System (SHMS) was employed in combination with a digital twin calibrated by the SHMS data. This method is one of the first in the United States used on a large-scale bridge installation. The calibration was done by moving a Live Load across the bridge and use the strain gauges to detect weight, speed, and spacing of the passing trucks. The measured configuration of the Live Load has subsequently been applied to a Digital Twin creating the digital responses. The measured forces analysed by applying linear stress theory and least square method on the strain gauge measurements, and the calculated Digital Twin forces show the same behaviour and the absolute values do not deviate more than 5%. This despite of a very small utilisation. After the Live load validation of the measurements a data-driven approach has been applied to identify critical behavior of the bridge while the digital twin has been held as backup for analyzing consequences of any extreme load combination, should it occur. © 20th Congress of IABSE, New York City 2019: The Evolving Metropolis - Report. All rights reserved.","Calibrated FE-Model; Digital twin; Predicative maintenance tool; Safe operation; Structural Health Monitoring; Warning System","Alarm systems; Arch bridges; Least squares approximations; Loads (forces); Monitoring; Strain gages; Alternative load paths; Data-driven approach; Digital twin; FE model; Least square methods; Predicative maintenance; Safe operation; Structural health monitoring systems; Structural health monitoring",,,,,,,,,,,,,,,,,"Andersen, J.E.; COWIDenmark; email: JCA@COWI.com",,"Allplan (Gala);et al.;Hardesty and Hanover;Silman;Wiss, Janney, Elstner Associates, Inc.;WSP","International Association for Bridge and Structural Engineering (IABSE)","20th IABSE Congress, New York City 2019: The Evolving Metropolis","4 September 2019 through 6 September 2019",,152767,,9783857481659,,,"English","Congr. IABSE, New York City: Evol. Metropolis - Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85074449290
"Adibfar A., Costin A.M.","57202945239;55200193500;","Creation of a Mock-up Bridge Digital Twin by Fusing Intelligent Transportation Systems (ITS) Data into Bridge Information Model (BrIM)",2022,"Journal of Construction Engineering and Management","148","9","04022094","","",,1,"10.1061/(ASCE)CO.1943-7862.0002332","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134403624&doi=10.1061%2f%28ASCE%29CO.1943-7862.0002332&partnerID=40&md5=81bebad90f75679dc41e7666aec0daa2","M.E. Rinker Sr. School Of Construction Management, Univ. Of Florida, 323 Rinker Hall, Gainesville, FL  32603, United States","Adibfar, A., M.E. Rinker Sr. School Of Construction Management, Univ. Of Florida, 323 Rinker Hall, Gainesville, FL  32603, United States; Costin, A.M., M.E. Rinker Sr. School Of Construction Management, Univ. Of Florida, 323 Rinker Hall, Gainesville, FL  32603, United States","Passage of overweighted commercial vehicles is one of the significant causes of damage to the pavement and structural components of bridges. Weigh-in-motion (WIM) systems can currently detect real-time traffic data; however, these data are stored in standalone databases. Building information modeling (BIM) has transformed the construction industry by injecting ""information""into the building model and integrating different databases. BIM capabilities for bilateral exchange of data led to the inception of digital twin. This research investigates the feasibility of developing a digital twin of a mock-up bridge by integrating WIM data into a bridge information model (BrIM). The system was validated by first creating a mock-up bridge with affixed weight sensors attached to microcomputers and then developing a BrIM model and passing scaled vehicles over in real time with varying weight capacities. This study showed the feasibility of creating digital twins, ultimately enabling future research. © 2022 American Society of Civil Engineers.","Bridge; Case study; Digital Twin; Infrastructure; Internet of Things","Architectural design; Commercial vehicles; Construction industry; Data integration; Intelligent systems; Mockups; Office buildings; Real time systems; Building Information Modelling; Building model; Case-studies; Information Modeling; Infrastructure; Intelligent transportation systems; Mock up; Real-time traffic datum; Structural component; Weigh-in-motion systems; Internet of things",,,,,,,,,,,,,,,,"Adibfar, A., (2020) Bridge digital twins: Fusion of intelligent transportation systems (ITS) sensor data and bridge information modeling (BrIM) for interoperability, , Doctoral dissertation, M. E. Rinker Sr School of Construction Management, Dept. of Design, Construction, and Planning, Univ. of Florida; Adibfar, A., Costin, A., (2019) Advances in informatics and computing in civil and construction engineering, pp. 43-50. , a. "" Next generation of transportation infrastructure management: Fusion of intelligent transportation systems (ITS) and bridge information modeling (BrIM)."" In, Cham, Switzerland: Springer; Adibfar, A., Costin, A., (2019) Evaluation of IFC for the augmentation of intelligent transportation systems (ITS) into bridge information models (BrIM), , b. "" "" In Proc. ASCE Int. Conf. on Computing in Civil Engineering. Reston, VA: ASCE; Adibfar, A., Costin, A., (2021) Integrated management of bridge infrastructure through bridge digital twins: A preliminary case study, , a. "" "" In Proc. ASCE Int. Conf. on Computing in Civil Engineering 2021. Reston, VA: ASCE; Adibfar, A., Costin, A., (2021) Review of data serialization challenges and validation methods for improving interoperability, , b. "" "" In Proc. ASCE Int. Conf. on Computing in Civil Engineering 2021. Reston, VA: ASCE; Adibfar, A., Gulhare, S., Srinivasan, S., Costin, A., (2022) Analysis and modeling of changes in online shopping behavior due to COVID-19 pandemic: A Florida case study, , Amsterdam, Netherlands: Elsevier; Akinci, N., Liu, J., Bowman, M., Spring analogy to predict the 3-D live load response of slab-on-girder bridges (2013) Eng. Struct., 56 (6), pp. 1049-1057. , https://doi.org/10.1016/j.engstruct.2013.06.025; Aroch, R., Sokol, M., Venglar, M., Structural health monitoring of major Danube bridges in Bratislava (2016) Procedia Eng., 156 (AUG), pp. 24-31. , https://doi.org/10.1016/j.proeng.2016.08.263; (2017) Infrastructure report card, , https://www.infrastructurereportcard.org, ASCE. "" "" Accessed July 19, 2018; (2021) Infrastructure report card, , https://www.infrastructurereportcard.org, ASCE. "" "" Accessed April 1, 2021; Badrinath, A., Chang, Y., Lin, E., Hsien, S., Zhao, B., (2016) A preliminary study on BIM enabled design warning analysis in T3A terminal of Chongqing Jiangbei international airport, , In Proc. ICCCBE2016-16th Int. Conf. of Computing in Civil and Building Engineering (ICCCBE). Reston, VA: ASCE; Catbas, F., Malekzadeh, M., A machine learning-based algorithm for processing massive data collected from the mechanical components of movable bridges (2016) Autom. Constr., 72 (3), pp. 269-278. , https://doi.org/10.1016/j.autcon.2016.02.008; Catbas, F.N., Susoy, M., Frangopol, D.M., Structural health monitoring and reliability estimation: Long span truss bridge application with environmental monitoring data (2008) Eng. Struct., 30 (9), pp. 2347-2359. , https://doi.org/10.1016/j.engstruct.2008.01.013; Chen, Y., Modeling and analysis methods of bridges and their effects on seismic responses: II-implementation (1996) Comput. Struct., 59 (1), pp. 99-114. , https://doi.org/10.1016/0045-7949(95)00226-X; Costin, A., (2016) A new methodology for interoperability of heterogeneous bridge information models, , Ph.D. Dissertation, School of Civil & Environmental Engineering, Georgia Institute of Technology; Costin, A., Adibfar, A., Hu, H., Chen, S., Building information modeling (BIM) for transportation infrastructure-Literature review, applications, challenges, and recommendations (2018) Autom. Constr., 94 (3), pp. 257-281. , https://doi.org/10.1016/j.autcon.2018.07.001; Costin, A., Adibfar, A., Nawari, N., Eastman, C.M., (2019) Preliminary evaluation of the industry foundation classes (IFC) to enable smart city applications, , In Proc. 21st CIB World Building Congress-Constructing smart Cities. Delft, Netherlands: CIB; Costin, A., Eastman, C., Need for interoperability to enable seamless information exchanges in smart and sustainable urban systems (2019) J. Comput. Civ. Eng., 33 (3). , https://doi.org/10.1061/(ASCE)CP.1943-5487.0000824, 04019008; Costin, A., Teizer, J., Fusing passive RFID and BIM for increased accuracy in indoor localization (2015) Visualization Eng., 3 (1), p. 17. , https://doi.org/10.1186/s40327-015-0030-6; Delgado, J.M., Butler, L.J., Gibbons, N., Brilakis, I., Elshafie, M.Z., Middleton, C., Management of structural monitoring data of bridges using BIM (2017) J. Bridge Eng., 170 (3), pp. 204-218. , https://doi.org/10.1680/jbren.16.00013; Dygalo, V., Keller, A., Shcherbin, A., Principles of application of virtual and physical simulation technology in the production of digital twin of active vehicle safety systems (2020) Transp. Res. Procedia, 50 (10), pp. 121-129. , https://doi.org/10.1016/j.trpro.2020.10.015; Elnabwy, M., Kaloop, M., Elbeltagi, E., Talkha steel highway bridge monitoring and movement identification using RTK-GPS technique (2013) Measurement, 46 (9), pp. 4282-4292. , https://doi.org/10.1016/j.measurement.2013.08.014; (2018) Truck size and weight research pooled fund project TPF-5(283): The influence of vehicular live loads on bridge performance, , https://highways.dot.gov/bridges-and-structure/long-term-bridge-performance/truck-size-and-weight-research, FHWA. "" "" Accessed October 27, 2019; Fortino, S., Genoese, A., Genoese, A., Nunes, L., Palma, P., Numerical modeling of the hygro-thermal response of timber bridges during their service life: A monitoring case-study (2013) Constr. Build. Mater., 47 (6), pp. 1225-1234. , https://doi.org/10.1016/j.conbuildmat.2013.06.009; Hofmann, W., Branding, F., Implementation of an IoT and cloud-based digital twin for real-time decision support in port operations (2019) IFAC PapersOnLine, 52 (13), pp. 2104-2109. , https://doi.org/10.1016/j.ifacol.2019.11.516; Hüthwohl, P., Lu, R., Brilakis, I., (2016) Challenges of bridge maintenance inspection, pp. 51-58. , In Proc. 16th Int. Conf. on Computing in Civil and Building Engineering (ICCCBE2016), edited by N. Yabuki and K. Makanae, Osaka, Japan: ICCCBE2016 Organizing Committee; Jeong, S., Byun, J., Kimb, D., Sohn, H., Bae, I.H., Law, K.H., (2015) A data management infrastructure for bridge monitoring, , In Proc. SPIE 9435, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2015, 94350P. Washington, DC: SPIE; Jeong, S., Zhang, Y., O'Connor, S., Lynch, J.P., Sohn, H., Law, K.H., A NoSQL data management infrastructure for bridge monitoring (2016) Smart Struct. Syst., 17 (4), pp. 669-690. , https://doi.org/10.12989/sss.2016.17.4.669; Lee, H., Lee, M., Lee, I., Nam, S., (2017) A study on the development of 3d parametric model for reinforced concrete bridge piers, pp. 102-105. , In Proc. Int. Conf. of Computing in Civil and Building Engineering (ICCCBE), Reston, VA: ASCE; Li, J., Hao, H., Health monitoring of joint conditions in steel truss bridges with relative displacement sensors (2016) Measurement, 88 (2), pp. 360-371. , https://doi.org/10.1016/j.measurement.2015.12.009; Liu, K., El-Gohary, N., Ontology-based semi-supervised conditional random fields for automated information extraction from bridge inspection reports (2017) Autom. Constr., 81 (FEB), pp. 313-327. , https://doi.org/10.1016/j.autcon.2017.02.003; Maeda, K., Takahashi, S., Ogawa, T., Haseyama, M., (2017) Distress classification of class imbalanced data for maintenance inspection of road structures in expressway, , In Proc. Int. Conf. of Computing in Civil and Building Engineering (ICCCBE). Reston, VA: ASCE; Marzouk, M., Hisham, M., (2011) Bridge information modeling in sustainable bridge management, pp. 457-466. , In Proc. Int. Conf. on Sustainable Design and Construction (ICSDC), Reston, VA: ASCE; McGuire, B., Atadero, R., Clevenger, C., Ozbek, M., Bridge information modeling for inspection and evaluation (2016) J. Bridge Eng., 21 (4). , https://doi.org/10.1061/(ASCE)BE.1943-5592.0000850, 04015076; Metni, N., Hamel, T., A UAV for bridge inspection: Visual servoing control law with orientation limits (2007) Autom. Constr., 17 (DEC), pp. 3-10. , https://doi.org/10.1016/j.autcon.2006.12.010; Motamedi, A., Yabuki, N., Fukuda, T., (2017) Extending BIM to include defects and degradations of buildings and infrastructure facilities, pp. 110-113. , In Proc. Int. Conf. of Civil and Building Engineering Informatics (ICCBEI), Reston, VA: ASCE; Oh, J., Jang, G., Oh, S., Lee, J.H., Yi, B., Moon, Y.S., Lee, J.S., Choi, Y., Bridge inspection robot system with machine vision (2009) Autom. Constr., 18 (JAN), pp. 929-941. , https://doi.org/10.1016/j.autcon.2009.04.003; Okasha, N., Frangopol, D., Computational platform for the integrated life-cycle management of highway bridges (2011) Eng. Struct., 33 (MAR), pp. 2145-2153. , https://doi.org/10.1016/j.engstruct.2011.03.005; Prendergast, L.J., Gavin, K., A review of bridge scour monitoring techniques (2014) J. Rock Mech. Geotech. Eng., 6 (2), pp. 138-149. , https://doi.org/10.1016/j.jrmge.2014.01.007; Sekiya, H., Maruyama, O., Miki, C., Visualization system for bridge deformations under live load based on multipoint simultaneous measurements of displacement and rotational response using MEMS sensors (2017) Eng. Struct., 146 (SEP), pp. 43-53. , https://doi.org/10.1016/j.engstruct.2017.05.036; Sofia, H., Anas, E., Faiz, O., (2020) Mobile mapping, machine learning and digital twin for road infrastructure monitoring and maintenance: Case study of Mohammed VI Bridge in Morocco, , In Proc. 2020 IEEE Int. Conf. of Moroccan Geomatics (Morgeo). New York: IEEE; Teng, S., Toud, M., Leong, W., How, B., Lam, H., Masa, V., Recent advances on industrial data-driven energy savings: Digital twins and infrastructures (2021) Renewable Sustainable Energy Rev., 135 (APR), p. 110208. , https://doi.org/10.1016/j.rser.2020.110208; Tochaei, E., Fang, Z., Taylor, T., Babanajad, S., Ansari, F., Structural monitoring and remaining fatigue life estimation of typical welded crack details in the Manhattan bridge (2021) Eng. Struct., 231 (11), p. 111760. , https://doi.org/10.1016/j.engstruct.2020.111760; Zhiming, B., Jianhang, T., Mengyao, W., Siqi, W., Yuxin, H., (2019) In depth: Overloaded and overturned-Inside the deadly Wuxi bridge collapse, , https://www.caixinglobal.com/2019-11-01/in-depth-overloaded-and-overturned-inside-the-deadly-wuxi-bridge-collapse-101478011.html, Accessed November 1, 2019; Zou, Y., Kiviniemi, A., Jones, S., A review of risk management through BIM and BIM-related technologies (2017) Saf. Sci., 97 (DEC), pp. 88-98. , https://doi.org/10.1016/j.ssci.2015.12.027","Adibfar, A.; M.E. Rinker Sr. School Of Construction Management, 323 Rinker Hall, United States; email: adib2016@ufl.edu",,,"American Society of Civil Engineers (ASCE)",,,,,07339364,,JCEMD,,"English","J Constr Eng Manage",Article,"Final","",Scopus,2-s2.0-85134403624
"Jiang F., Ma L., Broyd T., Chen K., Luo H.","57226336944;55720988200;56615866400;57211364148;23976309500;","Underpass clearance checking in highway widening projects using digital twins",2022,"Automation in Construction","141",,"104406","","",,1,"10.1016/j.autcon.2022.104406","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131677108&doi=10.1016%2fj.autcon.2022.104406&partnerID=40&md5=5038552210110b09f15772f6becb0580","The Bartlett School of Sustainable Construction, University College London, London, WC1E 6BT, United Kingdom; School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China","Jiang, F., The Bartlett School of Sustainable Construction, University College London, London, WC1E 6BT, United Kingdom; Ma, L., The Bartlett School of Sustainable Construction, University College London, London, WC1E 6BT, United Kingdom; Broyd, T., The Bartlett School of Sustainable Construction, University College London, London, WC1E 6BT, United Kingdom; Chen, K., School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Luo, H., School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China","Main road widening can reduce the clearance of the low-level underpass road, restricting the passage of vehicles and leading to collisions with structures. Therefore, checking the clearance of the underpass road effectively should be considered at the design stage. This paper describes a digital twin approach for checking the clearance of underpass roads in highway widening projects using online map data. The underpass road digital twin and BIM model of the newly widened road based on the existing main road digital twin are created to assist the clearance check and redesign. The proposed method presented a cost-effective clearance check for underpass roads in road widening design without field surveys and was successfully implemented in an underpass road in the UK. In future research, more digital twin methods for overpasses, bridges, tunnels, and traffic safety facilities should be employed comprehensively to assist more road widening applications. © 2022 The Authors","Alignment fitting; Building information modelling; Clearance check; Digital terrain model (DTM); Digital twin; Road widening; Underpass","Cost effectiveness; Highway planning; Roads and streets; Surveys; Alignment fitting; Building Information Modelling; Clearance check; Design stage; Digital terrain model; Main roads; Map data; Online maps; Road widening; Architectural design",,,,,"2020ACA006","This study is supported by the Major Science &Technology Project of Hubei (Grant No. 2020ACA006 ).",,,,,,,,,,"Adeyemi, G.A., Gbolahan, O.G., Markus, M., Edeki, S.O., Geospatial analysis of building demolition during road expansion project in Ado-Odo OTA settings (2018) Int. J. Civil Eng. 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"Lei D., Rong K., Song B., Ding H., Tang J.","55788200700;57219099054;57224216343;57382479900;55800516200;","Digital twin modeling for tooth surface grinding considering low-risk transmission performance of non-orthogonal aviation spiral bevel gears",2022,"ISA Transactions","128",,,"646","663",,1,"10.1016/j.isatra.2021.11.036","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121754773&doi=10.1016%2fj.isatra.2021.11.036&partnerID=40&md5=46384f0348d71d724fe0f8fcfbb69fab","State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, 410083, China; AECC Hunan Aviation Powerplant Research Institute, Zhuzhou, 412002, China","Lei, D., State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, 410083, China, AECC Hunan Aviation Powerplant Research Institute, Zhuzhou, 412002, China; Rong, K., State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, 410083, China; Song, B., State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, 410083, China; Ding, H., State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, 410083, China; Tang, J., State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, 410083, China","Low-risk transmission performance including elastic deformation, loaded contact pattern, load distribution and loaded transmission error is of paramount significance to the actual manufacturing for non-orthogonal aviation spiral bevel gears. The advanced digital twin technology is introduced into tooth flank grinding. A new digital twin modeling considering low-risk transmission performances is proposed. In the modeling, low-risk transmission performance driven simulation, sensitivity analysis and robust control are developed, respectively Firstly, data-driven tooth surface modeling is developed by simulating free-form tooth surface grinding including gear tilt method and pinion double helical method. With local geometric boundary setup meshing stiffness is determined by using local Rayleigh–Ritz solution. Then, to deal with the sensitivity of gear assembly, an improved tooth contact analysis (TCA) is developed. Moreover, numerical loaded tooth contact analysis (NLTCA) is performed to build a bridge between of low-risk performances and hypoid generator parameters. The low-risk transmission performance driven control model is established by using hypoid generator parameters modification. Finally, sensitivity analysis strategy-based robust control model is solved by using Levenberg–Marquardt method for accurate hypoid generator parameters having modification amount. The provided numerical instance can verify the proposed method. © 2021 ISA","Digital twin modeling; Free-form tooth surface grinding; Low-risk transmission performance; Non-orthogonal aviation spiral bevel gears; Numerical loaded tooth contact analysis (NLTCA)","Bevel gears; Grinding (machining); Numerical methods; Numerical models; Risk assessment; Robust control; Transmissions; Digital twin modeling; Free-form tooth surface grinding; Freeforms; Loaded tooth contact analysis; Low-risk transmission performance; Non-orthogonal; Non-orthogonal aviation spiral bevel gear; Numerical loaded tooth contact analyse; Spiral bevel gears; Surfaces grinding; Teeth surface; Transmission performance; Sensitivity analysis; adult; article; aviation; digital twin; DNA helix; grinding; rigidity; sensitivity analysis; tooth",,,,,"HFZL2020CXY025; National Natural Science Foundation of China, NSFC: U1604255; National Key Research and Development Program of China, NKRDPC: 2020YFB2010200","The authors gratefully acknowledge the support of National Key Research and Development Program of China through Grants No. 2020YFB2010200, China Aviation Engine Group Industry-University-Research Cooperation Project through Grants HFZL2020CXY025 , and the National Natural Science Foundation of China (NSFC) through Grants U1604255 .",,,,,,,,,,"Litvin, F.L., Fuentes, A., Gear geometry and applied theory (2004), PTR Prentice Hall Englewood Cliffs; Shao, W., Ding, H., Tang, J.Y., Peng, S.D., A data-driven optimization model to collaborative manufacturing system considering geometric and physical performances for hypoid gear product (2018) Robot Comput Integr Manuf, 54, pp. 1-16; Li, J.M., Wang, H., Zhang, J.F., Yao, X.F., Zhang, Y.G., Impact fault detection of gearbox based on variational mode decomposition and coupled underdamped stochastic resonance (2019) ISA Trans, 95, pp. 320-329; Peng, S.D., Ding, H., Zhang, G., New determination to loaded transmission error of the spiral bevel gear considering multiple elastic deformation evaluations under different bearing supports (2019) Mech Mach Theory, 137, pp. 37-52; Yang, Q.C., Liu, T., Wu, X., Deng, Y.N., Chen, Q., A planetary gear reducer backlash identification based on servo motor current signal and optimized fisher discriminant analysis (2021) ISA Trans, 112, pp. 350-362; Stantfeld, H.J., Advanced bevel gear technology (2000), The Gleason Works Rochester, NY; Gabiccini, M., Artoni, A., Guiggni, M., On the identification of machine settings for gear surface topography corrections (DETC2011-47727) (2012) ASME J Mech Des, 134 (4); Artoni, A., Gabiccini, M., Guiggiani, M., Multi-objective ease-off optimization of hypoid gears for their efficiency, noise, and durability performances (2011) ASME J Mech Des, 133 (6); Shih, Y.-P., Chen, S.-D., Free-from flank correction in helical gear grinding on a five-axis CNC gear profile grinding machine (2012) ASME J Manuf Sci E-Trans, 134 (8); Shih, Y.-P., A novel ease-off flank modification methodology for spiral bevel and hypoid gears (2010) Mech Mach Theory, 45, pp. 1108-1124; Simon, V.V., Influence of tooth modifications on tooth contact in face-hobbed spiral bevel gears (2011) Mech Mach Theory, 46, pp. 1980-1998; Ding, H., Wan, Z.G., Zhou, Y.S., Tang, J.Y., A data-driven programming of human-machine interactions for modeling a collaborative manufacturing system of hypoid gear by considering geometric and physical performances (2018) Robot Comput Integr Manuf, 51, pp. 121-138; Fan, Q., DaFoe, R.S., Swanger, J.W., Higher-order tooth flank form error correction for face-milled spiral bevel and hypoid gears (2008) ASME J Mech Des, 130 (7); Krenzer, T.J., (1984), Computer Aided corrective machine settings for manufacturing bevel and hypoid gear sets. 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"Kasper L., Birkelbach F., Schwarzmayr P., Steindl G., Ramsauer D., Hofmann R.","57217080482;57194472014;57219354141;57210927410;57226746998;55413515600;","Toward a Practical Digital Twin Platform Tailored to the Requirements of Industrial Energy Systems",2022,"Applied Sciences (Switzerland)","12","14","6981","","",,1,"10.3390/app12146981","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136374725&doi=10.3390%2fapp12146981&partnerID=40&md5=ed9a013a9e5d003277b85781169a4bd2","Institute of Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9/BA/E302, Vienna, A-1060, Austria; Institute of Computer Engineering, TU Wien, Treitlstrasse 3, Vienna, A-1040, Austria","Kasper, L., Institute of Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9/BA/E302, Vienna, A-1060, Austria; Birkelbach, F., Institute of Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9/BA/E302, Vienna, A-1060, Austria; Schwarzmayr, P., Institute of Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9/BA/E302, Vienna, A-1060, Austria; Steindl, G., Institute of Computer Engineering, TU Wien, Treitlstrasse 3, Vienna, A-1040, Austria; Ramsauer, D., Institute of Computer Engineering, TU Wien, Treitlstrasse 3, Vienna, A-1040, Austria; Hofmann, R., Institute of Energy Systems and Thermodynamics, TU Wien, Getreidemarkt 9/BA/E302, Vienna, A-1060, Austria","Digitalization and concepts such as digital twins (DT) are expected to have huge potential to improve efficiency in industry, in particular, in the energy sector. Although the number and maturity of DT concepts is increasing, there is still no standardized framework available for the implementation of DTs for industrial energy systems (IES). On the one hand, most proposals focus on the conceptual side of components and leave most implementation details unaddressed. Specific implementations, on the other hand, rarely follow recognized reference architectures and standards. Furthermore, most related work on DTs is done in manufacturing, which differs from DTs in energy systems in various aspects, regarding, for example, multiple time-scales, strong nonlinearities and uncertainties. In the present work, we identify the most important requirements for DTs of IES. We propose a DT platform based on the five-dimensional DT modeling concept with a low level of abstraction that is tailored to the identified requirements. We address current technical implementation barriers and provide practical solutions for them. Our work should pave the way to standardized DT platforms and the efficient encapsulation of DT service engineering by domain experts. Thus, DTs could be easy to implement in various IES-related use cases, host any desired models and services, and help get the most out of the individual applications. This ultimately helps bridge the interdisciplinary gap between the latest research on DTs in the domain of computer science and industrial automation and the actual implementation and value creation in the traditional energy sector. © 2022 by the authors.","digital twin platform; digital twin requirements; industrial energy systems; integrated energy systems; service engineering; service-oriented architecture",,,,,,"Technische Universität Wien Bibliothek, TU Wien Bibliothek","The authors acknowledge TU Wien Bibliothek for financial support through its Open Access Funding Programme.",,,,,,,,,,"Gibb, D., Johnson, M., Romaní, J., Gasia, J., Cabeza, L.F., Seitz, A., Process integration of thermal energy storage systems—Evaluation methodology and case studies (2018) Appl. 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Ind, 135, p. 103571; Richardson, C., (2018) Microservices Patterns: With Examples in Java, , Manning, Hong Kong, China; Steindl, G., (2021) Digital Twin Service Framework, , https://github.com/Smart-Industrial-Concept/DigitalTwinServiceFramework, Available online; Fernández-López, M., Gómez-Pérez, A., Juristo, N., METHONTOLOGY: From Ontological Art Towards Ontological Engineering (1997) Proceedings of the Ontological Engineering AAAI-97 Spring Symposium Series, pp. 24-26. , http://oa.upm.es/5484, Stanford, CA, USA, 24–26 March 1997, American Asociation for Artificial Intelligence, Menlo Park, CA, USA, Available online","Kasper, L.; Institute of Energy Systems and Thermodynamics, Getreidemarkt 9/BA/E302, Austria; email: lukas.kasper@tuwien.ac.at",,,"MDPI",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85136374725
"Fillmore T.B., Wu Z., Vega M.A., Hu Z., Todd M.D.","57201315841;57789011300;57209317664;55705495700;7202805915;","A surrogate model to accelerate non-intrusive global–local simulations of cracked steel structures",2022,"Structural and Multidisciplinary Optimization","65","7","208","","",,1,"10.1007/s00158-022-03287-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133655282&doi=10.1007%2fs00158-022-03287-w&partnerID=40&md5=ab74d812dbe23230c97bc97eebc7a5ee","Coastal and Hydraulics Laboratory, Engineer Research Development Center, 3909 Halls Ferry Rd, Vicksburg, MS  39180, United States; Department of Structural Engineering, University of California, San Diego, 9500 Gilman Drive Mail Code 0085, La Jolla, CA  92093, United States; Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM  87545, United States; Department of Industrial and Manufacturing Systems Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI  48128, United States","Fillmore, T.B., Coastal and Hydraulics Laboratory, Engineer Research Development Center, 3909 Halls Ferry Rd, Vicksburg, MS  39180, United States; Wu, Z., Department of Structural Engineering, University of California, San Diego, 9500 Gilman Drive Mail Code 0085, La Jolla, CA  92093, United States; Vega, M.A., Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, NM  87545, United States; Hu, Z., Department of Industrial and Manufacturing Systems Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI  48128, United States; Todd, M.D., Department of Structural Engineering, University of California, San Diego, 9500 Gilman Drive Mail Code 0085, La Jolla, CA  92093, United States","Physics-based digital twins often require many computations to diagnose current and predict future damage states in structures. This research proposes a novel iterative global–local method, where the local numerical model is replaced with a surrogate to simulate cracking quickly on large steel structures. The iterative global–local method bridges the scales from the operational level of a large steel structure to that of a cracked component. The linear global domain is efficiently simulated using static condensation, and the cracked local domain is quickly simulated using the adaptive surrogate modeling method proposed herein. This work compares solution time and accuracy of the proposed surrogate iterative global–local method with a reference model, a submodeling model, and an iterative global–local method with no surrogate model for the local domain. It is found that the surrogate iterative global–local method gives the fastest solution time with comparatively accurate results. © 2022, The Author(s).","Crack; Digital twin; Global/local; Surrogate","Iterative methods; Numerical methods; 'current; Damage state; Global-local; Global-local methods; Large steel structures; Non-intrusive; Physics-based; Solution time; Surrogate; Surrogate modeling; Steel structures",,,,,"Engineer Research and Development Center, ERDC: W912HZ-17-2-0024; U.S. Army Corps of Engineers, USACE","All authors except Travis B. Fillmore received financial support from the United States Army Corps of Engineers through the US Army Engineer Research and Development Center Research Cooperative Agreement W912HZ-17-2-0024. This publication is approved for public release by Los Alamos National Laboratory (LA-UR-22-21052). Travis B. Fillmore is employed by the funding agency, the US Army Engineer Research and Development Center.",,,,,,,,,,"(2021) Abaqus: Abaqus Verification Guide, Dassault Systemes; Allix, O., Gosselet, P., Non intrusive global/local coupling techniques in solid mechanics: an introduction to different coupling strategies and acceleration techniques (2020) Modeling in engineering using innovative numerical methods for solids and fluids, pp. 203-220. , Lorenzis LDA, (ed), CISM International Centre for Mechanical Sciences, Cham; Barsoum, R.S., On the use of isoparametric finite elements in linear fracture mechanics (1976) Int J Numer Methods Eng, 10, pp. 25-37; Bjorstad, P.E., Widlund, O.B., Iterative methods for the solution of elliptic problems on regions partitioned into substructures (1986) SIAM J Numer Anal, 23, pp. 1097-1120; Chen, X., Chen, X., Zhou, W., Zhang, J., Yao, W., The heat source layout optimization using deep learning surrogate modeling (2020) Struct Multidisc Optim, 62 (6), pp. 3127-3148; 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Williams, C.K., Rasmussen, C.E., (2006) Gaussian processes for machine learning, , MIT Press, Cambridge; Wyart, E., Duflot, M., Coulon, D., Martiny, P., Pardoen, T., Remacle, J.-F., Lani, F., Substructuring FE-XFE approaches applied to three-dimensional crack propagation (2008) J Comput Appl Math, 215, pp. 626-638; Xu, S., Liu, H., Wang, X., Jiang, X., A robust error-pursuing sequential sampling approach for global metamodeling based on voronoi diagram and cross validation (2014) J Mech Des, 136 (7); Yan, S., Zou, X., Ilkhani, M., Jones, A., An efficient multiscale surrogate modelling framework for composite materials considering progressive damage based on artificial neural networks (2020) Compos Part B Eng, 194, p. 108014; Zhang, J., Taflanidis, A.A., Multi-objective optimization for design under uncertainty problems through surrogate modeling in augmented input space (2019) Struct Multidisc Optim, 59 (2), pp. 351-372","Todd, M.D.; Department of Structural Engineering, 9500 Gilman Drive Mail Code 0085, United States; email: mdtodd@ucsd.edu",,,"Springer Science and Business Media Deutschland GmbH",,,,,1615147X,,SMOTB,,"English","Struct. Mutltidiscip. Opt.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85133655282
"Rageh A., Azam S.E., Alomari Q., Linzell D., Wood R.","55635603300;57196712513;57954270900;6602678682;55377863400;","Model Updating and Parameter Identification for Developing Digital Twins for Riveted Steel Railway Bridges",2022,"Recent Developments In Structural Health Monitoring And Assessment - Opportunities And Challenges: Bridges, Buildings And Other Infrastructures",,,,"285","318",,1,"10.1142/9789811243011 0010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135915264&doi=10.1142%2f9789811243011+0010&partnerID=40&md5=1e6e8b832ed0fb090faf9f5fedf5d321","SDR Engineering Consultants, Inc., 2260 Wednesday St #500, Tallahassee, FL  32308, United States; Department of Civil and Environmental Engineering, University of New Hampshire, 33 Academic Way, Durham, NH  03824, United States; Department of Civil and Environmental Engineering, University of Nebraska - Lincoln, 114L Othmer Hall, Lincoln, NE  68588, United States","Rageh, A., SDR Engineering Consultants, Inc., 2260 Wednesday St #500, Tallahassee, FL  32308, United States; Azam, S.E., Department of Civil and Environmental Engineering, University of New Hampshire, 33 Academic Way, Durham, NH  03824, United States; Alomari, Q., Department of Civil and Environmental Engineering, University of Nebraska - Lincoln, 114L Othmer Hall, Lincoln, NE  68588, United States; Linzell, D., Department of Civil and Environmental Engineering, University of Nebraska - Lincoln, 114L Othmer Hall, Lincoln, NE  68588, United States; Wood, R., Department of Civil and Environmental Engineering, University of Nebraska - Lincoln, 114L Othmer Hall, Lincoln, NE  68588, United States","One of the significant concerns for maintaining railway network reliability is the structural health of aged steel riveted railway bridges, some of which in the United States were built at the beginning of the twentieth century. A reliable structural health monitoring (SHM) network that would anticipate riveted steel bridge deficiencies, optimize maintenance, and, ultimately, extend bridge service life to reduce railway network interruption would be a key component to assuring that the network remains viable. A class of SHM methods that can also potentially predict the remaining useful life of a structure to take advantage of a physics based model would provide a cost-effective option to current methods that commonly rely on extensive instrumentation. In this regard, the validity of the physical model, its parameterization, and accuracy in emulating actual response is of utmost importance for SHM prognosis of remaining useful life. In the context of Industry 4.0, those physics-based numerical replicas of the physical asset are called digital twins of the structure. This chapter presents and examines the eficacy of a frame-work for automated model calibration using measured operational and ambient structural response for the development of a precise digital twin of a physical structure. The guidelines provided in this chapter will assist in choosing the right model class for accurate response prediction. The study used an in-service, double-track, riveted steel plate girder railway bridge as a testbed for the proposed framework. © 2022 World Scientific Publishing Company. All rights reserved.",,,,,,,,,,,,,,,,,,"Arisoy, B., Erol, O., Finite element model calibration of a steel railway bridge via ambient vibration test (2018) Steel Compos. Struct., 27 (3), pp. 327-335; Bartilson, D.T., Jang, J., Smyth, A.W., Finite element model updating using objective-consistent sensitivity-based parameter clustering and Bayesian regularization (2019) Mech. Syst. Signal Process., 114, pp. 328-345; Brownjohn, J.M.W., Moyo, P., Omenzetter, P., Lu, Y., Assessment of highway bridge upgrading by dynamic testing and finite-element model updating (2003) J. Bridge Eng., 8 (3), pp. 162-172; Caglayan, O., Ozakgul, K., Tezer, O., Uzgider, E., Evaluation of a steel railway bridge for dynamic and seismic loads (2011) J. Cons. Steel Res., 67 (8), pp. 1198-1211; Caglayan, O., Ozakgul, K., Tezer, O., Assessment of existing steel railway bridges (2012) J. Cons. Steel Res., 69 (1), pp. 54-63; Chang, C.C., Chang, T., Xu, Y.G., Adaptive neural networks for model updating of structures (2000) Smart Mater. Struct., 9 (1), p. 59; Chen, X., Omenzetter, P., Beskhyroun, S., Calibration of the finite element model of a twelve-span prestressed concrete bridge using ambient vibration data Conference: 7th European Workshop on Structural Health Monitoring, pp. 1388-1395. , (Nantes, France, 2014); Costa, B.J., Figueiras, J.A., Rehabilitation and condition assessment of a centenary steel truss bridge (2013) J. Cons. Steel Res., 89, pp. 185-197; Costa, B.J.A., Magalhaes, F., Cunha, A., Figueiras, J., Modal analysis for the rehabilitation assessment of the Luiz I Bridge (2014) J. Bridge Eng., 19 (12). , 05014006-1-05014006-11; Chotickai, P., Kanchanalai, T., Field testing and performance evaluation of a through-plate girder railway bridge (2010) Transp. Res. Rec. :J. Transp. Res. Board, 2172 (1), pp. 132-141; Cunha, A., Caetano, E., Ribeiro, P., Modal analysis of the Jalon Viaduct using FE updating In Proceedings of the 9th International Conference on Structural Dynamics (EURODYN 2014), pp. 2311-2317. , (Porto, Portugal, July 2014); Feng, D., Feng, M.Q., Model updating of railway bridge using in situ dynamic displacement measurement under trainloads (2015) J. Bridge. Eng., 20 (12); He, X., Yu, Z., Chen, Z., Finite element model updating of existing steel bridge based on structural health monitoring (2008) J. Cent. South Univ. T, 15 (3), pp. 399-403; Jaishi, B., Ren, W., Structural finite element model updating using ambient vibration test results (2005) J. Struct. Eng., 131 (4), pp. 617-628; Lee, J.W., Kim, J.D., Yun, C.B., Yi, J.H., Shim, J.M., Healthmonitoring method for bridges under ordinary trafic loadings (2002) J. Sound Vib., 257 (2), pp. 247-264; Lee, Y., Cho, S., SHM-based probabilistic fatigue life prediction for bridges based on FE model updating (2016) Sensors, 16 (3), p. 317; Marques, F., Moutinho, C., Magalhaes, F., Caetano, E., Cunha, A., Analysis of dynamic and fatigue effects in an old metallic riveted bridge (2014) J. Cons. Steel Res., 99, pp. 85-101; Nagaraja, R., Material properties of structural carbon and high strength steels, , Lehigh University, Report No. 249.20, (1963); Parkinson, A.R., Balling, R., Hedengren, J.D., (2013) Optimization Methods for Engineering Design; Rageh, A., (2018) Optimized health monitoring plans for a steel, Double-Track Railway Bridge, , [Thesis], (University of Nebraska-Lincoln, NE, USA; Rageh, A., Linzell, D.G., Eftekhar Azam, S., Automated, strain-based, output-only bridge damage detection (2018) J. Civ. Struct. Health Monit., 8 (5), pp. 833-846; Ribeiro, D., Cal cada, R., Delgado, R., Brehm, M., Zabel, V., Finite element model updating of a bowstring-arch railway bridge based on experimental modal parameters (2012) Eng. Struct., 40, pp. 413-435; Sanayei, M., Khaloo, A., Gul, M., Catbas, F.N., Automated finite element model updating of a scale bridge model using measured static and modal test data (2015) Eng. Struct., 102, pp. 66-79; Schueller, W., Building Support Structures, Analysis and Design with SAP2000 Software (2008), (Computers and Structures Inc; Shabbir, F., Omenzetter, P., Model updating using genetic algorithms with sequential niche technique (2016) Eng. Struct., 120, pp. 166-182; Teughels, A., Maeck, J., De Roeck, G., Damage assessment by FE model updating using damage functions (2002) Comput. Struct., 80 (25), pp. 1869-1879; Tobias, D.H., Foutch, D.H., Choros, J., Investigation of an open deck through-truss railway bridge: Work train tests (1993), American Association of Railroads Research and Test Department, Report No. R-830; Xia, C., De Roeck, G., Modal analysis of the Jalon Viaduct using FE updating In Proceedings of the 9th International Conference on Structural Dynamics (EURODYN 2014), , (European Assoc Structural Dynamics, Porto, Portugal, July 2014); Zhong, R., Zong, Z., Niu, J., Yuan, S., A damage prognosis method of girder structures based on wavelet neural networks (2014) Math. Prob. Eng., p. 2014","Rageh, A.; SDR Engineering Consultants, 2260 Wednesday St #500, United States; email: arageh@sdrengineering.com",,,"World Scientific Publishing Co. Pte. Ltd.",,,,,,9789811243011,,,"English","Recent Developments In Structural Health Monitoring And assess. - Opportunities And Challenges: Bridges, Buildings And Other Infrastructures",Book Chapter,"Final","",Scopus,2-s2.0-85135915264
"Schnicke F., Kuhn T., Klausmann T., Gruner S., Porta D.","57209138657;7101840137;48361421400;57211157714;25825411300;","Architecture Blueprints for the Application of the Industry 4.0 Asset Administration Shell",2022,"IEEE International Conference on Emerging Technologies and Factory Automation, ETFA","2022-September",,,"","",,1,"10.1109/ETFA52439.2022.9921694","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141368604&doi=10.1109%2fETFA52439.2022.9921694&partnerID=40&md5=f4f9d7f78a1b7f858adcf6d56c0b2ccd","Fraunhofer Iese, Virtual Engineering, Kaiserslautern, Germany; Fraunhofer Iese, Embedded Systems, Kaiserslautern, Germany; Lenze Se, Research and Development - System Engineering, Aerzen, Germany; Abb Corporate Research Center Germany, Ladenburg, Germany; German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany","Schnicke, F., Fraunhofer Iese, Virtual Engineering, Kaiserslautern, Germany; Kuhn, T., Fraunhofer Iese, Embedded Systems, Kaiserslautern, Germany; Klausmann, T., Lenze Se, Research and Development - System Engineering, Aerzen, Germany; Gruner, S., Abb Corporate Research Center Germany, Ladenburg, Germany; Porta, D., German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany","The digitization of value chains is an ongoing challenge in production. The Asset Administration Shell (AAS) aims to address this issue. Besides standardization activities, there is however little architectural guidance on how to bridge the gap between potential AAS use-cases and realization.In this paper, we describe four AAS related use-cases that we derived from 15 application projects which adopted the AAS into industrial contexts. For each of the four use-cases, we devise an architecture blueprint that documents our experiences when applying the AAS. By utilizing these blueprints, practitioners can benefit from our experiences when implementing the AAS and bridge the gap between use-cases and implementation more easily. © 2022 IEEE.","architecture blueprint; architecture building blocks; asset administration shell; digital twin; industrial internet; industry 4.0; information architecture; information exchange; software architecture","Application programs; Atomic absorption spectrometry; Industry 4.0; Metadata; Architecture blueprint; Architecture building block; Asset administration shell; Building blockes; Digitisation; Industrial context; Industrial internet; Information architectures; Information exchanges; Value chains; Blueprints",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 01IS19022","This work is partially supported by German Federal Ministry of Education and Research in the scope of the BaSys 4.2 project (01IS19022).",,,,,,,,,,"Details of the Asset Administration Shell-Part 1: The Exchange of Information between Partners in the Value Chain of Industrie 4.0, , https://www.plattform-i40.de/PI40/Redaktion/DE/Downloads/Publikation/DetailsoftheAssetAdministrationShellPart1V3.html, Plattform Industrie 4.0, [Online]; Details of the Asset Administration Shell-Part 2: Interoperability at Runtime-Exchanging Information Via Application Programming Interfaces, , https://www.plattform-i40.de/PI40/Redaktion/DE/Downloads/Publikation/DetailsoftheAssetAdministrationShellPart2V1.html, [Online]; Recommendation 2020.01-The Digital Nameplate, , https://www.zvei.org/en/press-media/publications/zvei-recommendation-The-digital-nameplate, ZVEI, [Online]; Diedrich, C., Belyaev, A., Schröder, T., Vialkowitsch, J., Willmann, A., Usländer, T., Koziolek, H., Niggemann, O., Semantic interoperability for asset communication within smart factories (2017) 2017 22nd Ieee International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1-8; Heidel, R., Hoffmeister, M., Hankel, M., Döbrich, U., (2019) The Reference Architecture Model Rami 4.0 and the Industrie 4.0 Component., , VDE Verlag; (2003) Iec 62264-1 Enterprise-control System Integration-part 1: Models and Terminology, , I. 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Commission et al., IEC, Genf; Ngu, A.H., Gutierrez, M., Metsis, V., Nepal, S., Sheng, Q.Z., Iot middleware: A survey on issues and enabling technologies (2017) Ieee Internet of Things Journal, 4 (1), pp. 1-20; Razzaque, M.A., Milojevic-Jevric, M., Palade, A., Clarke, S., Middleware for internet of things: A survey (2016) Ieee Internet of Things Journal, 3 (1), pp. 70-95; Salazar Ch, G.D., Venegas, C., Baca, M., Rodríguez, I., Marrone, L., Open Middleware proposal for IoT focused on Industry 4.0 (2018) 2018 Ieee 2nd Colombian Conference on Robotics and Automation (CCRA), pp. 1-6; Kassner, L., Gröger, C., Königsberger, J., Hoos, E., Kiefer, C., Weber, C., Silcher, S., Mitschang, B., The Stuttgart IT Architecture for Manufacturing (2017) Enterprise Information Systems., pp. 53-80. , Springer International Publishing; Solution Architecture Concept, , https://openindustry4.com/fileadmin/Dateien/Downloads/OI4RANEU.pdf, Open Industrie 4.0 Alliance, [Online]; Trunzer, E., Calà, A., Leitão, P., Gepp, M., Kinghorst, J., Lüder, A., Schauerte, H., Vogel-Heuser, B., System architectures for Industrie 4.0 applications (2019) Production Engineering, 13 (3-4), pp. 247-257. , Apr; Wagner, C., Grothoff, J., Epple, U., Drath, R., Malakuti, S., Grüner, S., Hoffmeister, M., Zimermann, P., The role of the industry 4.0 asset administration shell and the digital twin during the life cycle of a plant (2017) 2017 22nd Ieee International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1-8; Schuh, G., Anderl, R., Dumitrescu, R., Krüger, A., Ten Hompel, M., (2020) Industrie 4.0 Maturity Index. Managing the Digital Transformation of Companies-Update 2020 (Acatech STUDY), , https://en.acatech.de/publication/industrie-4-0-maturity-index-update-2020, Accessed: 2022-02-14; Pakala, H.K., Belyaev, A., Diedrich, C., Middleware architecture for application layer interoperability of standardized digital representations (2021) Iecon 2021-47th Annual Conference of the Ieee Industrial Electronics Society, pp. 1-6; Schnicke, F., Espen, D., Antonino, P.O., Kuhn, T., Architecture blueprint enabling distributed digital twins (2021) 7th Conference on the Engineering of Computer Based Systems, pp. 1-10; Kuhn, T., Antonino, P.O., Damm, M., Morgenstern, A., Schulz, D., Ziesche, C., Müller, T., Industrie 4.0 virtual automation bus (2018) Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, Ser. Icse '18, pp. 121-122. , New York, NY, USA: ACM; (2005) Iec 61360-4-Common Data Dictionary (CDD-V2.0014.0017).; Platenius-Mohr, M., Malakuti, S., Grüner, S., Schmitt, J., Goldschmidt, T., File-And API-based interoperability of digital twins by model transformation: An IIoT case study using asset administration shell (2020) Future Generation Computer Systems, 113, pp. 94-105",,,,"Institute of Electrical and Electronics Engineers Inc.","27th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2022","6 September 2022 through 9 September 2022",,183811,19460740,9781665499965,85ROA,,"English","IEEE Int. Conf. Emerging Technol. Factory Autom., ETFA",Conference Paper,"Final","",Scopus,2-s2.0-85141368604
"Zhang C., Zhou G., Jing Y., Wang R., Chang F.","56203682300;35188416000;57712471100;55717703900;57194276590;","A Digital Twin-Based Automatic Programming Method for Adaptive Control of Manufacturing Cells",2022,"IEEE Access","10",,,"80784","80793",,1,"10.1109/ACCESS.2022.3195905","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135759799&doi=10.1109%2fACCESS.2022.3195905&partnerID=40&md5=6c3aebdf42f985a77868bb31ab8b7989","School of Mechanical Engineering, Xi'An Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'An Jiaotong University, Xi'an, 710054, China","Zhang, C., School of Mechanical Engineering, Xi'An Jiaotong University, Xi'an, 710049, China, State Key Laboratory for Manufacturing Systems Engineering, Xi'An Jiaotong University, Xi'an, 710054, China; Zhou, G., School of Mechanical Engineering, Xi'An Jiaotong University, Xi'an, 710049, China, State Key Laboratory for Manufacturing Systems Engineering, Xi'An Jiaotong University, Xi'an, 710054, China; Jing, Y., School of Mechanical Engineering, Xi'An Jiaotong University, Xi'an, 710049, China, State Key Laboratory for Manufacturing Systems Engineering, Xi'An Jiaotong University, Xi'an, 710054, China; Wang, R., School of Mechanical Engineering, Xi'An Jiaotong University, Xi'an, 710049, China, State Key Laboratory for Manufacturing Systems Engineering, Xi'An Jiaotong University, Xi'an, 710054, China; Chang, F., School of Mechanical Engineering, Xi'An Jiaotong University, Xi'an, 710049, China, State Key Laboratory for Manufacturing Systems Engineering, Xi'An Jiaotong University, Xi'an, 710054, China","The booming personalized and customized demands of customers in Industry 4.0 pose a great challenge for manufacturing enterprises in terms of flexibility and responsiveness. Nowadays, many effective dynamic scheduling approaches have been proposed for manufacturing systems to quickly respond to changes in customer demands, where, however, the implementation of an automatic programming method with high control accuracy and low control delay is still challenging. The above unaddressed issue brings about a lot of labor-intensive and time-consuming manual offline programming work when adjusting the scheduling scheme to meet dynamic customer demands, resulting in limited flexibility and responsiveness in current manufacturing systems. To bridge this gap, a bi-level adaptive control architecture enabled by an automatic programming method is proposed and embedded into a digital twin manufacturing cell (DTMC). The bi-level architecture aims to automatically map an input task scheduling scheme with a batch of jobs into a group of control programs through a behavior model network and a set of event models embedded in DTMC. It also provides an adaptive program modification mechanism to quickly adapt to the dynamic adjustment of the scheduling scheme caused by the changing of customer demands or production conditions, thus equipping DTMC with strong flexibility and responsiveness. Based on the bi-level architecture, a DTMC prototype system is developed, where its application and evaluation examples demonstrate the feasibility and effectiveness of the proposed method. © 2013 IEEE.","adaptive control; automatic programming; behavior model; Digital twin; event model; industry 4.0","Behavioral research; Flexible manufacturing systems; Industry 4.0; Job shop scheduling; Memory architecture; Network architecture; Sales; Software prototyping; Adaptation models; Adaptive Control; Aerospace electronics; Behaviour models; Customer demands; Dynamic scheduling; Event model; Job-Shop scheduling; Manufacturing; Adaptive control systems",,,,,"National Natural Science Foundation of China, NSFC: 51975463, 52105530; China Postdoctoral Science Foundation: 2021M692556; Shaanxi University of Science and Technology, SUST: 20210409","This work was supported in part by the National Natural Science Foundation of China under Grant 51975463 and Grant 52105530; in part by the China Postdoctoral Science Foundation under Grant 2021M692556; and in part by the Young Talent Fund of University Association for Science and Technology in Shaanxi, China, under Grant 20210409.",,,,,,,,,,"Chen, C.-C., Hung, M.-H., Li, P.-Y., Lin, Y.-C., Liu, Y.-Y., Cheng, F.-T., A novel automated construction scheme for efficiently developing cloud manufacturing services (2018) IEEE Robot. 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Syst., Man, Cybern., Syst., 49 (1), pp. 81-91. , Jan; Zhang, C., Zhou, G., He, J., Li, Z., Cheng, W., A data-and knowledgedriven framework for digital twin manufacturing cell (2019) Proc. CIRP, 83, pp. 345-350. , Jan; Dai, W., Pang, C., Vyatkin, V., Christensen, J.H., Guan, X., Discreteevent-based deterministic execution semantics with timestamps for industrial cyber-physical systems (2020) IEEE Trans. Syst., Man, Cybern., Syst., 50 (3), pp. 851-862. , Mar; Leng, J., Yan, D., Liu, Q., Xu, K., Zhao, J., Shi, R., Wei, L., Chen, X., ManuChain: Combining permissioned blockchain with a holistic optimization model as bi-level intelligence for smart manufacturing (2020) IEEE Trans. Syst., Man, Cybern., Syst., 50 (1), pp. 182-192. , Jan; Zhou, G., Zhang, C., Li, Z., Ding, K., Wang, C., Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing (2020) Int. J. Prod. 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Res., to Be Published; Ding, D., Han, Q.-L., Ge, X., Wang, J., Secure state estimation and control of cyber-physical systems: A survey (2021) IEEE Trans. Syst., Man, Cybern., Syst., 51 (1), pp. 176-190. , Jan; Zhang, C., Zhou, G., Li, H., Cao, Y., Manufacturing blockchain of things for the configuration of a data-and knowledge-driven digital twin manufacturing cell (2020) IEEE Internet Things J., 7 (12), pp. 11884-11894. , Dec; Zhou, G., Chen, Z., Zhang, C., Chang, F., An adaptive ensemble deep forest based dynamic scheduling strategy for low carbon flexible job shop under recessive disturbance (2022) J. Cleaner Prod., 337. , Feb., Art. no. 130541; Angrish, A., Starly, B., Lee, Y.-S., Cohen, P.H., A flexible data schema and system architecture for the virtualization of manufacturing machines (VMM) (2017) J. Manuf. 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Surveys Tuts., 19 (3), pp. 1657-1681. , 3rd Quart","Zhou, G.; School of Mechanical Engineering, China; email: ghzhou@mail.xjtu.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85135759799
"Hosamo H.H., Hosamo M.H.","57222252101;57821152300;","Digital Twin Technology for Bridge Maintenance using 3D Laser Scanning: A Review",2022,"Advances in Civil Engineering","2022",,"2194949","","",,1,"10.1155/2022/2194949","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135059036&doi=10.1155%2f2022%2f2194949&partnerID=40&md5=84672885aa5cbb405f0581b333c3b0c2","University of Agder, Jon Lilletuns vei 9, Grimstad, 4879, Norway; HCO Cyber Security, Advania, Oslo, Norway","Hosamo, H.H., University of Agder, Jon Lilletuns vei 9, Grimstad, 4879, Norway; Hosamo, M.H., HCO Cyber Security, Advania, Oslo, Norway","There has been a significant surge in the interest in adopting cutting-edge new technologies in the civil engineering industry in recent times that monitor the Internet of Things (IoT) data and control automation systems. By combining the real and digital worlds, digital technologies, such as Digital Twin, provide a high-level depiction of bridges and their assets. The inspection, evaluation, and management of infrastructure have experienced profound changes in technological advancement over the last decade. Technologies like laser scanners have emerged as a viable replacement for labor-intensive, costly, and dangerous traditional methods that risk health and safety. The new maintenance techniques have increased their use in the construction section, particularly regarding bridges. This review paper aims to present a comprehensive and state-of-the-art review upon using laser scanners in bridge maintenance and engineering and looking deeper into the study field in focus and researchers' suggestions in this field. Moreover, the review was conducted to gather, evaluate, and analyze the papers collected in the years from 2017 to 2022. The interaction of research networks, dominant subfields, the co-occurrence of keywords, and countries were all examined. Four main categories were presented, namely machine learning, bridge management system (BMS), bridge information modeling (BrIM), and 3D modeling. The findings demonstrate that information standardization is the first significant obstacle to be addressed before the construction sector can benefit from the usage of Digital Twin. As a result, this article proposes a conceptual framework for building management using Digital Twins as a starting point for future research. © 2022 Haidar Hosamo Hosamo and Mohsen Hosamo Hosamo.",,,,,,,,,,,,,,,,,,"Barnes, P., (2019) BIM in Principle and in Practice, , London ICE publishing; Lu, Q., Xie, X., Heaton, J., Parlikad, A.K., Schooling, J., Borangiu, T., Trentesaux, D., Botti, V., From BIM towards digital twin: Strategy and future development for smart asset management (2020) Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future, 853, pp. 392-404. , Cham Springer International Publishing; Grieves, M., Vickers, J., Kahlen, J., Flumerfelt, S., Alves, A., Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems (2017) Transdisciplinary Perspectives on Complex Systems, , Cham Springer 2-s2.0-85006339863; Angjeliu, G., Coronelli, D., Cardani, G., Development of the simulation model for Digital Twin applications in historical masonry buildings: The integration between numerical and experimental reality (2020) Computers & Structures, 238. , 106282; Jones, D., Snider, C., Nassehi, A., Yon, J., Hicks, B., Characterising the Digital Twin: A systematic literature review (2020) CIRP Journal of Manufacturing Science and Technology, 29, pp. 36-52; Rasheed, A., San, O., Kvamsdal, T., Digital twin: Values, challenges and enablers from a modeling perspective (2020) IEEE Access, 8, p. 22012. , 21980; Hosamo, H.H., Svennevig, P.R., Svidt, K., Han, D., Nielsen, H.K., A Digital Twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics (2022) Energy and Buildings, 261. , 111988; Hosamo, H.H., Imran, A., Cardenas-Cartagena, J., Svennevig, P.R., Svidt, K., Nielsen, H.K., A review of the digital twin technology in the AEC-FM industry (2022) Advances in Civil Engineering, 2022. , e2185170; (2020) Introduction to Theme 1: Testing Digital Twin Concepts, , https://digitaltwinhub.co.uk/forums/topic/90-introduction-to-theme-1-testing-digital-twin-concepts/; Grieves, M.W., Product lifecycle management: The new paradigm for enterprises (2005) International Journal of Product Development, 2 (12), pp. 71-84; Hou, L., Zhao, S., Xiong, X., Zheng, K., Chatzimisios, P., Hossain, M.S., Xiang, W., Internet of things cloud: Architecture and implementation (2016) IEEE Communications Magazine, 54 (12), pp. 32-39. , 2-s2.0-85007490372; Yang, X., Moore, P., Chong, S.K., Intelligent products: From lifecycle data acquisition to enabling product-related services (2009) Computers in Industry, 60 (3), pp. 184-194. , 2-s2.0-60549114835; Tao, F., Zhang, H., Liu, A., Nee, A.Y.C., Digital twin in industry: State-of-the-art (2019) IEEE Transactions on Industrial Informatics, 15, pp. 2405-2415; (2017) ASCE's 2021 American Infrastructure Report Card | GPA: C-, , https://www.asce.org/publications-and-news/civil-engineering-source/article/2021/03/03/2021-report-card-for-americas-infrastructure-grades-reveal-widening-investment-gap; Virlogeux, M., Structural and architectural design of bridges (1996) Structural Engineering International, 6 (2), pp. 80-83; Parke, G.A.R., Disney, P., Bridge Management 5: Inspection, Maintenance, Assessment and Repair, , Proceedings of the 5th International Conference on Bridge Management, organized by the University of Surrey 2005 London, UK; Ren, G., Ding, R., Li, H., Building an ontological knowledgebase for bridge maintenance (2019) Advances in Engineering Software, 130, pp. 24-40. , 2-s2.0-85062474774; Edlin, P., Edlin, R., Is it really possible to build a bridge between cost-benefit analysis and cost-effectiveness analysis? 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Civ. Eng.",Review,"Final","All Open Access, Gold",Scopus,2-s2.0-85135059036
"Saback de Freitas Bello V., Popescu C., Blanksvärd T., Täljsten B.","57388414800;56272949500;20336636900;8703323300;","Framework for Bridge Management Systems (BMS) Using Digital Twins",2022,"Lecture Notes in Civil Engineering","200 LNCE",,,"687","694",,1,"10.1007/978-3-030-91877-4_78","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121920211&doi=10.1007%2f978-3-030-91877-4_78&partnerID=40&md5=6fdff59ca9db3312e752a9205198f0a7","Luleå University of Technology (LTU), Luleå, Sweden; SINTEF Narvik AS, Narvik, 8517, Norway","Saback de Freitas Bello, V., Luleå University of Technology (LTU), Luleå, Sweden; Popescu, C., Luleå University of Technology (LTU), Luleå, Sweden, SINTEF Narvik AS, Narvik, 8517, Norway; Blanksvärd, T., Luleå University of Technology (LTU), Luleå, Sweden; Täljsten, B., Luleå University of Technology (LTU), Luleå, Sweden","Bridge structures have significantly long life spans; many medieval and historic bridges remain in operation in the world. The concept of bridge management contains the activities related to managing bridge inspections and condition assessment, which can be gathered into a Bridge Management System (BMS). Deterioration and failures have increased over the years in the already aging bridges; therefore, the importance of BMS to ensure safety of bridge operation and maximize investments in bridge maintenance has also increased. Digital Twin (DT) technology can be applied in the construction industry to achieve smart management through the entire life cycle of structures. Unlike the aerospace and manufacturing industries, the maturity of development of DT models in the construction industry still lags behind. In this study, a literature review was initially performed to gather knowledge on the origins of the digital twin concept and current best practice focused on bridge structures. A systematic approach for the literature review is presented in the methodology. Lastly, a framework for facility management of bridge structures using digital twins is proposed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.","BMS; Bridge management systems; Bridges; Digital twins; Facility management","Bridges; Construction industry; Deterioration; Life cycle; Bridge inspection; Bridge management; Bridge management system; Bridge structures; Facilities management; Historic bridges; Lifespans; Literature reviews; Long life; Office buildings",,,,,"Energimyndigheten","This work was carried out within the strategic innovation program InfraSweden2030, a joint venture by Vinnova, Formas and The Swedish Energy Agency, the work is also funded by SBUF (construction industry’s organisation for research and development in Sweden) and Skanska Sweden.",,,,,,,,,,"Khan, M.A., Recent developments in ABC concepts (2015) Accelerated Bridge Construction, pp. 53-102. , Khan MA, Butterworth-Heinemann, Boston, pp, ISBN 978-0-12-407224-4; Mirzaei, Z., Adey, B.T., Klatter L (2014) Thompson P (2014) the IABMAS Bridge Management Committee Overview of Existing Bridge Management Systems; Morgenthal, G., Framework for automated UAS-based structural condition assessment of bridges (2019) Autom Constr, 97, pp. 77-95; Isailovic, D., Stojanovic, V., Trapp, M., Richter, R., Hajdin, R., Döllner, J., Bridge damage: Detection, IFC-based semantic enrichment and visualization (2020) Autom Constr, 112; Popescu C, Täljsten B, Blanksvärd T, Elfgren L (2019) 3D reconstruction of existing concrete bridges using optical methods. Struct Infrastruct Eng (2019). https://doi.org/10.1080/157 32479.2019.1594315; Riveiro, B., Jauregui, D.V., Arias, P., Armesto, J., Jiang, R., An innovative method for remote measurement of minimum vertical under clearance in routine bridge inspection (2012) Autom Constr, 25, pp. 34-40; Huthwohl, P., Brilakis, I., Borrmann, A., Sacks, R., Integrating RC bridge defect information into BIM models (2018) J Comput Civil Eng, 32 (3), pp. 04018013-4018021; Ban, F., Barazzetti, L., Previtali, M., Roncoroni, F., Historic BIM: A new repository for structural health monitoring (2017) International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences-Isprs Archives, 42, pp. 269-274. , pp; Cha G, Park S, Oh T (2019) A terrestrial LiDAR-based detection of shape deformation for maintenance of bridge structures. J Constr Eng Manag 145(12) (04019075):04019075–1– 04019075–12; Borin, P., Cavazzini, F., Condition assessment of RC bridges. Integrating machine learning, photogrammetry and BIM (2019) International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences-Isprs Archives, 42, pp. 201-208. , pp; Barazzetti, L., Ban, F., Brumana, R., Previtali, M., Roncoroni, F., BIM from laser scans… not just for buildings: NURBSbased parametric modeling of a medieval bridge (2016) ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3-5, pp. 51-56. , pp; Stavroulaki, M.E., Riveiro, B., Drosopoulos, G.A., Solla, M., Koutsianitis, P., Stavroulakis, G.E., Modelling and strength evaluation of masonry bridges using terrestrial photogrammetry and finite elements (2016) Adv Eng Softw, , https://doi.org/10.1016/j.advengsoft.2015.12.007; Popescu, C., Optical methods and wireless sensors for monitoring of bridges. In: IABSE symposium (2019) Guimaraes 2019: Towards a Resilient Built Environment Risk and Asset Management; McKenna, T., Minehane, M., O’Keefe, B., O’Sullivan, G., Ruane, K., Bridge information modelling (BrIM) for a listed viaduct (2017) In: Proceedings of the Institution of Civil Engineers: Bridge Engineering (1600007); León-Robles, C.A., Reinoso-Gordo, J.F., González-Quiñones, J.J., Heritage building information modeling (H-BIM) applied to a stone bridge (2019) ISPRS Int J Geo-Inf, 8, p. 121; Riveiro, B., González-Jorge, H., Varela, M., Jauregui, D.V., Validation of terrestrial laser scanning and photogrammetry techniques for the measurement of vertical under clearance and beam geometry in structural inspection of bridges (2013) Measurement, 46, pp. 784-794; Alani, A.M., Aboutalebi, M., Kilic, G., Integrated health assessment strategy using NDT for reinforced concrete bridges (2014) NDT E Int, 61, pp. 80-94; Abu Dabous, S., Yaghi, S., Alkass, S., Moselhi, O., (2015) Concrete Bridge Deck Condition Assessment Using IR Thermography and Ground Penetrating Radar Technologies, , 5th international/11th construction specialty conference (262; Conde, B., Ramos, L.F., Oliveira, D.V., Riveiro, B., Solla, M., Structural assessment of masonry arch bridges by combination of non-destructive testing techniques and three-dimensional numerical modelling: Application to Vilanova Bridge (2017) Eng Struct, 148, pp. 621-638; Xu, Y., Turkan, Y., BrIM and UAS for bridge inspections and management (2019) Eng Constr Archit Manag, 27 (3), pp. 785-807; Delgado, J.M.D., Brilakis, I., Middleton, C., Modelling, management, and visualization of structural performance monitoring data on BIM (2016) Proceedings of the International Conference on Smart Infrastructure and Construction, ICSIC, 2016, pp. 543-549. , pp; Davila Delgado, J.M., Butler, L.J., Gibbons, N., Brilakis, I., Elshae, M.Z.E., Middleton, C., Management of structural monitoring data of bridges using BIM (2017) Bridge Eng, 170 (10), pp. 204-218; Sacks, R., SeeBridge as next generation bridge inspection: Overview, information delivery manual and model view definition (2018) Autom Constr, 90, pp. 134-145; Omer, M., Margetts, L., Hadi Mosleh, M., Hewitt, S., Parwaiz, M., Use of gaming technology to bring bridge inspection to the office (2019) Struct Infrastruct Eng, 15 (10), pp. 1292-1307; Zhao, Z., Integrating BIM and IoT for smart bridge management (2019) IOP Conf Ser Earth Environ Sci 371(022034) (2019); Khajavi, S.H., Motlagh, N.H., Jaribion, A., Werner, L.C., Holmstrom, J., Digital twin: Vision, benefits, boundaries, and creation for buildings (2019) IEEE Access, 7 (10), pp. 147406-147419; Abu Dabous, S., Yaghi, S., Alkass, S., Moselhi, O., Concrete bridge deck condition assessment using IR thermography and ground penetrating radar technologies (2017) Autom Constr, 81, pp. 340-354; McGuire, B., Atadero, R., Clevenger, C., Ozbek, M., Bridge information modeling for inspection and evaluation (2016) J Bridge Eng, 21 (4); Boddupalli, C., Sadhu, A., Rezazadeh Azar, E., Pattyson, S., Improved visualization of infrastructure monitoring data using building information modeling (2019) Struct Infrastruct Eng, 15 (9), pp. 1247-1263; Lu, R., Brilakis, I., Digital twinning of existing reinforced concrete bridges from labelled point clusters (2019) Autom Constr, 105; Wan, C., Development of a bridge management system based on the building information modeling technology (2019) Sustainability, 11 (4583); Zhu, J., Tan, Y., Wang, X., Wu, P., BIM/GIS integration for web GIS-based bridge management (2020) Ann GIS, , https://doi.org/10.1080/19475683.2020.1743355; Lu, Q., Xie, X., Heaton, J., Parlikad, A.K., Schooling, J., From BIM towards digital twin: Strategy and future development for smart asset management (2020) Stud Comput Intell, 853, pp. 392-404; Cimino, C., Negri, E., Fumagalli, L., Review of digital twin applications in manufacturing (2019) Comput Ind, 113; Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W., Digital twin in manufacturing: A categorical literature review and classification (2018) IFAC Papers Online, 51 (11), pp. 1016-1022; Andersen, J.E., Rex, S., Concrete bridge deck condition assessment using IR thermography and ground penetrating radar technologies. In: 20th congress of IABSE (2019) New York City 2019: The Evolving Metropolis; Shim, C.-S., Dang, N.-S., Lon, S., Jeon, C.-H., Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model (2019) Struct Infrastruct Eng, 15 (10), pp. 1319-1332; Ye, C., A digital twin of bridges for structural health monitoring (2019) In: Proceedings of the 12Th International Workshop on Structural Health Monitoring (2019)","Saback de Freitas Bello, V.; Luleå University of Technology (LTU)Sweden; email: vanessa.saback.de.freitas@ltu.se","Pellegrino C.Faleschini F.Zanini M.A.Matos J.C.Casas J.R.Strauss A.",,"Springer Science and Business Media Deutschland GmbH","1st Conference of the European Association on Quality Control of Bridges and Structures, EUROSTRUCT 2021","29 August 2021 through 1 September 2021",,269849,23662557,9783030918767,,,"English","Lect. Notes Civ. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85121920211
"Zhang J., Liu Y., Qin X., Xu X.","57321787900;57191159228;56022330600;55568520270;","Energy-Efficient Federated Learning Framework for Digital Twin-Enabled Industrial Internet of Things",2021,"IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC","2021-September",,,"1160","1166",,1,"10.1109/PIMRC50174.2021.9569716","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118468217&doi=10.1109%2fPIMRC50174.2021.9569716&partnerID=40&md5=c203331afd8937d71e2e493f5a54e996","Beijing University of Posts and Telecommunications, State Key Laboratory of Networking and Switch Technology, Beijing, China; Beijing University of Posts and Telecommunications, National Engineering Laboratory for Mobile Network Technologies, Beijing, China; Beijing University of Posts and Telecommunications, Beijing, China","Zhang, J., Beijing University of Posts and Telecommunications, State Key Laboratory of Networking and Switch Technology, Beijing, China, Beijing University of Posts and Telecommunications, Beijing, China; Liu, Y., Beijing University of Posts and Telecommunications, State Key Laboratory of Networking and Switch Technology, Beijing, China, Beijing University of Posts and Telecommunications, Beijing, China; Qin, X., Beijing University of Posts and Telecommunications, State Key Laboratory of Networking and Switch Technology, Beijing, China, Beijing University of Posts and Telecommunications, Beijing, China; Xu, X., Beijing University of Posts and Telecommunications, National Engineering Laboratory for Mobile Network Technologies, Beijing, China, Beijing University of Posts and Telecommunications, Beijing, China","The digital twin (DT) bridges the physical world with the digital world in real-time for the Industrial Internet of Things (IIoT) and federated learning (FL) enables edge intelligence services for IIoT under the premise of avoiding privacy leakage. The fusion of two technologies can extremely accelerate the development of Industry 4.0 by enabling instant intelligence services. However, in the resource-constrained IIoT, the energy consumption of performing FL and maintaining the virtual object in the digital space by DT technology become the bottlenecks and can not be ignored. To address these issues, in this paper, we proposed an energy-efficient FL framework for DT-enabled IIoT. In the proposed framework, IIoT devices choose different training methods considering dynamic time-varying environment status to achieve energy-efficient FL, i.e., either train locally or connect to the virtual object by DT in the corresponding server of a small base station (SBS) to train mapped data using computing resources of SBS. Then, we investigate the joint training method selection and resource allocation problem to minimize the energy consumption while satisfying the convergence rate of the training model. Considering the problem is intractable using traditional approaches, we use a deep reinforcement learning (DRL)-based algorithm to solve it. Simulation results show that the proposed framework decreases greatly energy consumption compared with the static framework while satisfying the convergence rate of FL. © 2021 IEEE.","digital twin (DT); Energy efficiency; federated learning (FL); Industrial Internet of Things (IIoT)","Deep learning; E-learning; Energy utilization; Internet of things; Reinforcement learning; Digital twin; Energy efficient; Energy-consumption; Federated learning; Industrial internet of thing; Intelligence services; Learning frameworks; Physical world; Training methods; Virtual objects; Energy efficiency",,,,,"National Natural Science Foundation of China, NSFC: 62001050; Natural Science Foundation of Beijing Municipality: L192033","This work was supported in part by the National Natural Science Foundation for Young Scientists of China under Grant No. 62001050 and Beijing Natural Science Foundation under Grant No. L192033.",,,,,,,,,,"Stoyanova, M., Nikoloudakis, Y., Panagiotakis, S., Pallis, E., Markakis, E.K., A survey on the internet of things (iot) forensics: Challenges, approaches, and open issues (2020) IEEE Comm. Surveys and Tutorials, 22 (2), pp. 1191-1221. , Second quarter; Alam, T., A reliable communication framework and its use in internet of things (iot) (2018) International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), 3 (5); Dang, S., Amin, O., Shihada, B., Alouini, M.-S., What should 6g be (2020) Nature Electronics, 3 (1), pp. 20-29; Sun, W., Lei, S., Wang, L., Liu, Z., Zhang, Y., Adaptive federated learning and digital twin for industrial internet of things IEEE Trans. Industrial Informatics; Lameh, S.F., Noble, W., Amannejad, Y., Afshar, A., Analysis of federated learning as a distributed solution for learning on edge devices (2020) 2020 International Conference on Intelligent Data Science Technologies and Applications, pp. 66-74. , Valencia, Spain; Sun, H., Li, S., Yu, F.R., Qi, Q., Wang, J., Liao, J., Toward communication-efficient federated learning in the internet of things with edge computing (2020) IEEE Internet of Things Journal, 7 (11), pp. 11053-11067. , Nov; Lu, Y., Huang, X., Dai, Y., Maharjan, S., Zhang, Y., Differentially private asynchronous federated learning for mobile edge computing in urban informatics (2020) IEEE Trans. Industrial Informatics, 16 (3), pp. 2134-2143. , March; Lu, Y., Huang, X., Zhang, K., Maharjan, S., Zhang, Y., Low-time federated learning and blockchain for edge association in digital twin empowered 6g networks IEEE Trans. Industrial Informatics; Zeng, Q., Du, Y., Huang, K., Leung, K.K., Energy-efficient radio resource allocation for federated edge learning (2020) 2020 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1-6. , Dublin, Ireland; Guo, F., Yu, F.R., Zhang, H., Ji, H., Liu, M., Leung, V.C.M., Adaptive resource allocation in future wireless networks with blockchain and mobile edge computing (2020) IEEE Trans. Wireless Communications, 19 (3), pp. 1689-1703. , March; Volodymyr, M., Koray, K., David, S., Joel, V., Alex, G., Martin, R., Georg, O., Human-level control through deep reinforcement learning (2015) Nature, 518 (7540), p. 529; Hasselt, H.V., Double q-learning (2010) Advances in Neural Information Processing Systems, 23, pp. 2613-2621; Wang, Z., De Freitas, N., Lanctot, M., (2015) Dueling Network Architectures for Deep Reinforcement Learning, , CoRR. Abs/1511. 06581",,,,"Institute of Electrical and Electronics Engineers Inc.","32nd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2021","13 September 2021 through 16 September 2021",,173140,,9781728175867,,,"English","IEEE Int Symp Person Indoor Mobile Radio Commun PIMRC",Conference Paper,"Final","",Scopus,2-s2.0-85118468217
"Baron J.M., Salinas G., Mo X., Vergara F., Arnaiz P.J., Alou P., Vasic M.","57222720098;57204116610;57222725277;57222723293;57222725872;56249124100;24922369500;","Methodology for multi-die package semiconductor Thermal Model in a Dynamic Environment",2021,"2021 28th International Workshop on Electric Drives: Improving Reliability of Electric Drives, IWED 2021 - Proceedings",,,"9376341","","",,1,"10.1109/IWED52055.2021.9376341","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103847804&doi=10.1109%2fIWED52055.2021.9376341&partnerID=40&md5=e3ce38414beccc59873dbcfcac961b1c","Centro de Electrónica Industrial, Polytechnic University of Madrid, Madrid, Spain; European Space Agency, Noordwijk, Netherlands; Fagor Automation Mondragón, Spain","Baron, J.M., Centro de Electrónica Industrial, Polytechnic University of Madrid, Madrid, Spain; Salinas, G., European Space Agency, Noordwijk, Netherlands; Mo, X., Centro de Electrónica Industrial, Polytechnic University of Madrid, Madrid, Spain; Vergara, F., Fagor Automation Mondragón, Spain; Arnaiz, P.J., Fagor Automation Mondragón, Spain; Alou, P., Centro de Electrónica Industrial, Polytechnic University of Madrid, Madrid, Spain; Vasic, M., Centro de Electrónica Industrial, Polytechnic University of Madrid, Madrid, Spain","This paper provides a detailed methodology for the development of a dynamic thermal model based on the finite element method and statistical modelling. Finite element method based thermal modelling, consisting of a nonphysical model, is improved by means of statistical correlations to obtain accurate temperature estimates in response to dynamic boundary conditions, in contrast to the classical thermal models which are very dependent on the boundary conditions such as Cauer and Foster network, thus allowing it application for digital twinning and device failure reporting. The dynamic thermal model has been developed for a TO-247 IGBT device and, later, benchmarked and verified against measurements obtained from an experimental platform, composed of three IGBT half-bridge mounted on a single heatsink with forced air cooling. © 2021 IEEE.","Digital Twin; Finite element analysis; Semiconductor devices; Statistical analysis; Thermal analysis","Boundary conditions; Electric drives; Insulated gate bipolar transistors (IGBT); Thermography (temperature measurement); Dynamic boundary conditions; Dynamic environments; Dynamic thermal modeling; Experimental platform; Forced air cooling; Statistical correlation; Statistical modelling; Thermal modelling; Finite element method",,,,,,,,,,,,,,,,"Wang, Z., Tian, B., Qiao, W., Qu, L., Real-time aging monitoring forigbt modules using case temperature (2015) Ieee Transactions on IndustrialElectronics, 63 (2), pp. 1168-1178; Drofenik, U., Kolar, J.W., Teaching thermal design of powerelectronic systems with web-based interactive educational software (2003) Eighteenth Annual Ieee Applied Power Electronics Conference and Exposition, 2003. APEC'03., 2, pp. 1029-1036; Cengel, Y., (2020) Heat and Mass Transfer: Fundamentalsand Applications, , McGraw-Hill Higher Education; Bergman, T.L., Lavine, A.S., Incropera, F.P., Dewitt, D.P., (2012) Fundamentals of Heat and Mass Transfer 7th Ed, , John Wiley & SonsNew York; Sabatino, D., Yoder, K., Pyrolytic graphite heat sinks: A study of cir-cuit board applications (2014) Ieee Transactions on Components, Packagingand Manufacturing Technology, 4 (6), pp. 999-1009; Culham, J., Teertstra, P., Yovanovich, M., The role of spreadingresistance on effective conductivity in laminated substrates (2000) FutureCircuits, 6, pp. 73-78; Li, H., Hu, Y., Liu, S., Li, Y., Liao, X., Liu, Z., An improved thermalnetwork model of the igbt module for wind power converters consideringthe effects of base-plate solder fatigue (2016) Ieee Transactions on Deviceand Materials Reliability, 16 (4), pp. 570-575; Bahman, A.S., Ma, K., Ghimire, P., Iannuzzo, F., Blaabjerg, F., A3-d-lumped thermal network model for long-term load profiles analysisin high-power igbt modules (2016) Ieee Journal of Emerging and SelectedTopics in Power Electronics, 4 (3), pp. 1050-1063; Salinas, G., (2020) Thermal Modelling of High-frequency Magnetic Componentsfor Power Electronics by Finite Element Analysis, , Ph.D. dissertation, UPM ETSII Madrid; Wu, R., Iannuzzo, F., Wang, H., Blaabjerg, F., (2014) Fast and Accurateicepak-pspice Co-simulation of Igbts under Short-circuit with An Ad-vanced Pspice Model; Alavi, O., Abdollah, M., Viki, A.H., Thermal optimization ofigbt modules based on finite element method and particle swarmoptimization (2017) Journal of Computational Electronics, 16 (3), pp. 930-938; Jiang, M., Fu, G., Ceccarelli, L., Du, H., Fogsgaard, M.B., Bahman, A.S., Yang, Y., Iannuzzo, F., Finite element modelling of igbt modules to explore the correlation between electric parameters and damage inbond wires (2019) 2019 Ieee Energy Conversion Congress and Exposition (ECCE). Ieee, pp. 839-844; Maheswari, L., Anand, M., (2009) Analog Electronics, , PHI Learning Pvt.Ltd; Poppe, A., Zhang, Y., Wilson, J., Farkas, G., Szabo, P., Parry, J., Rencz, M., Szekely, V., Thermal measurement and modelling of multi-die pack-ages (2009) Ieee Transactions on Components and Packaging Technologies, 32 (2), pp. 484-492",,,,"Institute of Electrical and Electronics Engineers Inc.","28th International Workshop on Electric Drives: Improving Reliability of Electric Drives, IWED 2021","27 January 2021 through 29 January 2021",,168036,,9780738144436,,,"English","Int. Workshop Electr. Drives: Improv. Reliab. Electr. Drives, IWED - Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85103847804
"Mafipour M.S., Vilgertshofer S., Borrmann A.","57211838747;57188750778;14824718700;","Deriving Digital Twin Models of Existing Bridges from Point Cloud Data Using Parametric Models and Metaheuristic Algorithms",2021,"EG-ICE 2021 Workshop on Intelligent Computing in Engineering, Proceedings",,,,"464","474",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133514832&partnerID=40&md5=0e0643bbc13b19489569ec2e331c00eb","Technical University of Munich, Germany","Mafipour, M.S., Technical University of Munich, Germany; Vilgertshofer, S., Technical University of Munich, Germany; Borrmann, A., Technical University of Munich, Germany","In building information modeling (BIM), a digital twin (DT) is a model that represents the current status of an existing structure; thus, facilitating the operation and management process. Due to higher measurement speed and accuracy, laser scanning and photogrammetry are generally employed, resulting in point cloud data (PCD). Today, the required volumetric models are created in a laborious and costly manual process from PCD. This paper aims to automate this process by applying metaheuristic optimization algorithms to fit highly parametric BIM models of bridges into given point clouds. For this purpose, parametric base models of elements are created and instantiated by adjusting their parameters' value using metaheuristic algorithms. This optimization process leads to extracting the parameters for a model from PCD and creating 3-D volumetric shapes. The paper's results show that metaheuristic algorithms can be successfully used for parametric modeling even in point clouds with occlusion and clutter. © 2021 Universitätsverlag der Technischen Universität Berlin. All Rights Reserved.",,"Architectural design; Information management; Optimization; Three dimensional computer graphics; Building Information Modelling; Current status; Existing bridge; Existing structure; In-buildings; Meta-heuristics algorithms; Model algorithms; Parametric models; Point cloud data; Point-clouds; Parameter estimation",,,,,"Bundesministerium für Verkehr und Digitale Infrastruktur, BMVI","The research presented has been performed in the profile of the TwinGen project funded by the German Ministry of Transport and Digital Infrastructure (BMVI).",,,,,,,,,,"Adán, A., Quintana, B., Scan-to-BIM for 'secondary'building components (2018) Advanced Engineering Informatics, 37, pp. 119-138; Bosché, F., Ahmed, M., The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components (2015) Automation in Construction, 49, pp. 201-213; Cao, C., Wang, G., Fitting Cuboids from the Unstructured 3D Point Cloud (2019) International Conference on Image and Graphics, , http://dx.doi.org/10.1007/978-3-030-34110-7_16, Springer; Kennedy, J., Eberhart, R. C., A discrete binary version of the particle swarm algorithm (1997) 1997 IEEE International conference on systems, man, and cybernetics. Computational cybernetics and simulation, , IEEE; Kwon, S.-W., Bosche, F., Fitting range data to primitives for rapid local 3D modeling using sparse range point clouds (2004) Automation in construction, 13 (1), pp. 67-81. , http://dx.doi.org/10.1016/j.autcon.2003.08.007; Laing, R., Leon, M., Scan to BIM: the development of a clear workflow for the incorporation of point clouds within a BIM environment (2015) WIT Transactions on The Built Environment, 149, pp. 279-289; Lu, Q., Chen, L., Semi-automatic geometric digital twinning for existing buildings based on images and CAD drawings (2020) Automation in Construction, 115, p. 103183; Lu, R., Brilakis, I., Detection of structural components in point clouds of existing RC bridges (2019) Computer Aided Civil and Infrastructure Engineering, 34 (3), pp. 191-212. , http://dx.doi.org/10.1111/mice.12407; Pan, Y., Borrmann, A., Built Environment Digital Twinning (2019) Report of the International Workshop on Built Environment Digital Twinning presented by TUM Institute for Advanced Study and Siemens AG, , Technical University of Munich, Germany; Qin, G., Zhou, Y., Automated Reconstruction of Parametric BIM for Bridge Based on Terrestrial Laser Scanning Data (2021) Advances in Civil Engineering, 2021. , https://doi.org/10.1155/2021/8899323; Rocha, G., Mateus, L., A scan-to-BIM methodology applied to heritage buildings (2020) Heritage, 3 (1), pp. 47-67; Sacks, R., Kedar, A., (2016) SeeBridge information delivery manual (IDM) for next generation bridge inspection, , ISARC; Sacks, R., Kedar, A., SeeBridge as next generation bridge inspection: overview, information delivery manual and model view definition (2018) Automation in Construction, 90, pp. 134-145; Song, X., Jüttler, B., Modeling and 3D object reconstruction by implicitly defined surfaces with sharp features (2009) Computers & Graphics, 33 (3), pp. 321-330. , http://dx.doi.org/10.1016/j.cag.2009.03.021; (2015) SeeBridge-Semantic enrichment engine for bridges, p. 77. , Technion Technion; Walsh, S. B., Borello, D. J., Data processing of point clouds for object detection for structural engineering applications (2013) Computer Aided Civil and Infrastructure Engineering, 28 (7), pp. 495-508; Yan, Y., Guldur, B., Automated structural modelling of bridges from laser scanning (2017) Structures Congress 2017; Yang, X.-S., Firefly algorithm (2008) Nature-inspired metaheuristic algorithms, 20 (2008), pp. 79-90; Zhang, G., Vela, P. A., A sparsity inducing optimization based algorithm for planar patches extraction from noisy point cloud data (2015) Computer Aided Civil and Infrastructure Engineering, 30 (2), pp. 85-102; Zhu, Z., German, S., Detection of large-scale concrete columns for automated bridge inspection (2010) Automation in construction, 19 (8), pp. 1047-1055. , http://dx.doi.org/10.1016/j.autcon.2010.07.016; Zhu, Z., German, S., Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation (2011) Automation in Construction, 20 (7), pp. 874-883. , http://dx.doi.org/10.1016/j.autcon.2011.03.004","Mafipour, M.S.; Technical University of MunichGermany; email: m.saeed.mafipour@tum.de","Abualdenien J.Borrmann A.Ungureanu L.-C.Hartmann T.",,"Technische Universitat Berlin","28th International Workshop on Intelligent Computing in Engineering of the European Group for Intelligent Computing in Engineering, EG-ICE 2021","30 June 2021 through 2 July 2021",,180462,,9783798332126,,,"English","EG-ICE Workshop Intell. Comput. Eng., Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85133514832
"Gunner S., Voyagaki E., Gavriel G., Carhart N., MacDonald J., Tryfonas T., Taylor C., Pregnolato M.","57216458982;15081790600;57303798600;36442104800;56583351100;23006808300;7404823270;57189259631;","Digital Twins for civil engineering: the Clifton Suspension Bridge (UK)",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"1561","1566",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128350948&partnerID=40&md5=a1f6460c53c5c38a2a3fa7a023bfe218","Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom","Gunner, S., Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom; Voyagaki, E., Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom; Gavriel, G., Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom; Carhart, N., Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom; MacDonald, J., Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom; Tryfonas, T., Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom; Taylor, C., Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom; Pregnolato, M., Department of Civil Eng., Faculty of Engineering, University of Bristol, Bristol, United Kingdom","Society is dependent on aging infrastructure, which usually operates outside its expected life. Replacing this infrastructure is often an unviable option due to its cost and disruption. A structure's operational life might be extended if the features of its aging are better understood, enabling preventive maintenance to compensate. Digital Twins (the continuous comparison between sensor measurements and a mathematical model) are one way of enabling this sort of data-driven decision making. However, despite the possibilities for this technology, its take up amongst industry has been slow, in part because infrastructure managers are unsure of how the technology will support them. This work develops a methodological framework to enhance this uptake in the field of systems engineering and the system development life cycle, using the developed knowledge to inform how an operational Digital Twin should be created. The requirements capture is the most important part of any system design development process. We present a Digital Twin development method, grounded firmly in a thorough requirements capture, and illustrate how those requirements inform many of the later design decisions. We then present our method through a case study of the Clifton Suspension Bridge, UK. Our method provides a series of actionable steps, the completion of which will facilitate the creation of a Digital Twin able to support operational decisions. By fulfilling the requirements of infrastructure managers, we hope to encourage the uptake of the technology. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","Bridge Management; Decision Support; Digital Twin; Finite Element Model; Resilience; Structural Health Monitoring","Decision making; Decision support systems; Life cycle; Managers; Preventive maintenance; Structural health monitoring; Ageing infrastructures; Bridge management; Decision supports; Expected life; Finite element modelling (FEM); Infrastructure managers; Operational life; Requirements Capture; Resilience; Sensor measurements; Finite element method",,,,,"EP/R00742X/2; EP/P016782/1, EP/R013411/1; Engineering and Physical Sciences Research Council, EPSRC","This work was supported by the Engineering and Physical Sciences Research Council (ESPRC) LWEC (Living With Environmental Change) Fellowship (EP/R00742X/2); UK Collaboratorium for Research in Infrastructure & Cities (UKCRIC): Urban Observatories (EP/P016782/1);UKCRIC City Observatory Research platfOrm for iNnovation and Analytics (CORONA) (EP/R013411/1). The authors gratefully acknowledge: CSBT, Trish, AMP, COWI","ACKNOWLEDGMENTS This work was supported by the Engineering and Physical Sciences Research Council (ESPRC) LWEC (Living With Environmental Change) Fellowship (EP/R00742X/2); UK Collaboratorium for Research in Infrastructure & Cities (UKCRIC): Urban Observatories (EP/P016782/1);UKCRIC City Observatory Research platfOrm for iNnovation and Analytics (CORONA) (EP/R013411/1). The authors gratefully acknowledge: CSBT, Trish, AMP, COWI",,,,,,,,,"(2018) Digital Twin - Towards a Meaningful Framework [online], , https://www.arup.com//media/arup/files/publications/d/digital-twin-report.pdf, (accessed 1/06/2020); Wright, L., Davidson, S., How to tell the difference between a model and a digital twin (2020) Adv. Model. and Simul. in Eng. Sci, 7 (13). , https://doi.org/10.1186/s40323-020-00147-4; (2020) The Approach to Delivering a National Digital Twin for the United Kingdom, , https://www.cdbb.cam.ac.uk/files/approach_summaryreport_final.pdf, [online]; Daskalova, M., (2018) The 'digital twin' - a bridge between the physical and the digital world, , https://cobuilder.com/en/the-digital-twin-a-bridge-between-thephysical-and-the-digital-world/, [online]. (accessed on 09.06.2020); Gunner, S., Vardanega, P. J., Tryfonas, T., Macdonald, J. H., Wilson, R. E., Rapid deployment of a WSN on the Clifton Suspension Bridge, UK (2017) Proceedings of the Institution of Civil Engineers-Smart Infrastructure and Construction, 170 (3), pp. 59-71; Cook, R., (1995) Finite element modeling for stress analysis, , New York: Wiley; Balageas, D., Fritzen, Guemes, A., (2006) Structural health monitoring, , London Newport Beach, CA: ISTE; Sarget, G.R., Validation and Verification of Simulation Models (1992) Proceedings of the 1992 Winter Simulation Conference, pp. 104-114. , (Swain et al. eds): University Syracuse, New York; INCOSE, B., (2015) Systems engineering handbook: A guide for system life cycle processes and activities, , San Diego, US-CA: International Council on Systems Engineering; Maqsood, T., Finegan, A.D., Walker, D.H., (2001) Five Case Studies Applying Soft Systems Methodology to Knowledge Management, , Queensland University of Technology: Brisbane, Australia; Ewusi-Mensah, K., Przasnyski, Z. H., On information systems project abandonment: An exploratory study of organizational practices (1991) MIS Quarterly, 15 (1), pp. 67-84; Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Nee, A. Y. C., Enabling technologies and tools for digital twin (2019) Journal of Manufacturing Systems; Ko, J. M., Ni, Y. Q., Technology developments in structural health monitoring of large-scale bridges (2005) Engineering structures, 27 (12), pp. 1715-1725; Ballio, F., Ballio, G., Franzetti, S., Crotti, G., Solari, G., Actions monitoring as an alternative to structural rehabilitation: Case study of a river bridge (2018) Structural Control and Health Monitoring, 25 (11), p. e2250; Barlow, W. H., Description of the Clifton Suspension Bridge. Minutes of the Proceedings of the Institution of Civil Engineers, 1867,26, 243-257; Reprinted Proceedings of the Institution of Civil Engineers (1867), Bridge Engineering, 2003,156 1, 5-10; Macdonald, J.H.G., Pedestrian-induced vibrations of the Clifton Suspension Bridge, UK (2008) Proceedings of the Institution of Civil Engineers Bridge Engineering, 161 (BE2), pp. 69-77","Pregnolato, M.; Department of Civil Eng., United Kingdom",,,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021","30 June 2021 through 2 July 2021",,179266,25643738,,,,"English","Int. Conf. Struct. Health. Monit. Intelligent Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85128350948
"Lad V.H., Patel D.A., Chauhan K.A., Patel K.A.","57218671942;55755999400;55793349000;7402235231;","Development of a causal loop diagram for bridge resilience",2021,"Proceedings of the 37th Annual ARCOM Conference, ARCOM 2021",,,,"137","146",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118439593&partnerID=40&md5=958c43597a15f0498e872baac0834d5e","Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Gujarat, Surat, 395007, India","Lad, V.H., Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Gujarat, Surat, 395007, India; Patel, D.A., Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Gujarat, Surat, 395007, India; Chauhan, K.A., Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Gujarat, Surat, 395007, India; Patel, K.A., Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Gujarat, Surat, 395007, India","In recent years, the resilience assessment for bridges has drawn ample attention in the engineering and management community. It has been attempted to evaluate bridge resilience by developing a resilience matrix or single-measure index. However, existing studies overlooked the prevailing interdependency among various physical and social infrastructures. Moreover, the technical, organizational, social, and economic aspects of these infrastructures are of dynamic nature. Therefore, this study develops a causal loop diagram (CLD) of bridge resilience to explore and understand how other infrastructures and their dynamism influence bridge resilience. Total 21 bridge resilience factors are identified based on the literature review. Out of these, 14 bridge resilience factors are shortlisted by using the Delphi method. Along with these 14 shortlisted factors, four properties of resilience (robustness, rapidity, resourcefulness, redundancy) and four infrastructures (bridge, transportation network, other utility infrastructures, and governance system) are considered to develop CLD. Thus, eight causal loops are developed, validated, and presented for improving bridge resilience. Further, the proposed study can help to implement effective policies for improving urban resilience and developing a smart city digital twin (SCDT) system. © 2021 Proceedings of the 37th Annual ARCOM Conference, ARCOM 2021. All Rights Reserved.","Bridge; Causal loop diagram; Delphi method; Resilience","Bridges; Causal loop diagrams; Delphi method; Dynamic nature; Economic aspects; matrix; Organizational aspects; Organizational economics; Resilience; Social infrastructure; Technical aspects; Decision making",,,,,,,,,,,,,,,,"Andrić, J M, Lu, D-G, Fuzzy methods for prediction of seismic resilience of bridges (2017) International Journal of Disaster Risk Reduction, 22, pp. 458-468; Banerjee, S, Vishwanath, B S, Devendiran, D K, Multihazard resilience of highway bridges and bridge networks: A review (2019) Structure, and Infrastructure Engineering, 15 (12), pp. 1694-1714; Cartes, P C, Echaveguren, T, Gine, A C, Binet, E A, A cost-benefit approach to recover the performance of roads affected by natural disasters (2020) International Journal of Disaster Risk Reduction, 53, p. 102014; Deco, A, Bocchini, P, Frangopol, D M, A probabilistic approach for the prediction of seismic resilience of bridges (2013) Earthquake Engineering Structural Dynamic, 42 (10), pp. 1469-1487; Diaz, E E M, Moreno, F N, Mohammadi, J, Investigation of common causes of bridge collapse in Colombia (2009) Practice Periodical on Structural Design and Construction, 14 (4), pp. 194-200; Domaneschi, M, Martinelli, L, Earthquake-resilience-based control solutions for the extended benchmark cable-stayed bridge (2016) Journal of Structural Engineering, 142 (8), p. C4015009; Dong, Y, Frangopol, D M, Probabilistic time-dependent multi-hazard life-cycle assessment and resilience of bridges considering climate change (2016) Journal of Performance of Constructed Facilities, 30 (5), p. 0000883; Freckleton, D, Heaslip, K, Louisell, W, Collura, J, Evaluation of resiliency of transportation networks after disasters (2012) Transportation Research Record, 2281 (1), pp. 109-116; Fu, Z, Ji, B, Cheng, M, Maeno, H, Statistical analysis of the causes of bridge collapse in China (2013) Forensic Engineering 2012: Gateway to a Safer Tomorrow, Sixth Congress on Forensic Engineering, pp. 75-83. , October 31-November 3, San Francisco, California, USA; Hair, J F, Black, W C, Babin, B J, Anderson, R E, (2014) Multivariate Data Analysis 7th Edition, , Upper Saddle River, NJ: Prentice-Hall; Hallowell, M R, Gambatese, J A, Qualitative research: application of the Delphi method to CEM research (2010) Journal of Construction Engineering and Management, 136 (1), pp. 99-107; Harik, I E, Shaaban, A M, Gesund, H, Valli, G Y S, Wang, S T, United States bridge failures, 1951-1988 (1990) Journal of Performance of Constructed Facilities, 4 (4), pp. 272-277; Ikpong, A, Bagchi, A, New method for climate change resilience rating of highway bridges (2015) Journal of Cold Regions Engineering, 29 (3), p. 040114013; Karamlou, A, Bocchini, P, From component damage to system-level probabilistic restoration functions for a damaged bridge (2017) Journal of Infrastructure Systems, 23 (3), p. 4016042; Kirkwood, C W, (1998) System Dynamics Methods: A Quick Introduction, , College of Business, Arizona State University; Luko, G, Rojas, E M, Research validation: challenges and opportunities in the construction domain (2010) Journal of Construction Engineering and Management, 136 (1), pp. 127-135; Minaie, E, Moon, F, Practical and simplified approach for quantifying bridge resilience (2017) Journal of Infrastructure Systems, 23 (4), p. 4017016; Patel, D A, Jha, K N, Developing a process to evaluate construction project safety hazard index using the possibility approach in India (2017) Journal of Construction Engineering and Management, 143 (1), p. 04016081; Patel, D A, Lad, V H, Chauhan, K A, Patel, K A, Development of bridge resilience index using multi-criteria decision-making techniques (2020) Journal of Bridge Engineering, 25 (10), p. 04020090; Richardson, G P, Pugh, A L, (1981) Introduction to System Dynamics Modelling with DYNAMO, , Cambridge, Massachusetts: Productive Press; Sterman, J D, (2000) Business Dynamics: Systems Thinking and Modelling for a Complex World, , Boston: Irwin/McGraw-Hill; Vishwanath, B S, Banerjee, S, Life-cycle resilience of aging bridges under earthquakes (2019) Journal of Bridge Engineering, 24 (11), p. 04019106; Wardhana, K, Hadipriono, F C, Analysis of recent bridge failures in the United States (2003) Journal of Performance of Constructed Facilities, 17 (3), pp. 144-150","Lad, V.H.; Department of Civil Engineering, Gujarat, India; email: vishallad2507@gmail.com",,,"Association of Researchers in Construction Management","37th Annual Association of Researchers in Construction Management Conference, ARCOM 2021","6 September 2021 through 7 September 2021",,172701,,,,,"English","Proc. Annual ARCOM Conf., ARCOM",Conference Paper,"Final","",Scopus,2-s2.0-85118439593
"Bittencourt T.N., Futai M.M., da Conceição Neto A.P., Ribeiro D.M.","6603036318;12142761800;56811543600;25930078000;","Digital transformation of bridges inspection, monitoring and maintenance processes",2021,"Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations - Proceedings of the 10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020",,,,"11","30",,1,"10.1201/9780429279119-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117590927&doi=10.1201%2f9780429279119-2&partnerID=40&md5=3621d9aad7c362cb52a42515c381bd59","USP - University of São Paulo, São Paulo, Brazil; Rio de Janeiro, Brazil","Bittencourt, T.N., USP - University of São Paulo, São Paulo, Brazil; Futai, M.M., USP - University of São Paulo, São Paulo, Brazil; da Conceição Neto, A.P., USP - University of São Paulo, São Paulo, Brazil; Ribeiro, D.M., Rio de Janeiro, Brazil","New communication and information systems and technologies - known as ICT (Information and Communication Technologies) - have immense potential to aggregate new functionalities and services to the management of infrastructure assets. This phenomenon, known as 'Digital Transformation', has influenced the evolution of various sectors of our society, such as the emergence of 'Industry 4.0'. New wireless communication technologies, such as 5G networks (large capacity communication, high reliability, great coverage and low consumption, for information processing and management - such as Cloud Computing, Edge Computing, Big Data, Machine Learning and Artificial Intelligence - are considered as the enabling technologies of this digital transformation, integrated with the concept of IoT(Internet of Things). High connectivity capacity and intensive automation enable, for example, changes in inspection paradigms and asset maintenance by transferring product focus to service platforms ('Everything' as a Service - XaaS), bringing gains for efficiency, productivity, comfort and operational safety, as well as cost reduction. Bridges constitute an important part of the infrastructure and are subjected to damage caused by their continuous use over time. In addition to the effects of loading (fatigue, impacts, overloads, etc.), they are subjected to degradation of materials and support conditions, as well as exposure to adverse environmental conditions (storms, floods, gale, earthquakes, etc.). Scheduled inspections to assess their structural conditions are essential to ensure their proper use within the established safety limits. In other occasions, continuous or repeated monitoring of structural responses of bridges (displacements, vibrations and rotations at critical points) may add important information for decision-making regarding its maintenance, repair and reinforcement. The use of these data, together with techniques of structural reliability for the treatment of the uncertainties, allows predictions about the structural behavior to be elaborated with the consideration of different loading and degradation scenarios. The new ICTs can greatly contribute to the improvement of maintenance capacity and, consequently, to the reliability of the assets and to the operational availability of the system. Thus, the development of new predictive maintenance approaches, which make use of the large amount of data available, can improve the efficiency of maintenance processes, producing more accurate and reliable anticipated diagnostics. In this way, Digital Transformation can reduce maintenance costs (avoiding unnecessary maintenance events) and improve system availability, reducing operational losses. The use of Big Data Analytics, incorporating Artificial Intelligence and Machine Learning, are innovative solutions that can be introduced. The adoption of Digital Twins, that incorporate all these tools, can lead to a reduction in the total cost, allowing predictive and proactive maintenance. The concept of a Digital Twin for a railway bridge will be illustrated in this paper. © 2021 Taylor & Francis Group, London",,"Availability; Big data; Cost reduction; Data Analytics; Decision making; Efficiency; Information management; Inspection; Internet of things; Life cycle; Machine learning; Maintenance; Metadata; Predictive analytics; Reliability; Safety engineering; Bridge inspection; Bridge monitoring; Bridges maintenance; Communication and information systems; Digital transformation; Inspection process; Machine-learning; Maintenance process; Monitoring process; Predictive maintenance; 5G mobile communication systems",,,,,"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq","Authors would like to acknowledge CNPq (Brazilian Ministry of Science and Technology Agency) and CAPES (Higher Education Improvement Agency) for providing an important part of the financial support needed to develop this paper. The work described in this paper has been partially supported by VLI and VALE Railway Companies. The opinions and conclusions presented in this paper are those of the authors and do not necessarily reflect the views of the sponsoring organizations. The interaction and the support provided by PhDsoft is deeply appreciated.",,,,,,,,,,"(2016) Inspeção de Pontes, Viadutos e Passarelas de Concreto - Procedimento, , ABNT-NBR 9452. 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ISBN: 978-1-138-00120-6; Frangopol, D.M., Sabatino, S., Dong, Y., (2016) Bridge Life-Cycle Performance and Cost: Analysis, Prediction, Optimization and Decision Making IABMAS 2016, 1, pp. 3-17. , CRC Press Taylor and Francis. ISBN: 978-1-138-02851-7; Fujino, Y., Bridge maintenance, renovation and management - Research and Development of governmental program in Japan (2018) IABMAS 2018, 1, pp. 2-14. , CRC: 2018; Furuta, H., Frangopol, D.M., Akiyana, M., (2015) Life-Cycle of Structural Systems: Design, Assessment, Maintenance and Management, 1, p. 433. , CRC Press Taylor and Francis, 2015. ISBN: 978-1-138-00120-6; Ghosn, G., Duenas-Osorio, L., Frangopol, D.M., McAllister, T.P., Bocchini, P., Manuel, L., Ellingwood, B.R., Tsiatas, G., Performance indicators for structural systems and infrastructure networks (2016) Journal of Structural Engineering, 142 (9), p. 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London: CRC Press Taylor and Francis, 2018. ISBN: 978-1-138-73045-8; Pravia, Z.M.C., Braido, J.D., Measurements of bridges' vibration characteristics using a mobile phone (2015) IBRACON Structures and Materials Journal, 8 (5), pp. 721-743. , http://dx.doi.org/10.1590/S1983-41952015000500009, (October 2015) ISSN 1983-4195; Radomski, W., (2001) Bridge Rehabilitation, , Warsaw: Imperial College Press; Rothberg, S.J., An International Review of Laser Doppler Vibrometry: Making Light Work of Vibration Measurement (2017) Optics and Laser in Engineering, 99, pp. 11-22; Rowland, I., Howe, T.N., (1999) Vitruvius. 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Z., Sistonen, E., Machine learning for durability and service-life assessment of reinforced concrete structures: Recent advances and future directions (2017) Automation in Construction, 77, pp. 1-14; Ticona Melo, L.R., Ribeiro, D., Calçada, R.B., Bittencourt, T.N., Validation of a vertical train-track-bridge dynamic interaction model based on limited experimental data (2019) Structure and Infrastructure Engineering, 1, pp. 1-21. , https://doi.org/10.1080/15732479.2019.1605394, 2019; Wenzel, H., (2009) Health Monitoring of Bridges, , Vienna: John Wiley & Sons; (2019) Wikipedia 6D BIM, , https://en.wikipedia.org/wiki/6D_BIM, [Online] [Accessed 20 December 2019]; Witte, C.C., Ribeiro, D.M., A statistical time dependent degradation curve analysis for marine structures (2012) ASME 2012 - 31st International Conference on Ocean, Offshore and Arctic Engineering, pp. 1-5. , OMAE2012; Witte, C.C., Ribeiro, D.M., Structural integrity management: painting predictive control (2012) SPE International Conference and Exhibition on Oilfield Corrosion held in Aberdeen, pp. 1-5. , 2012-b. SPE 155857-PP, UK, 28-29 May; Yan, W., Denga, L., Zhang, F., Li, T., Shaofan Li, S., Probabilistic machine learning approach to bridge fatigue failure analysis due to vehicular overloading (2019) Engineering Structures, 193, pp. 91-99. , https://doi.org/10.1016/j.engstruct.2019.05.028, 15 August 2019; Yang, D.F., Frangopol, D.M., Probabilistic optimization framework for inspection/repair planning of fatigue-critical details using dynamic Bayesian networks (2018) Computers and Structures, 198, pp. 40-50. , https://doi.org/10.1016/j.compstruc.2018.01.006; Yang, D.F., Frangopol, D.M., Life-cycle management of deteriorating civil infrastructure considering resilience to lifetime hazards: A general approach based on renewal-reward processes (2019) Reliability Engineering & System Safety, 183, pp. 197-212. , https://doi.org/10.1016/j.ress.2018.11.016; Zhu, Z., Liu, C., Xu, X., Visualisation of the Digital Twin data in manufacturing by using Augmented Reality (2019) Procedia CIRP, 81, pp. 898-903. , http://doi.org/10.1016/j.procir.2019.03.223",,"Yokota H.Frangopol D.M.",,"CRC Press/Balkema","10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020","11 April 2021 through 15 April 2021",,172353,,9780429279119; 9780367232788,,,"English","Bridge Maint., Saf., Manag., Life-Cycle Sustain. Innov. - Proc. Int. Conf. Bridge Maint., Saf. Manag., IABMAS",Conference Paper,"Final","",Scopus,2-s2.0-85117590927
"Kleiser D., Woock P.","57223051163;36626808600;","Towards Automated Structural Health Monitoring for Offshore Wind Piles",2020,"2020 Global Oceans 2020: Singapore - U.S. Gulf Coast",,,"9389437","","",,1,"10.1109/IEEECONF38699.2020.9389437","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104661932&doi=10.1109%2fIEEECONF38699.2020.9389437&partnerID=40&md5=b0605ab2f93a0dc47daae3683c818a7b","Systems of Measurement, Control and Diagnosis (MRD), Fraunhofer Iosb, Karlsruhe, Germany","Kleiser, D., Systems of Measurement, Control and Diagnosis (MRD), Fraunhofer Iosb, Karlsruhe, Germany; Woock, P., Systems of Measurement, Control and Diagnosis (MRD), Fraunhofer Iosb, Karlsruhe, Germany","Simulation plays an important role in the development, testing and evaluation of new robotic applications, reducing implementation time, cost and risk. In this paper we show a digital twin simulation model of an inspection ROV which is capable of performing structural health monitoring by automated creation of a map of an offshore wind monopile. The data is compared to a known reference model. The digital twin simulation model is extended by a physical sensor data input device to bridge the gap between simulation and testing in water. © 2020 IEEE.","digital twin; monopile; offshore wind; ROV; simulation; Structural health monitoring","Digital twin; Offshore oil well production; Monopile; Offshore winds; Physical sensors; Reference modeling; Robotic applications; Simulation and testing; Simulation model; Testing and evaluation; Structural health monitoring",,,,,,"This work is funded by the German Federal Ministry for Economic Affairs and Energy",,,,,,,,,,"Ziegler, L., Muskulus, M., Lifetime extension of offshore wind monopiles: Assessment process and relevance of fatigue crack inspection (2016) 12th EAWE PhD Seminar, , DTU Lyngby, Denmark; Mathiesen, T., Black, A., Grønvold, F., Alle, P., Monitoring and inspection options for evaluating corrosion in offshore wind foundations (2016) NACE Corrosion-2016, 7702. , paper no C-2016; Cook, D., Vardy, A., Lewis, R., A survey of auv and robot simulators for multi-vehicle operations (2014) 2014 IEEE/OES Autonomous Underwater Vehicles (AUV), pp. 1-8; Robotic Operating System, , https://www.ros.org, Stanford Artificial Intelligence Laboratory et al; Koenig, N., Howard, A., Design and use paradigms for gazebo, an open-source multi-robot simulator (2004) 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No. 04CH37566), 3, pp. 2149-2154; Manhães, M.M.M., Scherer, S.A., Voss, M., Douat, L.R., Rauschenbach, T., UUV simulator: A gazebo-based package for underwater intervention and multi-robot simulation (2016) OCEANS 2016 MTS/IEEE Monterey. IEEE, , https://doi.org/10.1109%2Foceans.2016.7761080, sep; UUV Simulator, , https://github.com/uuvsimulator/uuvsimulator; Vio, R.P., Cristi, R., Smith, K.B., Uuv localization using acoustic communications, networking, and a priori knowledge of the ocean current (2017) OCEANS 2017-Aberdeen, pp. 1-7; Paull, L., Saeedi, S., Seto, M., Li, H., Auv navigation and localization: A review (2013) IEEE Journal of Oceanic Engineering, 39 (1), pp. 131-149; Foresti, G.L., Visual inspection of sea bottom structures by an autonomous underwater vehicle (2001) IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 31 (5), pp. 691-705; Choi, J., Lee, Y., Kim, T., Jung, J., Choi, H.-T., Development of a rov for visual inspection of harbor structures (2017) 2017 IEEE Underwater Technology (UT). IEEE, pp. 1-4; McLeod, D., Jacobson, J., Autonomous uuv inspection-revolutionizing undersea inspection (2011) OCEANS'11 MTS/IEEE KONA, pp. 1-4; Vissiere, D., Martin, A., Petit, N., Using distributed magnetometers to increase imu-based velocity estimation into perturbed area (2007) 2007 46th IEEE Conference on Decision and Control, pp. 4924-4931; Moore, T., Stouch, D., A generalized extended kalman filter implementation for the robot operating system (2014) Proceedings of the 13th International Conference on Intelligent Autonomous Systems (IAS-13), , Springer, July; Torr, P.H., Zisserman, A., MLESAC: A new robust estimator with application to estimating image geometry (2000) Computer Vision and Image Understanding, 78 (1), pp. 138-156; Fitzgibbon, A., Robust registration of 2d and 3d point sets (2002) Image and Vision Computing, 21, pp. 1145-1153. , 04",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020","5 October 2020 through 30 October 2020",,168366,,9781728154466,,,"English","Glob. Oceans: Singapore - U.S. Gulf Coast",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85104661932
"van Nimmen K., van Hauwermeiren J., van den Broeck P.","55237620100;57214717938;6506964349;","Identification of human-structure interaction based on full-scale observations",2020,"Proceedings of the International Conference on Structural Dynamic , EURODYN","1",,,"1874","1882",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099730516&partnerID=40&md5=6409d25b15439c1ab2b67ed93aee713e","KU Leuven, Department of Civil Engineering, Structural Mechanics, Leuven, B-3001, Belgium","van Nimmen, K., KU Leuven, Department of Civil Engineering, Structural Mechanics, Leuven, B-3001, Belgium; van Hauwermeiren, J., KU Leuven, Department of Civil Engineering, Structural Mechanics, Leuven, B-3001, Belgium; van den Broeck, P., KU Leuven, Department of Civil Engineering, Structural Mechanics, Leuven, B-3001, Belgium","The further development and improvement of prediction models for crowd-induced vibrations of footbridges requires detailed information on representative operational loading data. This paper uses an inverse method to estimate the parameters that govern human-structure interaction from the resulting structural response. The parameters of interest concern the dynamic characteristics of a single-degree-of-freedom (SDOF) system, applied to describe the mechanical interaction between the pedestrian and the structure. The parameter estimation procedure assumes that the dynamic behavior of the empty structure, the average pedestrian weight and the distribution of step frequencies in the crowd are known. The dynamic characteristics of the mechanical interaction model are estimated by minimizing the discrepancy between the observed and the simulated power spectral density of the structural response. The approach is applied to the Eeklo Footbridge Benchmark Dataset where the pedestrian and bridge motion are registered simultaneously using wireless tri-axial accelerometers, involving pedestrian densities up to 0.5 persons/m2. A digital twin of the Eeklo footbridge, the average weight of the pedestrians and the distribution of step frequencies, identified from the accelerations registered on the lower back of each pedestrian, are used as input for the proposed parameter estimation procedure. The results show that an estimate of the natural frequency and damping ratio of the mechanical interaction model is obtained that is in line with recent findings in the literature. These estimates are, however, for the first time ever based on a comprehensive set of full-scale observations. © 2020 European Association for Structural Dynamics. All rights reserved.","Footbridge; Full-scale observations; Human-structure interaction; Pedestrian-induced vibrations","Degrees of freedom (mechanics); Digital twin; Footbridges; Frequency estimation; Inverse problems; Predictive analytics; Spectral density; Structural dynamics; Dynamic characteristics; Human-structure interaction; Induced vibrations; Mechanical interactions; Pedestrian density; Single degree of freedom systems; Structural response; Triaxial accelerometer; Dynamics",,,,,,,,,,,,,,,,"Georgakis, C. T., Ingólfsson, E., Recent advances in our understanding of vertical and lateral footbridge vibrations (2014) Proceedings of the 5th International Footbridge Conference, , (London, UK), July; Salyards, K., Hua, Y., Assessment of dynamic properties of a crowd model for human-structure interaction modelling (2015) Engineering Structures, 89, pp. 103-110; Shahabpoor, E., Pavic, A., Racic, V., Interaction between walking humans and structures in vertical direction: A literature review (2016) Shock and Vibration, pp. 12-17; Van Nimmen, K., Lombaert, G., De Roeck, G., Van den Broeck, P., The impact of vertical human-structure interaction on the response of footbridges to pedestrian excitation (2017) Journal of Sound and Vibration, 402, pp. 104-121; Tubino, F., Probabilistic assessment of the dynamic interaction between multiple pedestrians and vertical vibrations of footbridges (2018) Journal of Sound and Vibration, 417, pp. 80-96; Živanovic, S., Pavic, A., Reynolds, P., Probability-based prediction of multi-mode vibration response to walking excitation (2007) Engineering Structures, 29 (6), pp. 942-954; Van Nimmen, K., Van den Broeck, P., Lombaert, G., Inverse identification of the pedestrian characteristics governing human-structure interaction (2017) Proceedings of the 10th International Conference on Structural Dynamics, EURODYN 2017, pp. 2889-2894. , (F. Vestroni, F. Romeo, and Gattulli, eds), (Rome, Italy), September; Van Nimmen, K., Van den Broeck, P., Lombaert, G., Tubino, F., Pedestrian-induced vibrations of footbridges: An extended spectral approach (2020) Journal of Bridge Engineering, , Forthcoming; Asami, T., Nishihara, O., Baz, A., Analytical solutions to h-infinity and h-2 optimization of dynamic vibration absorbers attached to damped linear systems (2002) Journal of Vibration and Acoustics - ASME, 124 (2), pp. 284-295; Peeters, B., De Roeck, G., Reference-based stochastic subspace identification for output-only modal analysis (1999) Mechanical Systems and Signal Processing, 13 (6), pp. 855-878; Reynders, E., De Roeck, G., Reference-based combined deterministic-stochastic subspace identification for experimental and operational modal analysis (2008) Mechanical Systems and Signal Processing, 22 (3), pp. 617-637",,"Papadrakakis M.Fragiadakis M.Papadimitriou C.",,"European Association for Structural Dynamics","11th International Conference on Structural Dynamics, EURODYN 2020","23 November 2020 through 26 November 2020",,165382,23119020,9786188507203,,,"English","Proc. Int. Conf. Struct. Dyn., EURODYN",Conference Paper,"Final","",Scopus,2-s2.0-85099730516
"van den Broeck P., van Hauwermeiren J., van Nimmen K.","6506964349;57214717938;55237620100;","An open access benchmark dataset on pedestrian-induced vibrations collected on the eeklo footbridge",2020,"Proceedings of the International Conference on Structural Dynamic , EURODYN","1",,,"1866","1873",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099730068&partnerID=40&md5=e790d8bb486356508cf094fbea8e51a9","KU Leuven, Department of Civil Engineering, Structural Mechanics, Leuven, B-3001, Belgium","van den Broeck, P., KU Leuven, Department of Civil Engineering, Structural Mechanics, Leuven, B-3001, Belgium; van Hauwermeiren, J., KU Leuven, Department of Civil Engineering, Structural Mechanics, Leuven, B-3001, Belgium; van Nimmen, K., KU Leuven, Department of Civil Engineering, Structural Mechanics, Leuven, B-3001, Belgium","This paper presents a new and publicly available full-scale dataset collected specifically for the further development and validation of models for crowd-induced loading. The dataset is collected on a real footbridge, with multiple modes sensitive to pedestrian-induced vibrations and with a digital twin available. Video cameras are used to capture the walking trajectories of the pedestrians. The bridge and pedestrian motion are registered simultaneously using wireless tri-axial accelerometers on the deck and the lower back of the pedestrians. Two data blocks are collected involving pure ambient excitation. Four data blocks are collected involving two pedestrian densities, 0.25 persons/m2 and 0.50 persons/m2, representing a total of more than one hour data for each pedestrian density. Analysis of the structural response shows that the different data blocks can be considered representative for the involved load case. The identified distribution of step frequencies in the crowd indicate a significant contribution of (near-)resonant loading with multiple modes of the footbridge. Furthermore, the dataset displays clear signs of human-structure interaction, suggesting a significant increase in effective modal damping ratios due to the presence of the crowd. © 2020 European Association for Structural Dynamics. All rights reserved.","Dataset; Footbridge; Open access; Pedestrian-induced vibrations","Digital twin; Structural dynamics; Vibrations (mechanical); Video cameras; Ambient excitation; Human-structure interaction; Modal damping ratios; Pedestrian density; Pedestrian-induced vibrations; Structural response; Triaxial accelerometer; Walking trajectory; Footbridges",,,,,,,,,,,,,,,,"Fanning, P., Healy, P., Pavic, A., Pedestrian bridge vibration serviceability: A case study in testing and simulation (2010) Advances in Structural Engineering, 13 (5), pp. 861-873; Tubino, F., Carassale, L., Piccardo, G., Human-induced vibrations on two lively footbridges in Milan (2016) Journal of Bridge Engineering, 21 (8); Dey, P., Sychterz, A., Narasimhan, S., Walbridge, S., Performance of pedestrian-load models through experimental studies on lightweight aluminium bridges (2016) Journal of Bridge Engineering, 21 (8); Van Nimmen, K., Verbeke, P., Lombaert, G., De Roeck, G., Van den Broeck, P., Numerical and experimental evaluation of the dynamic performance of a footbridge with tuned mass dampers (2016) Journal of Bridge Engineering; Bocian, M., Brownjoh, J., Racic, V., Hester, D., Quattrone, A., Gilbert, L., Beasley, R., Time-dependent spectral analysis of interactions within groups of walking pedestrians and vertical structural motion using wavelets (2018) Mechanical Systems and Signal Processing, 105, pp. 502-523; Caetano, E., Cunha, A., Magalhães, F., Moutinho, C., Studies for controlling human-induced vibration of the Pedro e Inês footbridge, Portugal. Part 1: Assessment of dynamic behaviour (2010) Engineering Structures, 32, pp. 1069-1081; Carroll, S. P., Owen, J. S., Hussein, M. F. M., A coupled biomechanical discrete element crowd model of crowd-bridge dynamic interaction and application to the clifton suspension bridge (2013) Engineering Structures, 49, pp. 58-75; Brownjohn, J., Middleton, C., Efficient dynamic performance assessment of a footbridge (2005) Proceedings of the Institution of Civil Engineers, 158 (4); Gheitasi, A., Ozbulut, O., Usmani, S., Alipour, M., Harris, D., Experimental and analysitcal vibration serviceability assessment of an in-service footbridge (2016) Case Studies in Nondestructive Testing and Evaluation, 6, pp. 79-88; Živanovic, S., Benchmark footbridge for vibration serviceability assessment under vertical component of pedestrian load (2012) Journal of Structural Engineering, 138, pp. 1193-1202; Gomez, S., Marulanda, J., Thomson, P., Garcia, J., Gomez, D., Ortiz, A., Dyke, S., Rietdyk, S., Benchmark problem for assessing effects of human-structure interaction in footbridges (2017) Proceedings of IMAC 35, the International Modal Analysis Conference, , (Garden Grove, CA, USA), Springer International Publishing, January February; Van Nimmen, K., Lombaert, G., Jonkers, I., De Roeck, G., Van den Broeck, P., Characterisation of walking loads by 3D inertial motion tracking (2014) Journal of Sound and Vibration, 333, pp. 5212-5226; (2012) User Manual, , GeoSIG GMS 18 24, GeoSIG Ltd; (2016) User Manual X16-1D USB Accelerometer Data Logger, , Gulf Coast Data Concepts, Gulf Coast Data Concepts; Van Nimmen, K., Zhao, G., Seyfarth, A., Van den Broeck, P., A robust methodology for the reconstruction of the vertical pedestrian-induced load from the registered body motion (2018) Vibration, 2, pp. 250-268; Weidmann, U., Transporttechnik der fussganger (Transport technology of the pedestrian) (1993) Schriftenreihe IVT-Berichte, 90; Venuti, F., Bruno, L., An interpretative model of the pedestrian fundamental relation (2007) Comptes Rendus - Mecanique, 335 (4), pp. 194-200; Wei, X., Van den Broeck, P., De Roeck, G., Van Nimmen, K., A simplified method to account for the effect of human-human interaction on the pedestrian-induced vibrations of footbridges (2017) Proceedings of the 10th International Conference on Structural Dynamics, EURODYN 2017, , (F. Vestroni, F. Romeo, and Gattulli, eds), (Rome, Italy), September; Van Hauwermeiren, J., Van den Broeck, P., Van Nimmen, K., Vergauwen, M., Vision-based methodology for characterizing the flow of a high-density crowd (2018) Proceedings of the 9th InternationalConference on Bridge Maintenance, Safety and Management, , (Melbourne, Australia), Taylor and Francis Group, CRC Press, July; Helbing, D., Molnar, P., Social force model for pedestrian dynamics (1995) Physical Review, 51 (5), pp. 4282-4286; Dong, W., Kasperski, M., Shiqiao, G., Change of the dynamic characteristics of a pedestrian bridge during a mass event (2011) Proceedings of the 8th International Conference on Structural Dynamics of EURODYN, , (Leuven, Belgium), July; Salyards, K., Hua, Y., Assessment of dynamic properties of a crowd model for human-structure interaction modelling (2015) Engineering Structures, 89, pp. 103-110; Shahabpoor, E., Pavic, A., Racic, V., Interaction between walking humans and structures in vertical direction: A literature review (2016) Shock and Vibration, pp. 12-17; Van Nimmen, K., Lombaert, G., De Roeck, G., Van den Broeck, P., The impact of vertical human-structure interaction on the response of footbridges to pedestrian excitation (2017) Journal of Sound and Vibration, 402, pp. 104-121; Tubino, F., Probabilistic assessment of the dynamic interaction between multiple pedestrians and vertical vibrations of footbridges (2018) Journal of Sound and Vibration, 417, pp. 80-96",,"Papadrakakis M.Fragiadakis M.Papadimitriou C.",,"European Association for Structural Dynamics","11th International Conference on Structural Dynamics, EURODYN 2020","23 November 2020 through 26 November 2020",,165382,23119020,9786188507203,,,"English","Proc. Int. Conf. Struct. Dyn., EURODYN",Conference Paper,"Final","",Scopus,2-s2.0-85099730068
"Lu R., Ma Y., Guo L., Thorpe T., Brilakis I.","57194640091;57220074436;57207806922;57220074401;8837673400;","An Automated Target-Oriented Scanning System for Infrastructure Applications",2020,"Construction Research Congress 2020: Computer Applications - Selected Papers from the Construction Research Congress 2020",,,,"457","467",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096741932&partnerID=40&md5=f41a92fb956c06d41fbfc8b008789f0e","School of Architecture Building, and Civil Engineering, Loughborough Univ., Darwin College, Univ. of Cambridge, United Kingdom; Data Science Institute, School of Mathematics and Statistics, Shandong Univ., China; Laing o'Rourke Centre, Dept. of Engineering, Univ. of Cambridge, United Kingdom","Lu, R., School of Architecture Building, and Civil Engineering, Loughborough Univ., Darwin College, Univ. of Cambridge, United Kingdom; Ma, Y., Data Science Institute, School of Mathematics and Statistics, Shandong Univ., China; Guo, L., Data Science Institute, School of Mathematics and Statistics, Shandong Univ., China; Thorpe, T., School of Architecture Building, and Civil Engineering, Loughborough Univ., Darwin College, Univ. of Cambridge, United Kingdom; Brilakis, I., Laing o'Rourke Centre, Dept. of Engineering, Univ. of Cambridge, United Kingdom","Point cloud pre-processing is essential for emerging applications such as digital twinning but currently requires a lot of manual effort before the resulting data can be used. Practitioners usually use default scan range settings to take full scans, which generate huge point cloud datasets containing millions of points. However, only a fraction of the dataset is used for subsequent twinning processes and the remaining data is ""noise"". Researchers need to perform substantial cropping work to enable the point cloud can be used for detecting objects of interest. However, the problem of object detection in the post-processing stage also remains unresolved. This paper describes a new system TOSS to conduct a target-oriented scanning process. It streamlines the scan-to-gDT (geometric digital twin) process by automatically identifying the region of interest and its corresponding scanning path. TOSS consists of a cost-effective 3-DoF rotational laser scanner, a vision-based object detection algorithm, and a geometric-camera-model-based scanning control algorithm. Preliminary results on a real-world bridge indicate that TOSS can produce accurate scans of regions of interest (average: 95.5% precision and 89.4% recall). It is fully scalable and can be adapted to various infrastructure types, including buildings, bridges, industrial plants, tunnels, and roads. The algorithms also have great potential to be embedded in a traditional scanner's software. © 2020 American Society of Civil Engineers.",,"Bridges; Cost effectiveness; Digital twin; Image segmentation; Object recognition; Scanning; Detecting objects; Emerging applications; Infrastructure applications; Object detection algorithms; Post-processing stages; Region of interest; Regions of interest; Scanning systems; Object detection",,,,,,,,,,,,,,,,"Adan, A., Huber, D., 3d reconstruction of interior wall surfaces under occlusion and clutter (2011) Proceedings-3DIMPVT, 2011, pp. 275-281. , https://doi.org/10.1109/3DIMPVT.2011.42; Dimitrov, A., Gu, R., Golparvar-Fard, M., Non-uniform b-spline surface fitting from unordered 3d point clouds for as-built modeling (2016) CACAIE, 31 (7), pp. 483-498. , https://doi.org/10.1111/mice.12192; (2019) Faro Focus, , https://www.faro.com/en-gb/products/construction-bim-cim/faro-focus/, Last Accessed 25 July 2019; Jung, J., Hong, S., Jeong, S., Kim, S., Cho, H., Hong, S., Heo, J., Productive modeling for development of as-built bim of existing indoor structures (2014) Autom. Constr, 42, pp. 68-77. , https://doi.org/10.1016/j.autcon.2014.02.021; (2019) Leica Scanners., , https://leica-geosystems.com/products/laser-scanners/scanners, Last Accessed 25 July 2019; Lu, R., Brilakis, I., Middleton, C.R., Detection of structural components in point clouds of existing rc bridges (2018) CACAIE, , https://doi.org/10.1111/mice.12407; Lu, R., Brilakis, I., Digital twinning of existing reinforced concrete bridges from labelled point clusters (2019) Autom. Constr, , https://doi.org/10.1016/j.autcon.2019.102837; Lucas, C., Bouten, W., Koma, Z., Kissling, W.D., Seijmonsbergen, A.C., Identification of linear vegetation elements in a rural landscape using lidar point clouds (2019) Remote Sensing, , https://doi.org/10.3390/rs11030292; (2019) Trimble SX10., , https://geospatial.trimble.com/SX10_stories, Last Accessed 25 July 2019; Walsh, S.B., Borello, D.J., Guldur, B., Hajjar, J.F., Data processing of point clouds for object detection for structural engineering applications (2013) CACAIE, 28 (7), pp. 495-508. , https://doi.org/10.1111/mice.12016; Wei, L., Yang, B., Jiang, J., Cao, G., Wu, M., Vegetation filtering algorithm for uav-borne lidar point clouds: A case study in the middle-lower yangtze river riparian zone (2017) International Journal of Remote Sensing, , https://doi.org/10.1080/01431161.2016.1252476; Xu, Y., Tuttas, S., Hoegner, L., Stilla, U., Voxel-based segmentation of 3d point clouds from construction sites using a probabilistic connectivity model (2018) Pattern Recognition Letters, 102, pp. 67-74. , https://doi.org/10.1016/j.patrec.2017.12.016",,"Tang P.Grau D.El Asmar M.","Arizona State University;Construction Institute (CI) of the American Society of Civil Engineers (ASCE);Construction Research Council","American Society of Civil Engineers (ASCE)","Construction Research Congress 2020: Computer Applications","8 March 2020 through 10 March 2020",,164877,,9780784482865,,,"English","Constr. Res. Congr.: Comput. Appl. - Sel. Papers Constr. Res. Congr.",Conference Paper,"Final","",Scopus,2-s2.0-85096741932
"Okkonen J., Ketamo H., Lindsten H., Rauhala T., Viteli J.","16022877200;6506441330;57211278538;57218310992;55212209800;","Using ai to decrease demand and supply mismatch in itc labour market",2020,"Advances in Intelligent Systems and Computing","1211 AISC",,,"310","316",,1,"10.1007/978-3-030-50896-8_44","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088746834&doi=10.1007%2f978-3-030-50896-8_44&partnerID=40&md5=9ecc28cde38f936c122032010b81597d","Faculty of Information Technologies and Communications Sciences, Tampere University, Tampere, FIN-33014, Finland; HeadAI, Rautatienpuistokatu 7, Pori, FI-28130, Finland","Okkonen, J., Faculty of Information Technologies and Communications Sciences, Tampere University, Tampere, FIN-33014, Finland; Ketamo, H., HeadAI, Rautatienpuistokatu 7, Pori, FI-28130, Finland; Lindsten, H., Faculty of Information Technologies and Communications Sciences, Tampere University, Tampere, FIN-33014, Finland; Rauhala, T., Faculty of Information Technologies and Communications Sciences, Tampere University, Tampere, FIN-33014, Finland; Viteli, J., Faculty of Information Technologies and Communications Sciences, Tampere University, Tampere, FIN-33014, Finland","In information technology and communication (ITC) industry the technology advances are unexpected and moving mysterious ways causing significant mismatch between demand and supply in labour market. To some extent the mismatch is due to emergence of new technologies replacing the old ones. On the other hand, it is also due to lack of capability and capacity of educational system to provide up to date and spot on graduates. There are several attempts to bridge the gap between supply and demand, yet both public and private sector had at least partially failed in the task. This paper presents a novel AI driven modus operandi that provides students or persons already in labour market a way to match one’s competencies against existing or future competency requirements, and thus being valid employee or applicant. On the other hand, the service discussed in the paper is also tool for giving input for forecasting future labour needs. And the third, the service also serves as mid- and long-range planning apparatus for education provider when making decisions on what academic modules best serve the needs of society and individuals. The paper presents the concept of technology and functionalities as well as roadmap for developing and implementing the service. The presented concept is heuristically proofed in expert evaluation. Moreover, the heart of the service has been technically and functionally tested in several cases. At the conclusion the paper presents model for matching system for labour competency development. The issues related to implementation and the next steps are also discussed. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.","Competency development; Digital Twins; Labour market mismatch; Natural language","Commerce; Education computing; Human engineering; Personnel training; Competency development; Demand and supply; Educational systems; Expert evaluation; Information technology and communications; Long Range Planning; Public and private sector; Technology advances; Employment",,,,,,,,,,,,,,,,"Okkonen, J., Helle, T., Lindsten, H., Expectation differences between students and staff of using learning analytics in Finnish universities (2020) Proceedings of ICITS 2020; Ketamo, H., Passi-Rauste, A., Vesterbacka, P., Vahtivuori-Hänninen, S., Accelerating the nation: applying AI to scout individual and organisational human capital (2018) Proceedings of ICIE 2018 International Conference on Innovation and Entrepreneurship, , Washington DC, 5–6 March (2018); Ketamo, H., Moisio, A., Passi-Rauste, A., Alamäki, A., Mapping the future curriculum: adopting artificial intelligence and analytics in forecasting competence needs (2019) Proceedings of the 10th European Conference on Intangibles and Intellectual Capital, ECIIC 2019, pp. 144-153. , Sargiacom, M. (ed) Italy, 23–24 May (2019). ISBN: 978-1-912764-19-8. ISSN: 2049-0941; Alamäki, A., Aunimo, L., Ketamo, H., Parvinen, L., Interactive machine learning: managing information richness in highly anonymized conversation data (2019) Collaborative Networks and Digital Transformation. The Proceeding of 20th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2019, pp. 173-183. , Camarinha-Matos, L.M., Afsarmanesh, H., Antonelli, D. (eds)","Okkonen, J.; Faculty of Information Technologies and Communications Sciences, Finland; email: jussi.okkonen@tuni.fi","Nazir S.Ahram T.Karwowski W.",,"Springer","AHFE Virtual Conference on Human Factors in Training, Education, and Learning Sciences, 2020","16 July 2020 through 20 July 2020",,241839,21945357,9783030508951,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85088746834
"Cardno C.A.","25958626200;","Next-Generation Drones Bolster Bridge Inspections",2019,"Civil Engineering Magazine Archive","89","3",,"34","35",,1,"10.1061/ciegag.0001371","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077506653&doi=10.1061%2fciegag.0001371&partnerID=40&md5=d2569da1aa437d4c3d1239b96c890fed","ASCE, ASCE World Headquarters, 1801 Alexander Bell Drive, Reston, VA, United States","Cardno, C.A., ASCE, ASCE World Headquarters, 1801 Alexander Bell Drive, Reston, VA, United States","A preflight, flight, and postflight drone system saves time and money in bridge inspections. It can also create a digital ""twin"" of bridges. © 2019 American Society of Civil Engineers.",,"Engineering; Industrial engineering; Bridge inspection; Drone system; Saves time; Drones; bridge; detection method; equipment; instrumentation",,,,,,,,,,,,,,,,,"Cardno, C.A.; ASCE, 1801 Alexander Bell Drive, United States",,,"American Society of Civil Engineers (ASCE)",,,,,08857024,,CIEGA,,"English","Civ. Eng. Magazine Arch.",Review,"Final","",Scopus,2-s2.0-85077506653
"Yoon S., Lee S., Kye S., Kim I.-H., Jung H.-J., Spencer B.F., Jr","57202858776;57202134166;57202363434;56555571800;57304560200;57456552900;","Seismic fragility analysis of deteriorated bridge structures employing a UAV inspection-based updated digital twin",2022,"Structural and Multidisciplinary Optimization","65","12","346","","",,,"10.1007/s00158-022-03445-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142392879&doi=10.1007%2fs00158-022-03445-0&partnerID=40&md5=eb99b58a1c0492508d033072a27f5287","Department of Artificial Intelligence, Hannam University, 70 Hannam-ro, Daedeok-gu, Daejeon, 34430, South Korea; Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, 44919, South Korea; Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea; Department of Civil Engineering, Kunsan National University, 558 Daehak-ro, Kunsan, 54150, South Korea; Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL  61801, United States","Yoon, S., Department of Artificial Intelligence, Hannam University, 70 Hannam-ro, Daedeok-gu, Daejeon, 34430, South Korea; Lee, S., Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, 44919, South Korea; Kye, S., Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea; Kim, I.-H., Department of Civil Engineering, Kunsan National University, 558 Daehak-ro, Kunsan, 54150, South Korea; Jung, H.-J., Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea; Spencer, B.F., Jr, Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL  61801, United States","Aging bridges require regular inspection due to performance deterioration. For this purpose, numerous researchers have considered the use of unmanned aerial vehicle (UAV) systems for structural health monitoring and inspection. However, present UAV-based inspection methods only represent the type and extent of external damage, but does not assess the seismic performance. In this study, a seismic fragility analysis of deteriorated bridges employing a UAV inspection-based updated digital twin is proposed. The proposed method consists of two phases: (1) bridge condition assessment using UAV inspection for updating the digital twin and (2) seismic fragility analysis based on the updated digital twin. To update the digital twin, the bridge damage grade is assigned based on the UAV inspection, and subsequently, the corresponding damage index is calculated. The damage index is utilized as a percentage reduction in the stiffness of finite element (FE) model, based on a previously proposed research. Using the updated digital twin, the seismic fragility analysis is conducted with different earthquake motions and magnitudes. To demonstrate the proposed method, an inservice pre-stressed concrete box bridge is examined. In particular, the seismic fragility curves of deteriorated bridges are compared with those of intact bridges. The numerical results show that the maximum failure probability of the deteriorated bridges is 3.6% higher than that of intact bridges. Therefore, the proposed method has the potential to updated the digital twin effectively using UAV inspection, allowing for seismic fragility analysis of deteriorated bridges to be conducted. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.","Bridge condition assessment; Deteriorated bridge structure; Digital twin; Seismic fragility analysis; Unmanned aerial vehicle (UAV); Visual inspection","Concretes; Damage detection; Deterioration; Earthquakes; Structural health monitoring; Unmanned aerial vehicles (UAV); Aerial vehicle; Bridge condition assessment; Bridge structures; Condition assessments; Damage index; Deteriorated bridge structure; Seismic fragility analysis; Unmanned aerial vehicle; Vehicle inspections; Visual inspection; Antennas",,,,,"Ministry of Education, MOE: 2022R1I1A1A01056139; National Research Foundation of Korea, NRF","This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. 2022R1I1A1A01056139)",,,,,,,,,,"Barroso, L.R., Rodriguez, R., Damage detection utilizing the damage index method to a benchmark structure (2004) J Eng. Mech., 130 (2), pp. 142-151; Bolourian, N., Hammad, A., LiDAR-equipped UAV path planning considering potential locations of defects for bridge inspection (2020) Autom Constr, 117, p. 103250; Cha, Y.J., Buyukozturk, O., Structural damage detection using modal strain energy and hybrid multiobjective optimization (2015) Comput-Aided Civil Infrastruct Eng, 30 (5), pp. 347-358; Chan, T.H., Chan, J.H., The use of eccentric beam elements in the analysis of slab-on-girder bridges (1999) Struct Eng Mech, 8 (1), pp. 85-102; Chen, S., Laefer, D.F., Mangina, E., Zolanvari, S.I., Byrne, J., UAV bridge inspection through evaluated 3D reconstructions (2019) J Bridg Eng, 24 (4), p. 05019001; Chung, W., Sotelino, E.D., Nonlinear finite-element analysis of composite steel girder bridges (2005) J Struct Eng, 131 (2), pp. 304-313; Chung, W., Phuvoravan, K., Liu, J., Sotelino, E.D., Applicabiliy of the simplified load distribution factor equation to PSC girder bridges (2005) KSCE J Civ Eng, 9 (4), pp. 313-319; Cornell, C.A., Jalayer, F., Hamburger, R.O., Foutch, D.A., Probabilistic basis for 2000 SAC federal emergency management agency steel moment frame guidelines (2002) J Struct Eng, 128 (4), pp. 526-533; Duque, L., (2017) UAV-based bridge inspection and computational simulations, , South Dakota State University; Dutta, A., Mander, J., Seismic fragility analysis of highway bridges (1998) Proceedings of the INCEDE-MCEER center-to-center project workshop on earthquake engineering Frontiers in transportation systems; Elnashai, A., Papanikolaou, V., Lee, D., Zeus-NL-A system for inelastic analysis of structures-user manual. Mid-America Earthquake (MAE) Center (2010) Department of Civil and Environmental Engineering, , (,), University of Illinois at Urbana-Champaign, Urbana; Ghosh, J., Sood, P., Consideration of time-evolving capacity distributions and improved degradation models for seismic fragility assessment of aging highway bridges (2016) Reliab Eng Syst Saf, 154, pp. 197-218; González-Aguilera, D., Gómez-Lahoz, J., Dimensional analysis of bridges from a single image (2009) J Comput Civ Eng, 23 (6), pp. 319-329; He, K., Gkioxari, G., Dollár, P., Girshick, R., (2017) Mask r-cnn. 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Structures Congress,: Metropolis and Beyond; Mangalathu, S., Choi, E., Park, H.C., Jeon, J.-S., Probabilistic seismic vulnerability assessment of tall horizontally curved concrete bridges in California (2018) J Perform Constr Facil, 32 (6), p. 04018080; Maria, A., Tommaselli, G., Luis, M., Reiss, L., A photogrammetric method for single image orientation and measurement (2005) Photogramm Eng Remote Sens, 71 (6), pp. 727-732; Martínez, J., Ordóñez, C., Arias, P., Armesto, J., Non-contact 3D measurement of buildings through close range photogrammetry and a laser distance meter (2011) Photogramm Eng Remote Sens, 77 (8), pp. 805-811; Metni, N., Hamel, T., A UAV for bridge inspection: Visual servoing control law with orientation limits (2007) Autom Constr, 17 (1), pp. 3-10; (2019) Detail guidelines for safety inspections and full safety examinations, , Ministry of Land Infrastructure and Transport, Korea Authority of Land and Infrastructure Safety; (2020) Special act on the safety and maintenance of Facilities, , Ministry of Land Infrastructure and Transport; 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Mutltidiscip. Opt.",Article,"Final","",Scopus,2-s2.0-85142392879
"Coelho Lima I., Robens-Radermacher A., Titscher T., Kadoke D., Koutsourelakis P.-S., Unger J.F.","57850251100;57201291070;56974336400;55315271700;7801591128;9133376300;","Bayesian inference for random field parameters with a goal-oriented quality control of the PGD forward model’s accuracy",2022,"Computational Mechanics","70","6",,"1189","1210",,,"10.1007/s00466-022-02214-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136181485&doi=10.1007%2fs00466-022-02214-6&partnerID=40&md5=5a54cc9ae5a4734ef04873a63dcc43b1","Modelling and Simulation, Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 87, Berlin, Berlin, 12205, Germany; Sensors, Measurement and Testing Methods, Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 87, Berlin, Berlin, 12205, Germany; Continuum Mechanics, Technical University of Munich, Boltzmannstr. 15, Bavaria, Garching, 85748, Germany","Coelho Lima, I., Modelling and Simulation, Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 87, Berlin, Berlin, 12205, Germany; Robens-Radermacher, A., Modelling and Simulation, Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 87, Berlin, Berlin, 12205, Germany; Titscher, T., Modelling and Simulation, Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 87, Berlin, Berlin, 12205, Germany; Kadoke, D., Sensors, Measurement and Testing Methods, Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 87, Berlin, Berlin, 12205, Germany; Koutsourelakis, P.-S., Continuum Mechanics, Technical University of Munich, Boltzmannstr. 15, Bavaria, Garching, 85748, Germany; Unger, J.F., Modelling and Simulation, Bundesanstalt für Materialforschung und -prüfung (BAM), Unter den Eichen 87, Berlin, Berlin, 12205, Germany","Numerical models built as virtual-twins of a real structure (digital-twins) are considered the future of monitoring systems. Their setup requires the estimation of unknown parameters, which are not directly measurable. Stochastic model identification is then essential, which can be computationally costly and even unfeasible when it comes to real applications. Efficient surrogate models, such as reduced-order method, can be used to overcome this limitation and provide real time model identification. Since their numerical accuracy influences the identification process, the optimal surrogate not only has to be computationally efficient, but also accurate with respect to the identified parameters. This work aims at automatically controlling the Proper Generalized Decomposition (PGD) surrogate’s numerical accuracy for parameter identification. For this purpose, a sequence of Bayesian model identification problems, in which the surrogate’s accuracy is iteratively increased, is solved with a variational Bayesian inference procedure. The effect of the numerical accuracy on the resulting posteriors probability density functions is analyzed through two metrics, the Bayes Factor (BF) and a criterion based on the Kullback-Leibler (KL) divergence. The approach is demonstrated by a simple test example and by two structural problems. The latter aims to identify spatially distributed damage, modeled with a PGD surrogate extended for log-normal random fields, in two different structures: a truss with synthetic data and a small, reinforced bridge with real measurement data. For all examples, the evolution of the KL-based and BF criteria for increased accuracy is shown and their convergence indicates when model refinement no longer affects the identification results. © 2022, The Author(s).","Digital twin; Goal-oriented; Proper Generalized Decomposition; Random field; Variational inference","Bayesian networks; Inference engines; Iterative methods; Parameter estimation; Probability density function; Stochastic models; Stochastic systems; Structural optimization; Bayes factor; Bayesian inference; Field parameters; Forward modeling; Goal-oriented; Model identification; Numerical accuracy; Proper generalized decompositions; Random fields; Variational inference; Quality control",,,,,"Deutsche Forschungsgemeinschaft, DFG: 326557591","The authors thanks the German Research Foundation (DFG) especially for the funded project 326557591. The real measurement data used here were undertaken by D. Kadoke and the crack measurements (Fig. right) by Dr. G. Hüsken & S. Pirskawetz as part of the BLEIB project within the Federal Institute for Materials Research and Testing (BAM) and funded by the Federal Ministry for Economic Affairs and Climate Action (BMWK), Germany.",,,,,,,,,,"Chinesta, F., Ammar, A., Cueto, E., Recent advances and new challenges in the use of the proper generalized decomposition for solving multidimensional models (2010) Arch Computat Methods Eng, 17 (4), pp. 327-350; Chinesta, F., Cueto, E., Abisset-Chavanne, E., Duval, J.L., Khaldi, F.E., Virtual, digital and hybrid twins: A new paradigm in data-based engineering and engineered data (2018) Arch Computat Methods Eng, 27 (1), pp. 105-134; Chappell, M.A., Groves, A.R., Whitcher, B., Woolrich, M.W., Variational Bayesian inference for a nonlinear forward model (2009) IEEE Trans Signal Process, 57 (1), pp. 223-236; Robens-Radermacher, A., Held, F., Coelho Lima, I., Titscher, T., Unger, J.F., Efficient identification of random fields coupling Bayesian inference and PGD reduced order model for damage localization (2021) Proc Appl Math Mech, 20 (1); Mohammad-Djafari, A., From deterministic to probabilistic approaches to solve inverse problems (1998) Bayesian Inference for Inverse Problems, 3459, pp. 2-11. , https://doi.org/10.1117/12.323787, Mohammad-Djafari, A., SPIE, International Society for Optics and Photonics; Tarantola, A., Inverse Problem Theory and Methods for Model Parameter Estimation (2005) Society for Industrial and Applied Mathematics, pp. 1-358. , https://doi.org/10.1137/1.9780898717921; Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E., Equation of state calculations by fast computing machines (1953) J Chem Phys, 21 (6), pp. 1087-1092; Hastings, W.K., Monte Carlo sampling methods using Markov chains and their applications (1970) Biometrika, 57 (1), pp. 97-109; Geman, S., Geman, D., Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images (1984) IEEE Trans Pattern Anal Mach Intell PAMI, 6 (6), pp. 721-741; Duane, S., Kennedy, A.D., Pendleton, B.J., Roweth, D., Hybrid Monte Carlo (1987) Phys Lett B, 195 (2), pp. 216-222; Salimans, T., Kingma, D.P., Welling, M., Markov chain Monte Carlo and variational inference: Bridging the gap (2015) ICML, pp. 1218-1226. , http://proceedings.mlr.press/v37/salimans15.html; Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., Saul, L.K., Introduction to variational methods for graphical models (1999) Mach Learn, 37 (2), pp. 183-233; Blei, D.M., Kucukelbir, A., McAuliffe, J.D., Variational inference: A review for statisticians (2017) . 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Robens-Radermacher, A.; Modelling and Simulation, Unter den Eichen 87, Berlin, Germany; email: annika.robens-radermacher@bam.de",,,"Springer Science and Business Media Deutschland GmbH",,,,,01787675,,CMMEE,,"English","Comput Mech",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85136181485
"Hughes A.J., Bull L.A., Gardner P., Dervilis N., Worden K.","57211513237;57194613339;57193994973;55210881700;7005669331;","On robust risk-based active-learning algorithms for enhanced decision support",2022,"Mechanical Systems and Signal Processing","181",,"109502","","",,,"10.1016/j.ymssp.2022.109502","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133905643&doi=10.1016%2fj.ymssp.2022.109502&partnerID=40&md5=43198425928b4a6952ff81485ee8a000","Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, S1 3JD, United Kingdom; The Alan Turing Institute, The British Library, 96 Euston RoadLondon  NW1 2DB, United Kingdom","Hughes, A.J., Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, S1 3JD, United Kingdom; Bull, L.A., The Alan Turing Institute, The British Library, 96 Euston RoadLondon  NW1 2DB, United Kingdom; Gardner, P., Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, S1 3JD, United Kingdom; Dervilis, N., Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, S1 3JD, United Kingdom; Worden, K., Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, S1 3JD, United Kingdom","Classification models are a fundamental component of physical-asset management technologies such as structural health monitoring (SHM) systems and digital twins. Previous work introduced risk-based active learning, an online approach for the development of statistical classifiers that takes into account the decision-support context in which they are applied. Decision-making is considered by preferentially querying data labels according to expected value of perfect information (EVPI). Although several benefits are gained by adopting a risk-based active learning approach, including improved decision-making performance, the algorithms suffer from issues relating to sampling bias as a result of the guided querying process. This sampling bias ultimately manifests as a decline in decision-making performance during the later stages of active learning, which in turn corresponds to lost resource/utility. The current paper proposes two novel approaches to counteract the effects of sampling bias: semi-supervised learning, and discriminative classification models. These approaches are first visualised using a synthetic dataset, then subsequently applied to an experimental case study, specifically, the Z24 Bridge dataset. The semi-supervised learning approach is shown to have variable performance; with robustness to sampling bias dependent on the suitability of the generative distributions selected for the model with respect to each dataset. In contrast, the discriminative classifiers are shown to have excellent robustness to the effects of sampling bias. Moreover, it was found that the number of inspections made during a monitoring campaign, and therefore resource expenditure, could be reduced with the careful selection of the statistical classifiers used within a decision-supporting monitoring system. © 2022 The Author(s)","Active learning; Decision-making; Digital twins; Risk; Sampling bias; Structural health monitoring; Value of information","Classification (of information); Decision support systems; E-learning; Health; Health risks; Learning algorithms; Learning systems; Sampling; Structural health monitoring; Supervised learning; Active Learning; Classification models; Decision supports; Decisions makings; Performance; Risk-based; Sampling bias; Semi-supervised learning; Statistical classifier; Value of information; Decision making",,,,,"Alan Turing Institute, ATI; UK Research and Innovation, UKRI: EP/W006022/1; Engineering and Physical Sciences Research Council, EPSRC: EP/R003625/1, EP/R004900/1, EP/R006768/1","The authors would like to acknowledge the support of the UK EPSRC via the Programme Grants EP/R006768/1 and EP/R004900/1 . KW would also like to acknowledge support via the EPSRC Established Career Fellowship, UK EP/R003625/1 . LAB was supported by Wave 1 of The UKRI Strategic Priorities Fund, UK under the EPSRC Grant EP/W006022/1 , particularly the Ecosystems of Digital Twins, UK theme within that grant and The Alan Turing Institute, UK .",,,,,,,,,,"Farrar, C.R., Worden, K., Structural Health Monitoring: A Machine Learning Perspective (2013), John Wiley & Sons, Ltd; Grieves, M., Vickers, J., Digital twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems (2017), pp. 85-113. , Transdisciplinary Perspectives on Complex Systems, Berlin, Germany; Niederer, S.A., Sacks, M.S., Girolami, M., Willcox, K., Scaling digital twins from the artisanal to the industrial (2021) Nat. Comput. Sci., 1 (5), pp. 313-320; Nielsen, J., Risk-Based Operation and Maintenance of Offshore Wind Turbines (2013), (Ph.D. thesis) Aalborg University; Hovgaard, M.K., Brincker, R., Limited memory influence diagrams for structural damage detection decision-making (2016) J. Civ. Struct. Health Monit., 6 (2), pp. 205-215; Hughes, A.J., Barthorpe, R.J., Dervilis, N., Farrar, C.R., Worden, K., A probabilistic risk-based decision framework for structural health monitoring (2021) Mech. Syst. Signal Process., 150; Bull, L.A., Rogers, T.J., Wickramarachchi, C., Cross, E.J., Worden, K., Dervilis, N., Probabilistic active learning: An online framework for structural health monitoring (2019) Mech. Syst. Signal Process., 134; Martínez-Arellano, G., Ratchev, S., Towards an active learning approach to tool condition monitoring with Bayesian deep learning (2019) ECMS; Chakraborty, D., Kovvali, N., Papandreou-Suppappola, A., Chattopadhyay, A., An adaptive learning damage estimation method for structural health monitoring (2015) J. Intell. Mater. Syst. Struct., 26 (2), pp. 125-143; Hughes, A.J., Bull, L.A., Gardner, P., Barthorpe, R.J., Dervilis, N., Worden, K., On risk-based active learning for structural health monitoring (2022) Mech. Syst. Signal Process., 167; Dasgupta, S., Two faces of active learning (2011) Theoret. Comput. Sci., 412 (19), pp. 1767-1781; Psorakis, I., Damoulas, T., Girolami, M., Multiclass relevance vector machines: Sparsity and accuracy (2010) IEEE Trans. Neural Netw., 21 (10), pp. 1588-1598; Schwenker, F., Trentin, E., Pattern classification and clustering: A review of partially supervised learning approaches (2014) Pattern Recognit. Lett., 37, pp. 4-14; Feng, C., Liu, M.Y., Kao, C.C., Lee, T.Y., Deep active learning for civil infrastructure defect detection and classification (2017) Computing in Civil Engineering 2017, pp. 298-306; Koller, D., Friedman, N., Probabilistic Graphical Models: Principles and Techniques (2009), MIT Press; Kjaerulff, U., Madsen, A., Bayesian Networks and Influence Diagrams: A Guide To Construction and Analysis (2008), Springer New York; Sucar, L., Probabilistic Graphical Models: Principles and Applications (2015), Springer London; Papakonstantinou, K.G., Shinozuka, M., Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part II: POMDP implementation (2014) Reliab. Eng. Syst. Saf., 130, pp. 214-224; Hamida, Z., Goulet, J.-A., Modeling infrastructure degradation from visual inspections using network-scale state-space models (2020) Struct. Control Health Monit., 27 (9); Dasgupta, S., Hsu, D., Hierarchical sampling for active learning (2008) Proceedings of the 25th International Conference on Machine Learning ACM; Valkonen, A., Glisic, B., Evaluation tool for assessing the influence of structural health monitoring on decision-maker risk preferences (2021) Struct. Health Monit.; Vega, M.A., Todd, M.D., A variational Bayesian neural network for structural health monitoring and cost-informed decision-making in miter gates (2020) Struct. Health Monit.; Hughes, A.J., Barthorpe, R.J., Worden, K., On health-state transition models for risk-based structural health monitoring (2022) Dynamics of Civil Structures, Volume 2, pp. 49-60. , Springer International Publishing; Fawcett, T., An introduction to ROC analysis (2006) Pattern Recognit. Lett., 27 (8), pp. 861-874; Damoulas, T., Girolami, M., Probabilistic multi-class multi-kernel learning: On protein fold recognition and remote homology detection (2008) Bioinformatics, 24 (10), pp. 1264-1270; Chapelle, O., Scholkopf, B., Zien, A., Semi-Supervised Learning (2006), MIT Press; Chen, S., Cerda, F., Rizzo, P., Bielak, J., Garrett, J., Kovačević, J., Semi-supervised multiresolution classification using adaptive graph filtering with application to indirect bridge structural health monitoring (2014) IEEE Trans. Signal Process., 62 (11), pp. 2879-2893; Bull, L.A., Worden, K., Dervilis, N., Towards semi-supervised and probabilistic classification in structural health monitoring (2021) Mech. Syst. Signal Process., 140; Dempster, A.P., Laird, N.M., Rubin, D.B., Maximum likelihood from incomplete data via the EM algorithm (1977) J. R. Stat. Soc., 39 (1), pp. 1-38; Einicke, G., Smoothing, Filtering and Prediction: Estimating the Past, Present and Future (2012), BoD–Books on Demand; Binder, J., Murphy, K., Russell, S., Space-efficient inference in dynamic probabilistic networks (1997) Proceedings of the Fifteenth International Joint Conference on Artifical Intelligence, IJCAI'97, 2, pp. 1292-1296. , Morgan Kaufmann Publishers Inc. San Francisco, CA, USA; Cortes, C., Vapnik, V., Support-vector networks (1995) Mach. Learn., 20 (3), pp. 273-297; Platt, J., Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods (1999) Adv. Large Margin Classif., 10 (3), pp. 61-74; Tipping, M.E., Sparse Bayesian learning and the relevance vector machine (2001) J. Mach. Learn. Res., 1, pp. 211-244; Damoulas, T., Girolami, M., Combining feature spaces for classification (2009) Pattern Recognit., 42 (11), pp. 2671-2683; Manocha, S., Girolami, M., An empirical analysis of the probabilistic k-nearest neighbour classifier (2007) Pattern Recognit. Lett., 28 (13), pp. 1818-1824; Maeck, J., De Roeck, G., Description of Z24 benchmark (2003) Mech. Syst. Signal Process., 17 (1), pp. 127-131; Maeck, J., Peeters, B., De Roeck, G., Damage identification on the Z24-bridge using vibration monitoring (2001) Smart Mater. Struct., 10 (3), pp. 512-517; De Roeck, G., The state-of-the-art of damage detection by vibration monitoring: the SIMCES experience (2003) Struct. Control Health Monit., 10 (2), pp. 127-134; Peeters, B., De Roeck, G., One-year monitoring of the Z24-bridge: environmental effects versus damage events (2001) Earthq. Eng. Struct. Dyn., 30 (2), pp. 149-171; Bull, L.A., Towards Probabilistic and Partially-Supervised Structural Health Monitoring (2020), (Ph.D. thesis) University of Sheffield; Worden, K., Cross, E.J., Barthorpe, R.J., Wagg, D.J., Gardner, P., On digital twins, mirrors, and virtualizations: Frameworks for model verification and validation (2020) ASCE-ASME J. Risk Uncert. Engrg. Sys. Part B Mech. Engrg., 6 (3); Gardner, P., Dal Borgo, M., Ruffini, V., Hughes, A.J., Zhu, Y., Wagg, D.J., Towards the development of an operational digital twin (2020) Vibration, 3 (3), pp. 235-265; Tsialiamanis, G., Wagg, D.J., Dervilis, N., Worden, K., On generative models as the basis for digital twins (2021) Data-Centric Eng., 2; Gardner, P., Bull, L.A., Gosliga, J., Dervilis, N., Worden, K., Foundations of population-based SHM, Part III: Heterogeneous populations – mapping and transfer (2021) Mech. Syst. Signal Process., 148; Murphy, K.P., Machine Learning: A Probabilistic Perspective (2012), MIT Press; Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D., Bayesian Data Analysis (2013), Chapman and Hall/CRC; Barber, D., Bayesian Reasoning and Machine Learning (2012), Cambridge University Press; Tipping, M.E., Faul, A.C., Fast marginal likelihood maximisation for sparse Bayesian models (2003) Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, R4, pp. 276-283. , Bishop C.M. Frey B.J. PMLR","Hughes, A.J.; Dynamics Research Group, United Kingdom; email: ajhughes2@sheffield.ac.uk",,,"Academic Press",,,,,08883270,,MSSPE,,"English","Mech Syst Signal Process",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85133905643
"Kusimba B.A., Rinzin T., Banno Y., Kinoshita K.","57979532100;57697436000;57208163754;57772942600;","Condition Assessment and Adaptation of Bailey Bridges as a Permanent Structures",2022,"Applied Sciences (Switzerland)","12","22","11673","","",,,"10.3390/app122211673","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142476503&doi=10.3390%2fapp122211673&partnerID=40&md5=d1bf6f72faca4b2884e20ff32bd9654a","Department of Civil Engineering, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan; Department of Roads, Ministry of Works and Human Settlement, Zhemgang, Gomphu, 34001, Bhutan","Kusimba, B.A., Department of Civil Engineering, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan; Rinzin, T., Department of Roads, Ministry of Works and Human Settlement, Zhemgang, Gomphu, 34001, Bhutan; Banno, Y., Department of Civil Engineering, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan; Kinoshita, K., Department of Civil Engineering, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan","The present study assessed the Bailey Bridge’s condition and investigated its adaptation as a permanent structure, targeted the Acrow Bailey Bridge in Japan. Field diagnostic loading experiments were performed under various loading conditions, such as dynamic and static loading tests. The onsite data were obtained using a transducer, friction strain gauge, target measurements for the image processing approach, and accelerometer. From the field measurements, the deflection and stresses of the bridge were found to operate within the linear elastic region. The bridge was then accurately modeled based on the in situ geometric configuration of the bridge, and Finite Element Analysis was performed. The model’s accuracy was validated with the onsite data under the linear elastic domain. The model was deployed to check for resistance of critical members. A nonlinear analysis based on the linear and nonlinear buckling method was performed to determine the subject bridge’s Serviceability Limit State and Ultimate Limit State. The results showed that the first out-of-plane eigenvalue buckling analysis could monolithically assess bridge members. Further, the study established digital twin models resolve for historical data through in situ modeling measurements. Therefore, the findings obtained in this study highlight the bridge’s Structural Health Condition, bearing capacity, and propose a framework for adaptation as a permanent structure. © 2022 by the authors.","bailey bridge; bearing capacity; bending moment; deflection; deformation; historical data; static and dynamic loading",,,,,,,,,,,,,,,,,"Parivallal, S., Narayanan, T., Ravisankar, K., Kesavan, K., Maji, S., Instrumentation and Response Measurement of a Double-Lane Bailey Bridge during Load Test (2005) Strain, 41, pp. 25-30; Khounsida, T., Takafumi, N., Shozo, N., Toshihiro, O., Khampaseuth, T., Study on Static and Dynamic Behavior of Bailey Bridge (2019) Adv. Struct. Eng. Mech, 46, pp. 9425-9428; Bailey Panel Bridge System—Triple Truss Single Storey Class 100, , https://www.sindorf.nl/Portals/0/BAILEYBRIDGES.pdf, Available online; Yi, P., Vaghela, G., Andrew, B., Condition Assessment And Load Rating of Arched Bailey Bridge Proceedings of the Austroads Bridge Conference, pp. 1-11. , Sydney, NSW, Australia, 22–24 October 2014; King, W.S., Wu, S.M., Duan, L., Laboratory Load Tests and Analysis of Bailey Bridge Segments (2013) J. Bridg. Eng, 18, pp. 957-968; Khounsida, T., Nishikawa, T., Nakamura, S., Okumatsu, T., Thepvongsa, K., Experimental and Analytical Study on Dynamic Behavior of Bailey Bridge (2020) Proc. Constr. Steel, 28, pp. 771-777; https://www.nacinc.jp/analysis/software/movias-neo/?add_list=MOVIAS+Neo, Available online; Jatmiko, J., Psimoulis, P., Deformation Monitoring of a Steel Structure Using 3D Terrestrial Laser Scanner (TLS) Proceedings of the 24th International Workshop on Intelligent Computing in Engineering, 2017, pp. 168-177. , Nottingham, UK, 12 July 2017; (2020) Precise and Flexible Strain Gauges, p. 96. , Tokyo Measuring Instruments Ltd., Tokyo, Japan; (1997) Fatigue of Steel Bridge, pp. 47-309. , Japan Road Association, Tokyo, Japan; Godoi, F.C., Prakash, S., Bhandari, B.R., (2005) Prefabricated Steel Bridge Systems, 1, pp. 1-261. , FHWA SOLICITATION NO. DTFH61-03-R-00113, Structure Design and Rehabilitation, Inc., Gaithersburg, MD, USA; Masatoshi, N., Yuki, C., Ichiro, A., Topological Optimum Shape of a Fundamental Module of the Periodic Structure and Cantilever Bridge (2021) J. Struct. Eng, 67A, pp. 90-98; Joiner, C.J.H., The Story of the Bailey Bridge (2011) Proc. Inst. Civ. Eng.-Eng. Hist. Herit, 164, pp. 65-72; (1986) Bailey Bridge, pp. 3-349. , Field Manual—TM 5-277, Department of the Army, Washington, DC, USA; Gómez-Martínez, R., Sánchez-García, R., Escobar-Sánchez, J.A., Arenas-García, L.M., Mendoza-Salas, M.A., Rosales-González, O.N., Monitoring Two Cable-Stayed Bridges during Load Tests with Fiber Optics (2021) Structures, 33, pp. 4344-4358; Umekawa, Y., Suganuma, H., Bridge Displacement Monitoring Using Acceleration Measurement and Development of Efficient Bridge Management System Proceedings of the IABSE Symp. Nantes 2018 Tomorrow’s Megastructures, , Nantes, France, 19–21 September 2018; Lichti, D.D., Gordon, S.J., Stewart, M.P., Franke, J., Tsakiri, M., Comparison of Digital Photogrammetry and Laser Scanning (2002) ISPRS J. Photogramm. Remote Sens, pp. 39-44; https://tml.jp/e/product/transducers/displacement_high.html, Available online; https://ttes.co.jp/service/integral/, Available online; Umekawa, Y., Hisatada, S., Kinoshita, K., Ono, T., A Study on Displacement Monitoring of Bridges Using Acceleration Data Associated with Vehicle Traffic Proceedings of the 72nd Annual Conference of Japan Society of Civil Engineers JSCE, 8, pp. 699-700. , Kyushu, Japan, 11 September 2017; Shimosato, T., Tai, M., Hisatada, S., Umekawa, Y., Hiyama, Y., Deflection Measurement of Bridge Using TWM System Proceedings of the Japan Society of Civil Engineers Western Branch Okinawa Meeting, 7th Technical Research Conference, pp. 146-147. , JSCE, Okinawa, Japan, 7–11 May 2018; Ma, M.J., Dynamic Load Test Analysis for Continuous Steel Bridge (2013) Appl. Mech. Mater, 275–277, pp. 1078-1081; Paeglite, I., Paeglitis, A., Smirnovs, J., Dynamic Amplification Factor for Bridges With Span Length From 10 To 35 Meters (2015) Eng. Struct. Technol, 6, pp. 151-158; Bruls, A., Calgaro, J.A., Mathieu, H., Prat, M., (1996) ENV1991–Part 3: The Main Models of Traffic Loads on Bridges: Background Studies, pp. 215-228. , IABSE Colloquim, Delft, The Netherlands; Ghavami, P., (1956) Mechanics of Materials: An Introduction to Engineering Technology, 78. , Springer, New York, NY, USA; Gross, D., Ehlers, W., Wriggers, P., Schröder, J., Müller, R., (2016) Mechanics of Materials-Formulas and Problems: Engineering Mechanics 2, 2, pp. 1-212. , Springer, Berlin/Heidelberg, Germany; Maros, H., Juniar, S., (2016) Inspection and Maintenance of Steel Girders, , 3rd ed., Indian Railways Institute of Civil Engg, Pune, India; (2010) AASHTO LRFD Bridge Design Specifications, , American Association of State Highway and Transportation Officials, Washington, DC, USA; Yuji, I., Nakamura, S., Kusaba, T., Nishikawa, T., Field Measurement and Structural Analysis of Bailey Bridge for Understanding Fundamental Load-Bearing Configuration (2022) Proceedings of the Japan Society of Civil Engineering-West, pp. 45-46. , I-023, JSCE, Tokyo, Japan; Benčat, J., Kohár, R., Bridges Subjected to Dynamic Loading (2018) Bridge Engineering, , Intech, Rijeka, Croatia; Kalin, J., Žnidarič, A., Anžlin, A., Kreslin, M., Measurements of Bridge Dynamic Amplification Factor Using Bridge Weigh-in-Motion Data (2021) Struct. 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Mater, 815, pp. 229-234; Holmes, J.D., (2018) Wind Loading of Structures, , 2nd ed., Spon Press, New York, NY, USA; Çiftçioğlu, A.Ö., Yildizel, S.A., Yildirim, M.S., Doğan, E., Wind Load Design of Hangar-Type Closed Steel Structures with Different Roof Pitches Using Abaqus CAE Software (2017) TEM J, 6, pp. 336-341; https://www.fhwa.dot.gov/bridge/lrfd/us_ds8.cfm#designstep86_5, Available online; Poddaeva, O., Fedosova, A., Gribach, J., The Study of Wind Effects on the Bridge Constructions (2019) E3S Web Conf, 97, p. 03030; Gorenc, B.E., Tinyou, R., Syam, A.A., (2005) Steel Designers’ Handbook, , 7th ed., UNSW Press, Sydney, Australia; Ochshorn, J., (2009) Structural Elements for Architects and Builders: Design of Columns, Beams, and Tension Elements in Wood, Steel, and Reinforced Concrete, , Elsevier, New York, NY, USA; (2003) American Society of Civil Engineers Design Loads on Structures During Construction American Society of Civil Engineers Design Loads on Structures During Construction, , Amer Society of Civil Engineers, Reston, VA, USA; Kumamoto, T., Seismic Hazard Maps of Japan and Computational Differences in Models and Parameters (1999) Geogr. Rev. Jpn. Ser. B, 72, pp. 135-161; Dassault Systèmes (2012) Abaqus CAE User’s Manual, pp. 1-1174. , Simulia, Johnston, RI, USA; Chakrabarty, J., (2006) Theory of Plasticity, , 3rd ed., Elsevier Butterworth-Heinemann, Bington, Singapore; Sun, S., Lei, G., Sun, Z., Dynamic and Static Load Tests on a Large-Span Rigid-Frame Bridge (2019) Math. Model. Eng. Probl, 6, pp. 409-414; Miki, C., (2000) Civil Engineering: Steel Structures, , 10th ed., Kyoritsu Publishing, Tokyo, Japan; (2001) Structural Use of Steel Work in Buildings Part 1, , BSI, London, UK","Kusimba, B.A.; Department of Civil Engineering, 1-1 Yanagido, Japan; email: z3921007@edu.gifu-u.ac.jp",,,"MDPI",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85142476503
"Dobs T., Elsotohy M., Jaeschke J., Sehr F., Strogies J., Wilke K.","57191580417;57949433700;24923329200;57215285682;55276823900;36890017400;","Multi-domain system level modeling approach for assessment of degradation behaviour under thermal and thermo-mechanical stress",2022,"Microelectronics Reliability","138",,"114710","","",,,"10.1016/j.microrel.2022.114710","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142439217&doi=10.1016%2fj.microrel.2022.114710&partnerID=40&md5=81eb06a9f56bd1f54c6555c99faa0dfc","Siemens AG, Berlin, Germany; Fraunhofer Institute for Reliability and Microintegration (IZM), Berlin, Germany","Dobs, T., Siemens AG, Berlin, Germany; Elsotohy, M., Fraunhofer Institute for Reliability and Microintegration (IZM), Berlin, Germany; Jaeschke, J., Fraunhofer Institute for Reliability and Microintegration (IZM), Berlin, Germany; Sehr, F., Fraunhofer Institute for Reliability and Microintegration (IZM), Berlin, Germany; Strogies, J., Siemens AG, Berlin, Germany; Wilke, K., Siemens AG, Berlin, Germany","Within the scope of this work, a multi-domain system modeling approach that is compatible with various electronical components and subsystems used in automotive and railway applications is to be developed. This approach serves the development of a hybrid, model-based condition monitoring of complex electronic and mechatronic systems. It aims at the implementation of condition monitoring in relevant applications of automotive and railway technology, e.g., safety-relevant electronic systems for train control and control units of electrified automobiles. Furthermore, the multi-physics modeling of the mentioned electronic components is performed using different object-oriented simulation environments, such as Simulink and OpenModelica1 in order to obtain as much information as possible that is relevant for the reliability assessment of the modeled systems. The approach is applied on two common example circuits used in complex electronic systems, a clocking circuit with a crystal oscillator and a full-wave bridge rectifier circuit. Moreover, the investigated circuits as well as the degradation behaviour under external loads during the operational lifetime in order to model the functionality are demonstrated. © 2022 Elsevier Ltd","Condition monitoring; Digital twin; Object-oriented modeling; System reliability modeling","Crystal oscillators; Railroads; Complex electronic systems; Degradation behavior; Modeling approach; Multi-domains; Object oriented modelling; System reliability models; System-level modeling; Systems modeling approach; Thermal mechanical stress; Thermo-mechanical stress; Condition monitoring",,,,,"21018","This work is part of the public funded project SesiM and supported by the Federal Ministry for Economic Affairs and Climate Action Germany ( FKZ 19|21018 ).",,,,,,,,,,"Dempsey, M., (2003), pp. 3-4. , Automatic translation of Simulink models into Modelica using Simelica and the Advanced Blocks library. Proc. 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Reliab.",Article,"Final","",Scopus,2-s2.0-85142439217
"Febrianto E., Butler L., Girolami M., Cirak F.","56493217600;55795448200;7005215170;6602445202;","Digital twinning of self-sensing structures using the statistical finite element method",2022,"Data-Centric Engineering","3","3","e31","","",,,"10.1017/dce.2022.28","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141456151&doi=10.1017%2fdce.2022.28&partnerID=40&md5=d194732f86500890c15708ba91c4ef33","Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom; The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, United Kingdom; Lassonde School of Engineering, York University, Toronto, ON  M3J 1P3, Canada","Febrianto, E., Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom, The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, United Kingdom; Butler, L., The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, United Kingdom, Lassonde School of Engineering, York University, Toronto, ON  M3J 1P3, Canada; Girolami, M., Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom, The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, United Kingdom; Cirak, F., Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom, The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, United Kingdom","The monitoring of infrastructure assets using sensor networks is becoming increasingly prevalent. A digital twin in the form of a finite element (FE) model, as commonly used in design and construction, can help make sense of the copious amount of collected sensor data. This paper demonstrates the application of the statistical finite element method (statFEM), which provides a principled means of synthesizing data and physics-based models, in developing a digital twin of a self-sensing structure. As a case study, an instrumented steel railway bridge of length located along the West Coast Mainline near Staffordshire in the UK is considered. Using strain data captured from fiber Bragg grating sensors at 108 locations along the bridge superstructure, statFEM can predict the true system response while taking into account the uncertainties in sensor readings, applied loading, and FE model misspecification errors. Longitudinal strain distributions along the two main I-beams are both measured and modeled during the passage of a passenger train. The statFEM digital twin is able to generate reasonable strain distribution predictions at locations where no measurement data are available, including at several points along the main I-beams and on structural elements on which sensors are not even installed. The implications for long-term structural health monitoring and assessment include optimization of sensor placement and performing more reliable what-if analyses at locations and under loading scenarios for which no measurement data are available. © 2022 The Author(s).","Bayesian learning; digital twin; FBG sensing; physics-informed machine learning; statistical finite element method; structural health monitoring","Fiber Bragg gratings; Fiber optic sensors; Finite element method; Location; Machine learning; Sensor networks; Statistical Physics; Strain; Bayesian learning; FBG sensing; Finite element modelling (FEM); I beams; Machine-learning; Measurement data; Physic-informed machine learning; Self-sensing; Statistical finite element methods; Strain distributions; Structural health monitoring",,,,,"Alan Turing Institute, ATI; UK Research and Innovation, UKRI; Engineering and Physical Sciences Research Council, EPSRC: EP/T001569/1","This work was supported by Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the “Digital twins for complex engineering systems” theme within that grant, and The Alan Turing Institute.",,,,,,,,,,"Abdulkarem, M., Samsudin, K., Rokhani, F.Z., Rasid, M.F.A., Wireless sensor network for structural health monitoring: A contemporary review of technologies, challenges, and future direction (2020) Structural Health Monitoring, 19, pp. 693-735; Arendt, P.D., Apley, D.W., Chen, W., Quantification of model uncertainty: Calibration, model discrepancy, and identifiability (2012) Journal of Mechanical Design, 134, pp. 1009081-10090812; Beck, J.L., Bayesian system identification based on probability logic (2010) Structural Control and Health Monitoring, 17, pp. 825-847; Bolton, A., Enzer, M., Schooling, J., (2018) The Gemini Principles: Guiding Values for the National Digital Twin and Information Management Framework, , Technical report, Centre for Digital Built Britain and Digital Framework Task Group; Brownjohn, J.M.W., Structural health monitoring of civil infrastructure (2007) Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365, pp. 589-622; Butler, L.J., Lin, W., Xu, J., Gibbons, N., Elshafie, M.Z.E.B., Middleton, C.R., Monitoring, modeling, and assessment of a selfsensing railway bridge during construction (2018) Journal of Bridge Engineering, 23, pp. 1-16; Cirak, F., Long, Q., Subdivision shells with exact boundary control and non-manifold geometry (2011) International Journal for Numerical Methods in Engineering, 88, pp. 897-923; Cirak, F., Ortiz, M., Schröder, P., Subdivision surfaces: A new paradigm for thin-shell finite-element analysis (2000) International Journal for Numerical Methods in Engineering, 47, pp. 2039-2072; Cirak, F., Scott, M.J., Antonsson, E.K., Ortiz, M., Schröder, P., Integrated modeling, finite-element analysis, and engineering design for thin-shell structures using subdivision (2002) Computer-Aided Design, 34, pp. 137-148; De Battista, N., Cheal, N., Harvey, R., Kechavarzi, C., Monitoring the axial displacement of a high-rise building under construction using embedded distributed fibre optic sensors (2017) SHMII 2017-8th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Proceedings, pp. 1058-1067; Di, J., Ruan, X., Zhou, X., Wang, J., Peng, X., Fatigue assessment of orthotropic steel bridge decks based on strain monitoring data (2021) Engineering Structures, 228, p. 111437; Di Murro, V., Pelecanos, L., Soga, K., Kechavarzim, C., Morton, R., Distributed fibre optic long-term monitoring of concrete-lined tunnel section tt10 at cern (2016) International Conference on Smart Infrastructure and Construction; Frangopol, D.M., Soliman, M., Life-cycle of structural systems: Recent achievements and future directions (2016) Structure and Infrastructure Engineering, 12, pp. 1-20; Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B., (2013) Bayesian Data Analysis, , 3rd Edn. Boca Raton: CRC Press; Ghanem, R.G., Spanos, P.D., (1991) Stochastic Finite Elements: A Spectral Approach, , New York: Springer; Girolami, M., Febrianto, E., Yin, G., Cirak, F., The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions (2021) Computer Methods in Applied Mechanics and Engineering, 375, pp. 1135331-11353332; Huang, Y., Shao, C., Wu, B., Beck, J.L., Li, H., State-of-the-art review on Bayesian inference in structural system identification and damage assessment (2019) Advances in Structural Engineering, 22, pp. 1329-1351; Hughes, T.J.R., Cottrell, J.A., Bazilevs, Y., Isogeometric analysis: CAD, finite elements, NURBS, exact geometry and mesh refinement (2005) Computer Methods in Applied Mechanics and Engineering, 194, pp. 4135-4195; Kaipio, J., Somersalo, E., (2006) Statistical and Computational Inverse Problems, , New York: Springer; Kennedy, M.C., O'Hagan, A., Bayesian calibration of computer models (2001) Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63, pp. 425-464; Lau, F.D.-H., Adams, N.M., Girolami, M.A., Butler, L.J., Elshafie, M.Z.E.B., The role of statistics in data-centric engineering (2018) Statistics & Probability Letters, 136, pp. 58-62; Lin, W., Butler, L.J., Elshafie, M.Z.E.B., Middleton, C.R., Performance assessment of a newly constructed skewed halfthrough railway bridge using integrated sensing (2019) Journal of Bridge Engineering, 24, pp. 1-14; Lynch, J.P., An overview of wireless structural health monitoring for civil structures (2007) Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 365, pp. 345-372; MacKay, D.J.C., Bayesian interpolation (1992) Neural Computation, 4, pp. 415-447; MacKay, D.J.C., Comparison of approximate methods for handling hyperparameters (1999) Neural Computation, 11, pp. 1035-1068; Malekzadeh, M., Atia, G., Catbas, F.N., Performance-based structural health monitoring through an innovative hybrid data interpretation framework (2015) Journal of Civil Structural Health Monitoring, 5, pp. 287-305; Mikkola, P., Martin, O.A., Chandramouli, S., Hartmann, M., Pla, O.A., Thomas, O., Pesonen, H., Klami, A., Prior knowledge elicitation: The past, present, and future (2021) Preprint; Murphy, K.P., (2012) Machine Learning: A Probabilistic Perspective, , Cambridge: MIT Press; Nagel, J.B., Sudret, B., A unified framework for multilevel uncertainty quantification in Bayesian inverse problems (2016) Probabilistic Engineering Mechanics, 43, pp. 68-84; Oden, J.T., Moser, R., Ghattas, O., Computer predictions with quantified uncertainty, part I (2010) SIAM News, 43, pp. 1-3; Pasquier, R., Smith, I., Iterative structural identification framework for evaluation of existing structures (2016) Engineering Structures, 106, pp. 179-194; Rasheed, A., San, O., Kvamsdal, T., Digital twin: values, challenges and enablers from a modeling perspective (2020) IEEE Access, 8, pp. 21980-22012; Scarth, C., Adhikari, S., Cabral, P.H., Silva, G.H.C., Do Prado, A.P., Random field simulation over curved surfaces: Applications to computational structural mechanics (2019) Computer Methods in Applied Mechanics and Engineering, 345, pp. 283-301; Stuart, A.M., Inverse problems: A Bayesian perspective (2010) Acta Numerica, 19, pp. 451-559; Sudret, B., Der Kiureghian, A., (2000) Stochastic Finite Element Methods and Reliability: A State-of-the-Art Report, , Technical Report UCB/SEMM-2000/08, Department of Civil & Environmental Engineering, University of California, Berkeley; Tsialiamanis, G., Wagg, D.J., Dervilis, N., Worden, K., On generative models as the basis for digital twins (2021) Data-Centric Engineering 2; Worden, K., Cross, E., Barthorpe, R., Wagg, D., Gardner, P., On digital twins, mirrors, and virtualizations: Frameworks for model verification and validation (2020) ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 6 (3), p. 30902; Wu, R.-T., Jahanshahi, M.R., Data fusion approaches for structural health monitoring and system identification: Past, present, and future (2020) Structural Health Monitoring, 19, pp. 552-586; Zhang, Q., Sabin, M., Cirak, F., Subdivision surfaces with isogeometric analysis adapted refinement weights (2018) Computer-Aided Design, 102, pp. 104-114","Cirak, F.; Department of Engineering, Trumpington Street, United Kingdom; email: f.cirak@eng.cam.ac.uk",,,"Cambridge University Press",,,,,26326736,,,,"English","Data-Centric Eng.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85141456151
"Wang Z., Zheng P., Li X., Chen C.-H.","57204931556;56352424300;56455381400;25921980900;","Implications of data-driven product design: From information age towards intelligence age",2022,"Advanced Engineering Informatics","54",,"101793","","",,,"10.1016/j.aei.2022.101793","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140922158&doi=10.1016%2fj.aei.2022.101793&partnerID=40&md5=00a7cf449808d7457daed523b31b9a68","Department of Industrial and Manufacturing Systems Engineering, Beihang University, China; Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong; College of Mechanical Engineering, Donghua University, Shanghai, China; School of Mechanical and Aerospace Engineering, Nanyang Technological University639798, Singapore","Wang, Z., Department of Industrial and Manufacturing Systems Engineering, Beihang University, China; Zheng, P., Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong; Li, X., College of Mechanical Engineering, Donghua University, Shanghai, China; Chen, C.-H., School of Mechanical and Aerospace Engineering, Nanyang Technological University639798, Singapore","Data-driven design (D3), a new design paradigm benefited from advanced data analytics and computational intelligence, has gradually promoted the research of data-driven product design (DDPD) ever since 2000 s. In today's Intelligence Age, some theoretical and practical studies have tried to achieve the advanced intelligence capabilities in DDPD. However, to the authors' best knowledge, there is still a lack of a holistic summary of DDPD with chronological concern in the intelligence age. To bridge the gap, this research undertakes a literature review of DDPD publications from 2000 to date (19/09/2022), of which 172 relevant papers are discussed via bibliometric analysis and state-of-the-art analysis. The results shown that DDPD has vitality in the Intelligence Age by combining the cutting-edge digital technologies, such as AI, additive manufacturing, digital twin, and so on. Moreover, current DDPD studies could outperform the classical design methods on the well-defined tasks, but it still cannot master the creative/innovative design tasks which require the cognitive capability. This survey further highlights several future research potentials including cognitive intelligence-enabled design, end-to-end design integration, advanced design knowledge support, design for additive manufacturing, and sustainable smart product-service systems. It is hoped that this work can be regarded as a reference to understand the roadmap of DDPD and offer insights for the design practitioners to complete relevant tasks in today's intelligence age. © 2022 Elsevier Ltd","Cognitive computing; Computer intelligence; Data-driven design; Digital transformation; Product design and development","3D printers; Cognitive systems; Data Analytics; Industrial research; Metadata; Advanced intelligences; Cognitive Computing; Computer intelligences; Data analytics; Data driven; Data-driven design; Design paradigm; Digital transformation; Information age; Product design and development; Product design",,,,,"BZ2020049; 52005424","The authors wish to acknowledge the funding support from the National Natural Research Foundation of China (No. 52005424), and Jiangsu Provincial Policy Guidance Program (Hong Kong/Macau/Taiwan Science and Technology Cooperation, BZ2020049).",,,,,,,,,,"Martín-Peña, M.L., Díaz-Garrido, E., Sánchez-López, J.M., The digitalization and servitization of manufacturing: A review on digital business models (2018) Strategic Change, 27 (2), pp. 91-99; Urbach, N., Ahlemann, F., Böhmann, T., The Impact of Digitalization on the IT Department (2019) Bus. 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Ind., 125","Zheng, P.; Department of Industrial and Systems Engineering, Hong Kong; email: pai.zheng@polyu.edu.hk",,,"Elsevier Ltd",,,,,14740346,,,,"English","Adv. Eng. Inf.",Article,"Final","",Scopus,2-s2.0-85140922158
"Yang X., del Rey Castillo E., Zou Y., Wotherspoon L., Tan Y.","57455667800;57202917547;57830372300;35281084700;57830372400;","Automated semantic segmentation of bridge components from large-scale point clouds using a weighted superpoint graph",2022,"Automation in Construction","142",,"104519","","",,,"10.1016/j.autcon.2022.104519","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135409932&doi=10.1016%2fj.autcon.2022.104519&partnerID=40&md5=315ccbe067312586e55e30131ea4a397","Department of Civil and Environmental Engineering, University of Auckland, Auckland, 1023, New Zealand; College of Civil Engineering, Shenzhen University, Shenzhen, China","Yang, X., Department of Civil and Environmental Engineering, University of Auckland, Auckland, 1023, New Zealand; del Rey Castillo, E., Department of Civil and Environmental Engineering, University of Auckland, Auckland, 1023, New Zealand; Zou, Y., Department of Civil and Environmental Engineering, University of Auckland, Auckland, 1023, New Zealand; Wotherspoon, L., Department of Civil and Environmental Engineering, University of Auckland, Auckland, 1023, New Zealand; Tan, Y., College of Civil Engineering, Shenzhen University, Shenzhen, China","Deep learning techniques have the potential to provide versatile solutions for automated semantic segmentation of bridge point clouds, but previous studies were limited to small-scale bridge point clouds and focused on limited bridge component categories due to training sample scarcity. Additionally, no prior work considered the intrinsic data imbalance problem in the bridge dataset, with the points unequally distributed between the various components. This paper presents a weighted superpoint graph (WSPG) method, where bridge point clouds were firstly clustered into hundreds of semantically homogeneous superpoints that were then classified into different bridge components using PointNet and Graph Neural Networks. The WSPG method can recognize components directly from large-scale bridge point clouds and alleviate the data imbalance by leveraging a novel loss function that assigns weights according to the number of points contained in different bridge components. The effectiveness of the method was validated on both a real-world dataset with 5 categories of bridge components and a synthetic dataset with 8 categories of bridge components. Experiment results on the real-world dataset showed that the WSPG model achieved the best performance on all overall evaluation metrics of overall accuracy (OA: 99.43%), mean class accuracy (mAcc: 98.75%) and mean Intersection over Union (mIoU: 96.49%) compared to the existing cutting edge models such as PointNet, DGCNN and the original SPG. Additionally, the WSPG method also surpassed the cutting edge representatives in terms of mAcc and mIoU on the synthetic dataset, especially increasing the original SPG by 8.5% mAcc and 6.7% mIoU. The successful application of the proposed method will significantly improve upper-level tasks such as digital twining for existing bridges. © 2022 Elsevier B.V.","Bridge component recognition; Deep learning; Large-scale point clouds; Semantic segmentation; Weighted Superpoint Graph","Cutting tools; Deep neural networks; Graph neural networks; Semantic Segmentation; Bridge component recognition; Data imbalance; Deep learning; Graph methods; Large-scale point cloud; Large-scales; Point-clouds; Real-world datasets; Semantic segmentation; Weighted superpoint graph; Semantics",,,,,"University of Auckland: 3716476","The authors would like to acknowledge the support by University of Auckland FRDF Grant (Project No. 3716476 ).",,,,,,,,,,"R. 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Rebeiro de Faria Paiva Luiz Ricardo Cerri; Diakogiannis, F.I., Waldner, F., Caccetta, P., Wu, C., ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data (2020) ISPRS J. Photogramm. Remote Sens., 162, pp. 94-114","Yang, X.; Department of Civil and Environmental Engineering, New Zealand; email: fyan983@aucklanduni.ac.nz",,,"Elsevier B.V.",,,,,09265805,,AUCOE,,"English","Autom Constr",Article,"Final","",Scopus,2-s2.0-85135409932
"Wang X., Li Y., Wang B., Chen Z.","57932597900;57932598100;57931900000;56196459100;","Research on Construction Planning for Bridge in Mountainous Area Based on GIS/BIM Virtual Construction Technology",2022,"Advances in Transdisciplinary Engineering","23",,,"903","910",,,"10.3233/ATDE220368","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140076095&doi=10.3233%2fATDE220368&partnerID=40&md5=ee749c6a56761725c9f9e3ebcde55c86","School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China; State Key Laboratory of Bridge Structure Health and Safety, Hubei, Wuhan, 430034, China","Wang, X., School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China, State Key Laboratory of Bridge Structure Health and Safety, Hubei, Wuhan, 430034, China; Li, Y., State Key Laboratory of Bridge Structure Health and Safety, Hubei, Wuhan, 430034, China; Wang, B., State Key Laboratory of Bridge Structure Health and Safety, Hubei, Wuhan, 430034, China; Chen, Z., School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China","It is difficult to plan the construction of super large bridge in mountainous area by traditional methods due to the complex geological conditions and narrow sites there. This paper proposed a new construction method of virtual construction platform for the construction planning of super large bridge in mountainous area applied in engineering based on BIM/GIS virtual construction technology. Based on the visual representation of BIM model and taking the GIS graphics engine as a carrier, this method combines the temporary building, the main structure and the geographical environment for virtual presentation. An integrated platform of model, data and rendering is formed for the realization virtual planning, virtual construction, digital twin, data supervision and other functions. Taking the pre-construction planning of a grand suspension bridge in the complex mountainous area of southwest China as an example, this virtual construction technology is used to realize the goal of the intelligent application in three aspects, namely, 3D planning of temporary construction site, intelligent control of access road and virtual preview of construction progress. © 2022 The authors and IOS Press.","BIM technology; Bridge; construction planning; digital twin; engineering application; GIS technology; virtual construction","Access control; Architectural design; Bridges; Area-based; BIM technologies; Bridges in mountainous area; Complex geological condition; Construction planning; Construction technologies; Engineering applications; GIS technology; Large Bridges; Virtual construction; Geographic information systems",,,,,,,,,,,,,,,,"Shen, X.L., Analysis of BIM technology deepening design and construction optimization of super tall buildings under epidemic situation (2020) Construction Quality, 38 (12), pp. 86-89; Zhang, H., Utilization of BIM technology in north anchorage construction of Qipanzhou Changjiang River highway bridge (2021) World Bridges, 49 (1), pp. 89-94; Fu, Z.G., Guo, H., Zhang, R., Application of BIM technology in design stage of main navigational channel bridge of Changtai Changjiang river bridge (2020) Bridge Construction, 50 (5), pp. 90-95; Yan, Z.G., Yue, Q., Shi, Z., Design of structural health monitoring system for Hutong Changjiang river bridge (2017) Bridge Construction, 47 (4), pp. 7-12; Liu, M., Research on building construction schedule optimization model based on BIM technology (2017) Modern Electronics Technique, 40 (3), pp. 103-105. , 109; Wu, J.F., Qi, J.B., Fang, L.J., Study of BIM-based bridge life-cycle management technique and application (2020) World Bridges, 48 (4), pp. 75-80; Li, K., Zeng, Q.L., Zeng, Y.C., The optimization of virtual construction based on BIM technology to the construction process of prefabricated buildings (2020) Housing and Real Estate, (4), p. 182; Yang, S., Feng, Z., Meng, X.M., Practice and research on virtual simulation information technology of GIS/BIM system rural house design and construction technology (2019) Technology Innovation and Application, (36), pp. 70-71; Sun, T., Application of BIM virtual construction technology in renovation and reconstruction of existing urban highway interchange hub (2019) Building Construction, 41 (10), pp. 1912-1915; Wang, S.J., Application and research of BIM 4D virtual construction method on pumped storage hydropower station projects (2019) Yellow River, 41 (3), pp. 145-149","Chen, Z.; School of Civil and Hydraulic Engineering, 1037 Luoyu Road, Hongshan District, Hubei Province, China; email: chenzj@hust.edu.cn","Zhang X.Ren H.Lu Y.Wang C.",,"IOS Press BV","3rd International Conference on Green Energy, Environment and Sustainable Development, GEESD 2022","29 June 2022",,183365,,9781643683126,,,"English","Adv. Transdiscipl. Eng.",Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85140076095
"Pregnolato M., Gunner S., Voyagaki E., De Risi R., Carhart N., Gavriel G., Tully P., Tryfonas T., Macdonald J., Taylor C.","57189259631;57216458982;15081790600;23026567500;36442104800;57303798600;56205847300;23006808300;56583351100;7404823270;","Towards Civil Engineering 4.0: Concept, workflow and application of Digital Twins for existing infrastructure",2022,"Automation in Construction","141",,"104421","","",,,"10.1016/j.autcon.2022.104421","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133234378&doi=10.1016%2fj.autcon.2022.104421&partnerID=40&md5=87b8e764516159abef9b905924c747b6","Dept. of Civil Engineering, University of Bristol, UK, Bristol, BS8 1TR, United Kingdom","Pregnolato, M., Dept. of Civil Engineering, University of Bristol, UK, Bristol, BS8 1TR, United Kingdom; Gunner, S., Dept. of Civil Engineering, University of Bristol, UK, Bristol, BS8 1TR, United Kingdom; Voyagaki, E., Dept. of Civil Engineering, University of Bristol, UK, Bristol, BS8 1TR, United Kingdom; De Risi, R., Dept. of Civil Engineering, University of Bristol, UK, Bristol, BS8 1TR, United Kingdom; Carhart, N., Dept. of Civil Engineering, University of Bristol, UK, Bristol, BS8 1TR, United Kingdom; Gavriel, G., Dept. of Civil Engineering, University of Bristol, UK, Bristol, BS8 1TR, United Kingdom; Tully, P., Dept. of Civil Engineering, University of Bristol, UK, Bristol, BS8 1TR, United Kingdom; Tryfonas, T., Dept. of Civil Engineering, University of Bristol, UK, Bristol, BS8 1TR, United Kingdom; Macdonald, J., Dept. of Civil Engineering, University of Bristol, UK, Bristol, BS8 1TR, United Kingdom; Taylor, C., Dept. of Civil Engineering, University of Bristol, UK, Bristol, BS8 1TR, United Kingdom","Digital Twins (DTs) are forecasted to be used in two-thirds of large industrial companies in the next decade. In the Architecture, Engineering and Construction (AEC) sector, their actual application is still largely at the prototype stage. Industry and academia are currently reconciling many competing definitions and unclear processes for developing DTs. There is a compelling need to establish DTs as practice in AEC by developing common procedures and standards tailored to the sector's procedures and use cases. This paper proposes a step-by-step workflow process for developing a DT for an existing asset in the built environment, providing a proof-of-concept case study based on the Clifton Suspension Bridge in Bristol (UK). To achieve its aim, this paper (i) reviews the state-of-the-art of DTs in Civil Engineering, (ii) proposes a working DT-based workflow framework for the built environment applicable to existing assets, (iii) applies the framework and develops of the physical-virtual architecture to a case study of bridge management, and finally (iv) discusses insights from the application. The main novelty lies in the development of a versatile methodological framework that can be applied to the broad context of civil infrastructure. This paper's importance resides in the knowledge challenge, value proposition and operation dictated by developing a DT workflow for the built environment, which ultimately represents a relevant use case for the digital transformation of national infrastructure. © 2022 The Authors","Bridge; Civil Engineering; Digital Twin; Infrastructure; Monitoring","Architecture engineering; Built environment; Case-studies; Construction sectors; Engineering sectors; Industrial companies; Infrastructure; Proof of concept; Work-flows; Workflow process; Bridges",,,,,"EP/R00742X/2; EP/P016782/1, EP/R013411/1; Engineering and Physical Sciences Research Council, EPSRC","This work was supported by the Engineering and Physical Sciences Research Council (ESPRC) LWEC (Living With Environmental Change) Fellowship (EP/R00742X/2); UK Collaboratorium for Research in Infrastructure & Cities (UKCRIC): Urban Observatories (EP/P016782/1); UKCRIC City Observatory Research platfOrm for iNnovation and Analytics (CORONA) (EP/R013411/1). The authors gratefully acknowledge: the Clifton Suspension Bridge Trust and the Bridgemaster Trish Johnson, COWI, and AMP Electrical. The authors would like to mention the passing away of the co-author Prof. John Macdonald, in March 2022. This work was completed before his passing away except for the minor corrections and editorial modifications. He has been an excellent scientist and the perfect leader for thisProject. His vision, his scientific approach and his passion for interdisciplinary research have been our beacon of light in the preparation of this work and will continue to inspire us. We would like to dedicate this paper to him.","This work was supported by the Engineering and Physical Sciences Research Council (ESPRC) LWEC (Living With Environmental Change) Fellowship ( EP/R00742X/2 ); UK Collaboratorium for Research in Infrastructure & Cities (UKCRIC): Urban Observatories ( EP/P016782/1 ); UKCRIC City Observatory Research platfOrm for iNnovation and Analytics (CORONA) ( EP/R013411/1 ). 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"John Samuel I., Salem O., He S.","57730095200;57197270015;57729792000;","Defect-oriented supportive bridge inspection system featuring building information modeling and augmented reality",2022,"Innovative Infrastructure Solutions","7","4","247","","",,,"10.1007/s41062-022-00847-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131431436&doi=10.1007%2fs41062-022-00847-3&partnerID=40&md5=5b7a8027b77b30c9006b21833a622c4b","Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA  22030, United States; Department of Civil Engineering, College of Civil Engineering and Architecture, Wenzhou University, Zhejiang, 325035, China","John Samuel, I., Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA  22030, United States; Salem, O., Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA  22030, United States; He, S., Department of Civil Engineering, College of Civil Engineering and Architecture, Wenzhou University, Zhejiang, 325035, China","Bridges are indispensable links of transportation infrastructure systems, and inspections play a critical role in maintaining bridge components in the state of good repair. Through a survey of bridge inspectors, the authors revealed that visual inspection techniques are the prominent inspection method but result in inaccuracy and ambiguity due to high variances among inspection results; modern inspections using drones and robots could improve efficiency but pose new challenges and do not reduce subjectivity. As a result, a novel, building information modeling- and augmented reality-based supportive inspection system (BASIS) that objectively captures bridge defects is proposed and validated. On-site inspectors can access the bridge model containing historical defect information (defect type, length/width/depth, and location) and overlay relevant content on the actual infrastructure through BASIS for inspection data collection with more accuracy and less ambiguity. A proof-of-concept prototype of the BASIS for bridges was developed as an android application and verified by bridge inspectors for effectiveness on a small pedestrian bridge. It was found that BASIS was able to collect accurate inspection data irrespective of the level of experience of the user, thusly minimizing the data subjectivity caused by differences among inspectors’ judgment and/or human errors. This research explores the utilization of emerging tools to collect bridge condition information in a more comprehensive and objective manner. Collected information can be further integrated it into a digital model that reflects the bridge’s most accurate and up-to-date condition, heading toward a digital twin of the physical infrastructure. The proposed system may also be adapted for other types of infrastructure (e.g., dams, levees, and railroads) that also require routine inspections. © 2022, Springer Nature Switzerland AG.","Augmented reality; Bridge inspections; Building information modeling; Defect information; Supportive systems",,,,,,,,,,,,,,,,,"Phares, B.M., Washer, G.A., Rolander, D.D., Graybeal, B.A., Moore, M., Routine highway bridge inspection condition documentation accuracy and reliability (2004) J Bridg Eng, 9 (4), pp. 403-413; Agnisarman, S., Lopes, S., Chalil Madathil, K., Piratla, K., Gramopadhye, A., A survey of automation-enabled human-in-the-loop systems for infrastructure visual inspection (2019) Autom Constr, 97, pp. 52-76; Sutter, B., Lelevé, A., Pham, M.T., Gouin, O., Jupille, N., Kuhn, M., Lulé, P., Rémy, P., A semi-autonomous mobile robot for bridge inspection (2018) Autom Constr, 91, pp. 111-119; Dorafshan, S., Maguire, M., Bridge inspection: human performance, unmanned aerial systems and automation (2018) J Civ Struct Heal Monit, 8 (3), pp. 443-476; McGuire, B., Atadero, R., Clevenger, C., Ozbek, M., Bridge information modeling for inspection and evaluation (2016) J Bridg Eng, 21 (4), p. 04015076; Estes, A.C., Frangopol, D.M., Updating bridge reliability based on bridge management systems visual inspection results (2003) J Bridg Eng, 8 (6), pp. 374-382; Bridge inspector's reference manual. 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Department of Civil Engineering, China; email: 20210465@wzu.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,23644176,,,,"English","Innov. Infrastruct. Solut.",Article,"Final","",Scopus,2-s2.0-85131431436
"Yu S., Li D., Ou J.","57752060000;57219460395;57211125450;","Digital twin-based structure health hybrid monitoring and fatigue evaluation of orthotropic steel deck in cable-stayed bridge",2022,"Structural Control and Health Monitoring","29","8","e2976","","",,,"10.1002/stc.2976","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128285952&doi=10.1002%2fstc.2976&partnerID=40&md5=aa6732c09732e03ebecef25e44fe3194","School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China; School of Civil Engineering and State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, China","Yu, S., School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China; Li, D., School of Civil Engineering and State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, China; Ou, J., School of Civil and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China, School of Civil Engineering and State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian, China","Digital twin bridges are virtual replicas of real physical entity bridges in computers. A digital twin bridge in the form of a finite element model can help in making sense of the structural responses monitored by the structure health monitoring system. This study proposes the structure health hybrid monitoring method, which provides a mean for synthesizing monitoring data and finite element model to reconstruct the un-monitoring structure responses, in developing a digital twin of a cable-stayed bridge. The considered structure is the orthotropic steel deck, in which the welding residual stress is an important cause of fatigue cracking. The submodel technology is employed to study the distribution characteristics of welding residual stress and the coupling effect with vehicle-induced stress and temperature-induced stress near the weld in the U-ribs to top deck joint in orthotropic steel deck. Aiming at the defect that cannot consider welding residual stress for the S-N curves based on fatigue evaluation and life prediction method, a nonlinear fatigue damage model based on the continuum damage mechanics, which has been verified by orthotropic steel deck fatigue test, is employed to evaluate the fatigue performance of U-ribs to top deck joint in orthotropic steel deck for an in-service cable-stayed bridge. © 2022 John Wiley & Sons Ltd.","digital twin bridge; fatigue performance; orthotropic steel deck; structural health hybrid monitoring; welding residual stress","Cable stayed bridges; Cables; Continuum damage mechanics; Fatigue testing; Finite element method; Structural health monitoring; Welding; Deck joints; Digital twin bridge; Fatigue evaluation; Fatigue performance; Finite element modelling (FEM); Induced stress; Orthotropic steel decks; Structural health; Structural health hybrid monitoring; Welding residual stress; Residual stresses",,,,,"National Natural Science Foundation of China, NSFC: 51778104, 51921006, U1709207","The National Natural Science Foundation of China (NSFC) with grant Nos.U1709207, 51921006, and 51778104.","The authors are grateful for the financial support from the National Natural Science Foundation of China with Grant Nos. 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"Cech M., Vosáhlo M.","44261326900;57971432400;","Digital Twins and HIL Simulators in Control Education - Industrial Perspective",2022,"IFAC-PapersOnLine","55","17",,"67","72",,,"10.1016/j.ifacol.2022.09.226","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142256241&doi=10.1016%2fj.ifacol.2022.09.226&partnerID=40&md5=83d315b5154f8a0a0d7a3e94befa9b29","University of West Bohemia, NTIS Research Center, Univerzitni 8, Pilsen, 30100, Czech Republic; ZF Engineering, Univerzitni 1159, Pilsen, 30100, Czech Republic","Cech, M., University of West Bohemia, NTIS Research Center, Univerzitni 8, Pilsen, 30100, Czech Republic; Vosáhlo, M., ZF Engineering, Univerzitni 1159, Pilsen, 30100, Czech Republic","Novel concepts like digital twins and X-in-the-Loop (XIL) simulations are being adopted in many new application areas by all entities in supply chains, including enterprises of all sizes as well as research institutes. However, they are not sufficiently addressed during control education in standard bachelor and master courses. The main cause is the high price of required equipment and SW toolchains. In addition, clear vision and common understanding of the role of digital twins in individual XIL stages should be created. In this paper, cost effective tools are presented and their utilization is demonstrated with a simple gantry crane model with special focus on load swinging attenuation. The authors believe that the presented tools and ideas would bring wider competences to the students and thus bridge the gap between industrial needs and academic practice and shorten the way towards 4th generation universities. © 2022 The Authors.","4th generation university; control education; Digital twin; feedback control; HIL simulator; overhead crane; STEM; vibration damping; XIL standard","Cost effectiveness; E-learning; Education computing; Feedback control; 4th generation university; Controls education; HIL simulator; In-control; New applications; Novel concept; Overhead crane; STEM; Vibration-damping; X-in-the-loop standard; Supply chains",,,,,"101007311","master’s theses. The goal is to make it an inherent part offi the courses ffior all stuffents. ACKNOWLEDGEMENTS This work was supporteff by the H2020 ECSEL JU grant agreement No. 101007311 IMOCO4.E project ’Intelligent Motion Control under Industry4.E.’ The support is grate-ffiully acknowleffgeff.",,,,,,,,,,"Alptekin, M., Temmen, K., Gamification in an augmented reality based virtual preparation laboratory training (2019) Advances in Intelligent Systems and Computing, 916, pp. 567-578; Bazylev, D., Shchukin, A., Margun, A., Zimenko, K., Kremlev, A., Titov, A., Applications of innovative active learning strategy in control systems curriculum (2016) Smart Innovation, Systems and Technologies, 59, pp. 485-494; De La Torre, L., Heradio, R., Jara, C., Sanchez, J., Dormido, S., Torres, F., Candelas, F., Providing collaborative support to virtual and remote laboratories (2013) IEEE Transactions on Learning Technologies, 6 (4), pp. 312-323; Desai, N., Ananya, S., Bajaj, L., Periwal, A., Desai, S., Process parameter monitoring and control using digital twin (2019) Lecture Notes in Networks and Systems, 80, pp. 74-80; Docekal, T., Golembiovsky, M., Low cost laboratory plant for control system education (2018) IFAC-PapersOnLine, 51 (6), pp. 289-294; Gomes, L., Bogosyan, S., Current trends in remote laboratories (2009) Industrial Electronics, IEEE Transactions on, 56 (12), pp. 4744-4756; Goubej, M., Königsmarková, J., Kampinga, R., Nieuwenkamp, J., Paquay, S., Employing finite element analysis and robust control concepts in mechatronic system design-flexible manipulator case study (2021) Applied Sciences, 11 (8); Goubej, M., Vyhlídal, T., Schlegel, M., Frequency weighted H2 optimization of multi-mode input shaper (2020) Automatica, 121; Helma, V., Goubej, M., Active anti-sway crane control using partial state feedback from inertial sensor (2021) 2021 23rd International Conference on Process Control (PC), pp. 137-142; Heradio, R., de la Torre, L., Dormido, S., Virtual and remote labs in control education: A survey (2016) Annual Reviews in Control, 42, pp. 1-10; Horácek, P., Laboratory experiments for control theory courses: A survey (2018) Annual Reviews in Control, 24, pp. 151-162; Leshner, A., Student-centered, modernized graduate STEM education (2018) Science, 360 (1), pp. 969-970; Ljung, L., (1999) System Identification: Theory for the User, , Prentice Hall PTR, Upper Saddle River, New Jersey, USA; Ramli, N., Rawi, M., Rebuan, F., Integrated smart home model: An IoT learning-inspired platform (2022) International Journal of Web-Based Learning and Teaching Technologies, 17 (3), pp. 1-14; Rani, R., Bavithran, N., Prasannakumar, S., Design and development of home automation system (2022) Lecture Notes in Mechanical Engineering, pp. 387-395; Reitinger, J., Cech, M., Schlegel, M., Balda, P., New tools for teaching vibration damping concepts (2014) Contlab.eu., 19, pp. 10580-10585; Riera, B., Vigario, B., Home I/O and factory I/O: a virtual house and a virtual plant for control education (2017) IFAC-PapersOnLine, 50 (1), pp. 9144-9149. , 20th IFAC World Congress; Rossiter, J., Pasik-Duncan, B., Dormido, S., Vlacic, L., Jones, B., Murray, R., A survey of good practice in control education (2018) European Journal of Engineering Education, 43 (6), pp. 801-823; Santo, L., Tandalla, R., Andaluz, H., Collaborative control of mobile manipulator robots through the hardware-in-the-loop technique (2022) Lecture Notes in Networks and Systems, 236, pp. 643-656; Severa, O., Cech, M., REX - rapid development tool for automation and robotics (2012) Proceedings of 2012 8th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2012, pp. 184-189; Severa, O., Cech, M., Balda, P., New tools for 3D HMI development in Java (2011) 2011 12th International Carpathian Control Conference (ICCC), pp. 342-346; Singh, J., Garg, K., Kumar, N., Sharma, B., Home automation: A novel approach (2022) Lecture Notes in Electrical Engineering, 776, pp. 479-486; Sitton-Candanedo, I., Alonso, R., Rodriguez-Gonzalez, S., Garcia Coria, J., De La Prieta, F., Edge computing architectures in industry 4.0: A general survey and comparison (2019) Advances in Intelligent Systems and Computing, 950, pp. 121-131; Sobota, J., Goubej, M., Königsmarková, J., Cech, M., Raspberry Pi-based HIL simulators for control education (2019) IFAC-PapersOnLine, 52 (9), pp. 68-73. , 12th IFAC Symposium on Advances in Control Education ACE 2019; Cech, M., Königsmarková, J., Goubej, M., Oomen, T., Visioli, A., Essential challenges in motion control education (2019) IFAC-PapersOnLine, 52 (9), pp. 200-205. , 12th IFAC Symposium on Advances in Control Education ACE 2019; Cech, M., Beltman, A.J., Ozols, K., I-MECH - smart system integration for mechatronic applications (2019) IEEE ETFA, 2019, pp. 843-850; Cech, M., Beltman, A.J., Ozols, K., Pushing mechatronic applications to the limits via smart motion control (2021) Applied Sciences, 11 (18); Cech, M., Königsmarková, J., Reitinger, J., Balda, P., Novel tools for model-based control system design based on FMI/FMU standard with application in energetics (2017) 2017 21st International Conference on Process Control (PC), pp. 416-421; Vosáhlo, M., (2020) HIL simulator for analysis and demonstration of smart control algorithms for overhead cranes, , Master's thesis, University of West Bohemia; Yasuda, K., Imai, N., Hirai, K., The class of controllability gramians assignable by state feedback (1988) Transactions of the Society of Instrument and Control Engineers, 24 (8), pp. 802-808; Zimenko, K., Bazylev, D., Margun, A., Kremlev, A., (2014) Application of innovative mechatronic systems in automation and robotics learning, pp. 437-441","Cech, M.; University of West Bohemia, Univerzitni 8, Czech Republic; email: mcech@ntis.zcu.cz","Guzman J.L.","et al.;International Federation of Automatic Control (IFAC) - Control Education, TC 9.4;TC 1.1. Modelling, Identification and Signal Processing;TC 3.3. Telematics: Control via Communication Networks;TC 4.3. Robotics;TC 6.1. Chemical Process Control","Elsevier B.V.","13th IFAC Symposium on Advances in Control Education, ACE 2022","24 July 2022 through 27 July 2022",,183673,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85142256241
"Mahmoodian M., Shahrivar F., Setunge S., Mazaheri S.","57195293461;57773032100;6602559307;57854541200;","Development of Digital Twin for Intelligent Maintenance of Civil Infrastructure",2022,"Sustainability (Switzerland)","14","14","8664","","",,,"10.3390/su14148664","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136404746&doi=10.3390%2fsu14148664&partnerID=40&md5=1f5a76e04fbed879a146f715e842911c","School of Engineering, RMIT University, Melbourne, 3000, Australia; STEM College, RMIT University, Melbourne, 3000, Australia; Beta International Associates Pty Ltd., Melbourne, 3000, Australia","Mahmoodian, M., School of Engineering, RMIT University, Melbourne, 3000, Australia; Shahrivar, F., School of Engineering, RMIT University, Melbourne, 3000, Australia; Setunge, S., STEM College, RMIT University, Melbourne, 3000, Australia; Mazaheri, S., Beta International Associates Pty Ltd., Melbourne, 3000, Australia","Over the life cycle of a civil infrastructure (a bridge as an example), 0.4–2% of the construction cost is spent annually on its maintenance. Utilising new technologies including the internet of things (IoT) and digital twin (DT) can significantly reduce the infrastructure maintenance costs. An infrastructure DT involves its digital replica and must include data on geometric, geospatial reference, performance, attributes (material, environment etc.) and management. Then, the acquired data need to be analysed and visualised to inform maintenance decision making. To develop this DT, the first step is the study of the infrastructure life cycle to design DT architecture. Using data semantics, this paper presents a novel DT architecture design for an intelligent infrastructure maintenance system. Semantic modelling is used as a powerful tool to structure and organize data. This approach provides an industry context through capturing knowledge about infrastructures in the structure of semantic model graph. Using new technologies, DT approach derives and presents meaningful data on infrastructure real-time performance and maintenance requirements, and in a more expressible and interpretable manner. The data semantic model will guide when and what data to collect for feeding into the infrastructure DT. The proposed DT concept was applied on one of the conveyors of Dalrymple Bay Coal Terminal in Queensland Australia to monitor the structural performance in real-time, which enables predictive maintenance to avoid breakdowns and disruptions in operation and consequential financial impacts. © 2022 by the authors.","architecture design; data semantic modelling; digital twin; intelligent infrastructure maintenance; internet of things","civil engineering; data mining; numerical model; performance assessment; real time; Australia; Queensland",,,,,,,,,,,,,,,,"Medhi, M., Dandautiya, A., Raheja, J.L., Real-Time Video Surveillance Based Structural Health Monitoring of Civil Structures Using Artificial Neural Network (2019) J. Nondestruct. 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J. Crit. Infrastruct, 18, p. 1; Shim, C.-S., Dang, N.-S., Lon, S., Jeon, C.-H., Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model (2019) Struct. Infrastruct. Eng, 15, pp. 1319-1332","Mahmoodian, M.; School of Engineering, Australia; email: mojtaba.mahmoodian@rmit.edu.au",,,"MDPI",,,,,20711050,,,,"English","Sustainability",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85136404746
"Choi J.-S., Kim C., Jang H., Kim E.-J.","57201666011;57221217100;57192402749;55477775700;","Dynamic thermal bridge evaluation of window-wall joints using a model-based thermography method",2022,"Case Studies in Thermal Engineering","35",,"102117","","",,,"10.1016/j.csite.2022.102117","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131204575&doi=10.1016%2fj.csite.2022.102117&partnerID=40&md5=d91640131caa3f17f06ff7fb74c11e61","Department of Architectural Engineering, Inha University, Incheon, 22212, South Korea; Intelligence Research Institute, EG Architectural Solution, Seoul, 07806, South Korea","Choi, J.-S., Department of Architectural Engineering, Inha University, Incheon, 22212, South Korea; Kim, C., Department of Architectural Engineering, Inha University, Incheon, 22212, South Korea; Jang, H., Intelligence Research Institute, EG Architectural Solution, Seoul, 07806, South Korea; Kim, E.-J., Department of Architectural Engineering, Inha University, Incheon, 22212, South Korea","Infrared thermography is an effective method for diagnosing thermal bridges in the field. However, it is less applied in a quantitative manner particularly under dynamic conditions. This study proposed digital twin-based research, wherein a structure is described as a numerical model to identify insulation defects and thermal bridges at the window-wall joint. The window-wall joint construction level was divided into cases with or without insulation for the window-wall joints using a chamber describing dynamic external temperature changes. Thermal images for the indoor wall surface temperature around the window-wall joint were obtained and described using a numerical model capable of high-speed calculation. Regarding the case of absent insulation of the joint, the obtained thermal image results exhibited a large difference in temperature pattern compared to the reference model, which can be reduced to a smaller difference through multiple numerical models obtained by changing the thermal conductivity of the joint. The reduced model proposed for practical optimization simulation exhibited superior performance in terms of accuracy and calculation time compared to a commercial simulation model. © 2022 The Authors.","Chamber tests; IR thermography; Reduction technique; Thermal bridge; Window-wall joint","Thermal conductivity; Thermal insulation; Thermography (imaging); Bridge evaluation; Chamber tests; Dynamic condition; IR-thermography; Model-based OPC; Reduction techniques; Thermal bridge; Thermal images; Window wall; Window-wall joint; Numerical models",,,,,"Ministry of Science, ICT and Future Planning, MSIP: 2021R1A4A1031705; National Research Foundation of Korea, NRF","This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIP) (No. 2021R1A4A1031705 ).",,,,,,,,,,"Johnston, D., Miles-Shenton, D., Farmer, D., Quantifying the domestic building fabric 'performance gap' (2015) Build. Serv. Eng. Technol., 36, pp. 614-627; De Wilde, P., The gap between predicted and measured energy performance of buildings: A framework for investigation (2014) Autom. ConStruct., 41, pp. 40-49; Theodosiou, T.G., Papadopoulos, A.M., The impact of thermal bridges on the energy demand of buildings with double brick wall constructions (2008) Energy Build., 40, pp. 2083-2089; Larbi, A.B., Statistical modelling of heat transfer for thermal bridges of buildings (2005) Energy Build., 37, pp. 945-951; Déqué, F., Ollivier, F., Roux, J.J., Effect of 2D modelling of thermal bridges on the energy performance of buildings: Numerical application on the Matisse apartment (2001) Energy Build., 33, pp. 583-587; Torres-Rivas, A., Palumbo, M., Haddad, A., Cabeza, L.F., Jiménez, L., Boer, D., Multi-objective optimisation of bio-based thermal insulation materials in building envelopes considering condensation risk (2018) Appl. Energy, 224, pp. 602-614; Duan, Q., Hinkle, L., Wang, J., Zhang, E., Memari, A., Condensation effects on energy performance of building window systems (2021) Energy Rep., 7, pp. 7345-7357; Hallik, J., Gustavson, H., Kalamees, T., Air leakage of joints filled with polyurethane foam (2019) Buildings, 9, p. 172; Mao, S., Kan, A., Wang, N., Numerical analysis and experimental investigation on thermal bridge effect of vacuum insulation panel (2020) Appl. Therm. Eng., 169; Hudobivnik, B., Pajek, L., Kunič, R., Košir, M., FEM thermal performance analysis of multi-layer external walls during typical summer conditions considering high intensity passive cooling (2016) Appl. Energy, 178, pp. 363-375; Nagy, B., Stocker, G., Numerical analysis of thermal and moisture bridges in insulation filled masonry walls and corner joints (2019) Period. Polytech. Civ. Eng., 63, pp. 446-455; Kordatos, E.Z., Aggelis, D.G., Matikas, T.E., Monitoring mechanical damage in structural materials using complimentary NDE techniques based on thermography and acoustic emission (2012) Compos. B Eng., 43, pp. 2676-2686; Montanini, R., Quantitative determination of subsurface defects in a reference specimen made of Plexiglas by means of lock-in and pulse phase infrared thermography (2010) Infrared Phys. Technol., 53, pp. 363-371; Bodnar, J.L., Candoré, J.C., Nicolas, J.L., Szatanik, G., Detalle, V., Vallet, J.M., Stimulated infrared thermography applied to help restoring mural paintings (2012) NDT e Int., 49, pp. 40-46; Kylili, A., Fokaides, P.A., Christou, P., Kalogirou, S.A., Infrared thermography (IRT) applications for building diagnostics: A review (2014) Appl. Energy, 134, pp. 531-549; Kim, C., Choi, J.-S., Jang, H., Kim, E.-J., Automatic detection of linear thermal bridges from infrared thermal images using neural network (2021) Appl. Sci., 11, p. 931; Nardi, I., Lucchi, E., De Rubeis, T., Ambrosini, D., Quantification of heat energy losses through the building envelope: A state-of-the-art analysis with critical and comprehensive review on infrared thermography (2018) Build. Environ., 146, pp. 190-205; Asdrubali, F., Baldinelli, G., Bianchi, F., A quantitative methodology to evaluate thermal bridges in buildings (2012) Appl. Energy, 97, pp. 365-373; Nardi, I., Ambrosini, D., Paoletti, D., Sfarra, S., Combining infrared thermography and numerical analysis for evaluating thermal bridges in buildings: A case study (2015) Int. J. Eng. Res. Afr., 5, pp. 67-76; Baldinelli, G., Bianchi, F., Rotili, A., Costarelli, D., Seracini, M., Vinti, G., Evangelisti, L., A model for the improvement of thermal bridges quantitative assessment by infrared thermography (2018) Appl. Energy, 211, pp. 854-864; Lydon, G.P., Caranovic, S., Hischier, I., Schlueter, A., Coupled simulation of thermally active building systems to support a digital twin (2019) Energy Build., 202; Lu, Q., Xie, X., Parlikad, A.K., Schooling, J.M., Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance (2020) Autom. ConStruct., 118; Choi, J.S., Kim, C.M., Jang, H.I., Kim, E.J., Detailed and fast calculation of wall surface temperatures near thermal bridge area (2021) Case Stud. Therm. Eng., 25; Vollme, M., Möllmann, K.P., (2017) Infrared Thermal Imaging: Fundamentals, Research and Applications, , John Wiley & Sons; (2018) Standard Practice for Measuring and Compensating for Emissivity Using Infrared Imaging Radiometers; (2010) Standard Test Methods for Measuring and Compensating for Reflected Temperature Using Infrared Imaging Radiometers; Moore, B., Principal component analysis in linear systems: Controllability, observability, and model reduction (1981) IEEE Trans Automat Constr, 26, pp. 17-32; Gao, Y., Roux, J.J., Teodosiu, C., Zhao, L.H., Reduced linear state model of hollow blocks walls, validation using hot box measurements (2004) Energy Build., 36, pp. 1107-1115; Kim, E.J., Plessis, G., Hubert, J.L., Roux, J.J., Urban energy simulation: Simplification and reduction of building envelope models (2014) Energy Build., 84, pp. 193-202; Kim, E.J., Roux, J.J., Bernier, M.A., Cauret, O., Three-dimensional numerical modeling of vertical ground heat exchangers: Domain decomposition and state model reduction (2011) HVAC R Res., 17, pp. 912-927; Kim, E.J., He, X., Roux, J.J., Johannes, K., Kuznik, F., Fast and accurate district heating and cooling energy demand and load calculations using reduced-order modelling (2019) Appl. Energy, 238, pp. 963-971","Kim, E.-J.; Department of Architectural Engineering, South Korea; email: ejkim@inha.ac.kr",,,"Elsevier Ltd",,,,,2214157X,,,,"English","Case Stud. Therm. Eng.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85131204575
"Grunert G.","57191472720;","Data and evaluation model for the description of the static–dynamic interface between trains and railway bridges",2022,"Engineering Structures","262",,"114335","","",,,"10.1016/j.engstruct.2022.114335","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129934482&doi=10.1016%2fj.engstruct.2022.114335&partnerID=40&md5=9384eef2c9fb9a07fe7beedff33291f0","Deutsche Bahn DB Netz AG, Brückenbau und Lärmschutzanlagen Technik, Germany","Grunert, G., Deutsche Bahn DB Netz AG, Brückenbau und Lärmschutzanlagen Technik, Germany","This paper presents a five-level data model based on the single beam with simple supports for the static and dynamic assessment for existing railway bridges with regard to loading from trains. Through the joint representation of action and structural responses based on wavelengths, clear diagrams of the verification variables are achieved. The combination of the relevant parameters enables numerous evaluations, which can be used for the validation of an entire existing bridge stock, but also for the assessment of a single bridge. © 2022 Elsevier Ltd","Bridge dynamics; Bridge monitoring; Digital twin; EN line category; Eurocode; Existing and new railway bridges; Interface bridges and rolling stock; Static with dynamic factors; Technical specification for interoperability","Interoperability; Railroads; Bridge dynamics; Bridge monitoring; Dynamic factors; EN line category; Eurocodes; Existing and new railway bridge; Interface bridge; Interface bridge and rolling stock; Railway bridges; Static with dynamic factor; Technical Specification for Interoperability; Railroad bridges; bridge; data set; dynamic response; loading test; structural response; wavelength",,,,,"Bundesministerium für Verkehr und Digitale Infrastruktur, BMVI","In total, approx. calculable models of various quality levels are available for existing single-span girders, continuous girders, portal and closed frames and complex structures. For these models, static, quasi-static and dynamic analyses from traffic loads or other actions are possible. By means of intersection with other data sources of real actual traffic (timetable, axle load measuring points, monitoring systems), for example, also better analyses of the fatigue of the structures can be carried out, which can be of benefit for the predictive maintenance strategy of the DB Netz AG. This could be a significant step to the full implementation of the Digital Twin concept. These and other development opportunities are supported by research projects such as “Digital Maintenance of Railway Bridges” (DiMaRB) [38] , “Condition Assessment of Railway Bridges and trains with AI Methods for the Evaluation of Sensor Data and Structural Dynamic Models” (ZEKISS) [39] and “Data-based extrapolation model for the residual life analysis of railway infrastructure assets” [40] (DEEB-INFRA, all within the framework of the funding guideline Modernity Fund (mFund) of the German Federal Ministry of Transport and Digital Infrastructure (BMVI)). For more research in bridge dynamics and regarding the further development of the HSLM load model, reference is made to the ongoing research projects Shift2Rail In2Track3 [41] and “Dynamic load model for high-speed railway bridges” (German Centre for Rail Traffic Research at the Federal Railway Authority) [42] .",,,,,,,,,,"TSI OPE- COMMISSION IMPLEMENTING REGULATION (EU) 2019/773 on the technical specification for interoperability relating to the operation and traffic management subsystem of the rail system within the European Union and repealing decision 2012/757/EU (2019) Off J Eur Union; European Committee for Standardization (CEN), EN 15528 Railway applications - Line categories for managing the interface between load limits of vehicles and infrastructure (2021); (2019), DB Netz AG. Abschlussbericht Projekt Brückenbefahrbarkeit: Nachweis der Brückenkompatibilität für den ICE4. Tech. rep. (unpublished); DB Netz AG, Richtlinien des netzzugangsrelevanten Regelwerks der NBN 2022: Technische Netzzugangsbedingungen (TNB) (2022), https://fahrweg.dbnetze.com/fahrweg-de/kunden/nutzungsbedingungen/regelwerke/regelwerke_netzzugangsrelevant/netzzugangsrelevantes_regelwerk-5762000?contentId=1370052, URL; (1999), D214 (RP6) Rail bridges for speeds ¿200 km/h. Calculations for bridges with simply supported beams during passage of a train; EN 15528 Railway applications - Line categories for managing the interface between load limits of vehicles and infrastructure (2015); EN 1991-2 Eurocode 1: Actions on structures - Part 2: Traffic loads on bridges (2010); Weber, W., Das brückentechnische Beurteilungsverfahren von Schwerwagensendungen bei der Deutschen Bundesbahn (1984) Schienen der Welt März, pp. 33-41; Unterweger, H., Schörghofer, A., Taras, A., Tragsicherheit von Bestandsbrücken für neue Hochgeschwindigkeitszüge - Teil 1 Analyse der maßgebenden Zugparameter und deren Grenzwerte (2016) Eisenbahningenieur, 67 (4), pp. 40-44; TSI INF- COMMISSION REGULATION (EU) no 1299/2014 on the technical specifications for interoperability relating to the ‘infrastructure’ subsystem of the rail system in the European Union (2014) Off J Eur Union; Sogabe, M., Matsumoto, N., Kanamori, M., Sato, T., Wakui, H., Impact factors of concrete girders coping with train speed-up (2005) Q Rep RTRI, 46 (1), pp. 46-52; Fink, J., Mähr, T., Vereinfachte Methoden zur Berechnung der Dynamischen Antwort von Eisenbahnbrücken bei Zugüberfahrt (2007) Stahlbau, 76 (10), pp. 710-721; Spengler, M., Dynamik von Eisenbahnbrücken unter Hochgeschwindigkeitsverkehr - Entwicklung eines Antwortspektrums zur Erfassung der dynamischen Tragwerksreaktion (2010), http://tuprints.ulb.tu-darmstadt.de/2036/, (Ph.D. thesis); Unterweger, H., Schörghofer, A., Taras, A., Tragsicherheit von Bestandsbrücken für neue Hochgeschwindigkeitszüge - Teil 2 Selektion kritischer Einzeltragwerke und objektiver Zugvergleich (2016) Eisenbahningenieur, (5), pp. 62-66; DB Netz AG, H., Richtlinie 804 Eisenbahnbrücken (und sonstige Ingenieurbauwerke) planen, bauen und instand halten (2013); UIC CODE 776-1, Loads to be considered in railway bridge design (3) (1979); D214 (RP9) rail bridges for speeds higher than 200 km/h: Final report (1999); Frýba, L., Dynamics of railway bridges (1996), p. 330. , Thomas Telford London; DB Netz AG, L., Dynamische Effekte bei Resonanzgefahr: Leitfaden für die dynamische Untersuchung (2000); DB Netz AG, L., Richtlinie 458 Außergewöhnliche Sendungen: Schwerwagen-Transporte beurteilen (1999); ÖBB-Infrastruktur, A.G., (2022), Dynamische Berechnung von Eisenbahnbrücken, Regelwerk 08 01.04; DB Netz AG, L., Richtlinie 805 Tragsicherheit bestehender Eisenbahnbrücken (2010); Neu, K.-T., Funktionsausbildung zum Fachverantwortlichen für Belastbarkeit Regelverkehr - Schwerwagenverkehr (2013); Glatz, B., Fink, J., Einfluss der Zugmodelle auf die dynamische Antwort von 75 Stahl-, Verbund- und Stahlbetonbrücken (2019) Stahlbau, 88 (5), pp. 470-477; Museros, P., Andersson, A., Martí, V., Karoumi, R., Dynamic behaviour of bridges under critical articulated trains: Signature and bogie factor applied to the review of some regulations included in EN 1991-2 (2020) Proc Inst Mech Eng Part F; Firus, A., Berthold, H., Grunert, G., Schneider, J., Messkonzept Hochtastfahrten. 2017, (unpublished); Doménech, A., Museros, P., Martínez-Rodrigo, M.D., Influence of the vehicle model on the prediction of the maximum bending response of simply-supported bridges under high-speed railway traffic (2014) Eng Struct, 72 (8), pp. 123-139; Firus, A., Berthold, H., Schneider, J., Grunert, G., Untersuchungen zum dynamischen Verhalten einer Eisenbahnbrücke bei Anregung durch den neuen ICE4 (2018); Khalaf, A.W., (2021), Untersuchung dynamischer Kompatibilität von bestehenden Eisenbahnbrücken mit einer neuen Zugklasse. Master Thesis (unpublished); Rebelo, C., Simões da Silva, L., Rigueiro, C., Pircher, M., Dynamic behaviour of twin single-span ballasted railway viaducts - field measurements and modal identification (2008) Eng Struct, 30 (9), pp. 2460-2469; Firus, A., A contribution to moving force identification in bridge dynamics (2022), (Ph.D. thesis); Li, J., Su, M., Fan, L., Natural frequency of railway girder bridges under vehicle loads (2003) Journal of Bridge Engineering, 8 (4), pp. 199-203; Müller, M., Gutachterliche Stellungnahme: Erstellung eines Prognosemodells für den Einsatz des ICx (2016), DB Netz AG (unpublished); Bigelow, H., Hoffmeister, B., Feldmann, M., Zur Einspannwirkung von Eisenbahngleisen teil 2: Nichtlineare Federcharakteristiken (2020) Bautechnik, 97 (2), pp. 74-84; Reiterer, M., Experimentelle und numerische Untersuchung einer bestehenden Eisenbahnbrücke bei Zugüberfahrt (2020) Bautechnik, 97 (7), pp. 473-489; Andersson, A., Arvidsson, T., Dynamic analysis of load effects for railway bridges on malmbanan (2020) Rapp Trafikverket; Nguyen, K., Velarde, C., Goicolea, J.M., Analytical and simplified models for dynamic analysis of short skew bridges under moving loads (2019) Adv Struct Eng; Hartung, R., Naraniecki, H., Klemt-Albert, K., Marx, S., Konzept zur BIM-basierten Instandhaltung von Ingenieurbauwerken mit Monitoringsystemen (2020) Bautechnik, 97 (12), pp. 826-835; Thiele, C., Brötzmann, J., Huyeng, T., Rüppel, U., Lorenzen, S.R., Berthold, H., Schneider, J., A digital twin as a framework for a machine learning based predictive maintenance system (2021) ECPPM 2021 - ework ebus. archit. eng. constr., pp. 313-319. , Taylor & Francis Moscow; Datenbasiertes extrapolationsmodell für die Restlebensdaueranalyse von Eisenbahninfrastrukturanlagen - DEEB-INFRA (2021), https://www.bmvi.de/SharedDocs/DE/Artikel/DG/mfund-projekte/deep-infra.html, URL; (2021), Tunnel and bridge I2T2 report: High speed low cost bridges, background report D5.2.5. Tech. rep; Vorwagner, A., Kwapisz, M., Flesch, R., Kohl, A.M., Firus, A., Vospernig, M., Big data FEM approach for development of a new high-speed load- model for railway bridge (2021) IABSE congr. Ghent 2021 - struct. eng. futur. soc. needs",,,,"Elsevier Ltd",,,,,01410296,,ENSTD,,"English","Eng. Struct.",Article,"Final","",Scopus,2-s2.0-85129934482
"Mozo A., Karamchandani A., Gómez-Canaval S., Sanz M., Moreno J.I., Pastor A.","24479201000;57713253300;55894434500;57214241978;35611835100;57210395292;","B5GEMINI: AI-Driven Network Digital Twin",2022,"Sensors","22","11","4106","","",,,"10.3390/s22114106","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130790092&doi=10.3390%2fs22114106&partnerID=40&md5=58a36efd3a35bda2e8242702fb7f0c42","ETSI Sistemas Informáticos, Dpto. Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, 28031, Spain; ETSI Telecomunicación, Dpto. Ingeniería de Sistemas Telemáticos, Universidad Politécnica de Madrid, Madrid, 28040, Spain; Telefónica I+D, Madrid, 28050, Spain","Mozo, A., ETSI Sistemas Informáticos, Dpto. Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, 28031, Spain; Karamchandani, A., ETSI Sistemas Informáticos, Dpto. Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, 28031, Spain; Gómez-Canaval, S., ETSI Sistemas Informáticos, Dpto. Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, 28031, Spain; Sanz, M., ETSI Telecomunicación, Dpto. Ingeniería de Sistemas Telemáticos, Universidad Politécnica de Madrid, Madrid, 28040, Spain; Moreno, J.I., ETSI Telecomunicación, Dpto. Ingeniería de Sistemas Telemáticos, Universidad Politécnica de Madrid, Madrid, 28040, Spain; Pastor, A., ETSI Sistemas Informáticos, Dpto. Sistemas Informáticos, Universidad Politécnica de Madrid, Madrid, 28031, Spain, Telefónica I+D, Madrid, 28050, Spain","Network Digital Twin (NDT) is a new technology that builds on the concept of Digital Twins (DT) to create a virtual representation of the physical objects of a telecommunications network. NDT bridges physical and virtual spaces to enable coordination and synchronization of physical parts while eliminating the need to directly interact with them. There is broad consensus that Artificial Intelligence (AI) and Machine Learning (ML) are among the key enablers to this technology. In this work, we present B5GEMINI, which is an NDT for 5G and beyond networks that makes an extensive use of AI and ML. First, we present the infrastructural and architectural components that support B5GEMINI. Next, we explore four paradigmatic applications where AI/ML can leverage B5GEMINI for building new AI-powered applications. In addition, we identify the main components of the AI ecosystem of B5GEMINI, outlining emerging research trends and identifying the open challenges that must be solved along the way. Finally, we present two relevant use cases in the application of NDTs with an extensive use of ML. The first use case lays in the cybersecurity domain and proposes the use of B5GEMINI to facilitate the design of ML-based attack detectors and the second addresses the design of energy efficient ML components and introduces the modular development of NDTs adopting the Digital Map concept as a novelty. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.","artificial intelligence; digital twin; machine learning; network digital twin; telecommunications","5G mobile communication systems; E-learning; Machine learning; Architectural components; Artificial intelligence learning; Cyber security; Machine-learning; Network digital twin; Physical objects; Research trends; Telecommunications networks; Virtual representations; Virtual spaces; Energy efficiency; artificial intelligence; ecosystem; machine learning; technology; Artificial Intelligence; Ecosystem; Machine Learning; Technology",,,,,"Horizon 2020 Framework Programme, H2020: 101015857, 833685, 871808, INSPIRE-5Gplus; Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España, MINECO: TSI-063000-2021-79, TSI-063000-2021-81","Funding: This work was supported in part by the Spanish Ministerio de Asuntos Económicos y Transformación Digital, Programa UNICO under project B5GEMINI (TSI-063000-2021-79, TSI-063000-2021-81). This work was partially supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 833685 (SPIDER), Grant 101015857 (Teraflow), and Grant 871808 (INSPIRE-5Gplus).",,,,,,,,,,"Grieves, M., Vickers, J., Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems (2017) Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, pp. 85-113. , Kahlen, F.-J., Flumerfelt, S., Alves, A., Eds.; Springer International Publishing: Cham, Switzerland, ISBN 978-3-319-38756-7; Wu, Y., Zhang, K., Zhang, Y., Digital Twin Networks: A Survey (2021) IEEE Internet Things J, 8, pp. 13789-13804. , [CrossRef]; Nguyen, H.X., Trestian, R., To, D., Tatipamula, M., Digital Twin for 5G and Beyond (2021) IEEE Commun. Mag, 59, pp. 10-15. , [CrossRef]; Zhou, C., Yang, H., Duan, X., Lopez, D., Pastor, A., Wu, Q., Boucadair, M., Jacquenet, C., (2021) Digital Twin Network: Concepts and Reference Architecture, , Internet Engineering Task Force: Fremont, CA, USA; Grieves, M., (2016) Origins of the Digital Twin Concept, , Florida Institute of Technology: Melbourne, FL, USA; Tuegel, E.J., Ingraffea, A.R., Eason, T.G., Spottswood, S.M., Reengineering Aircraft Structural Life Prediction Using a Digital Twin (2011) Int. J. Aerosp. Eng, 2011, p. e154798. , [CrossRef]; Rathore, M.M., Shah, S.A., Shukla, D., Bentafat, E., Bakiras, S., The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities (2021) IEEE Access, 9, pp. 32030-32052. , [CrossRef]; Ai, Y., Peng, M., Zhang, K., Edge Computing Technologies for Internet of Things: A Primer (2018) Digit. Commun. 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"Rojas-Mercedes N., Erazo K., Di Sarno L.","56685658700;55805452100;6506371363;","Seismic fragility curves for a concrete bridge using structural health monitoring and digital twins",2022,"Earthquake and Structures","22","5",,"503","515",,,"10.12989/eas.2022.22.5.503","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131428696&doi=10.12989%2feas.2022.22.5.503&partnerID=40&md5=6d064f837047f5a01cfc74486cfe2cf1","School of Engineering, Instituto Tecnológico de Santo Domingo (INTEC), Santo Domingo, Dominican Republic; Department of Civil and Environmental Engineering, Rice University, Houston, TX, United States; Department of Civil Engineering and Industrial Design, School of Engineering, University of Liverpool, Liverpool, United Kingdom; Department of Engineering, University of Sannio, Benevento, Italy","Rojas-Mercedes, N., School of Engineering, Instituto Tecnológico de Santo Domingo (INTEC), Santo Domingo, Dominican Republic; Erazo, K., School of Engineering, Instituto Tecnológico de Santo Domingo (INTEC), Santo Domingo, Dominican Republic, Department of Civil and Environmental Engineering, Rice University, Houston, TX, United States; Di Sarno, L., Department of Civil Engineering and Industrial Design, School of Engineering, University of Liverpool, Liverpool, United Kingdom, Department of Engineering, University of Sannio, Benevento, Italy","This paper presents the development of seismic fragility curves for a precast reinforced concrete bridge instrumented with a structural health monitoring (SHM) system. The bridge is located near an active seismic fault in the Dominican Republic (DR) and provides the only access to several local communities in the aftermath of a potential damaging earthquake; moreover, the sample bridge was designed with outdated building codes and uses structural detailing not adequate for structures in seismic regions. The bridge was instrumented with an SHM system to extract information about its state of structural integrity and estimate its seismic performance. The data obtained from the SHM system is integrated with structural models to develop a set of fragility curves to be used as a quantitative measure of the expected damage; the fragility curves provide an estimate of the probability that the structure will exceed different damage limit states as a function of an earthquake intensity measure. To obtain the fragility curves a digital twin of the bridge is developed combining a computational finite element model and the information extracted from the SHM system. The digital twin is used as a response prediction tool that minimizes modeling uncertainty, significantly improving the predicting capability of the model and the accuracy of the fragility curves. The digital twin was used to perform a nonlinear incremental dynamic analysis (IDA) with selected ground motions that are consistent with the seismic fault and site characteristics. The fragility curves show that for the maximum expected acceleration (with a 2% probability of exceedance in 50 years) the structure has a 62% probability of undergoing extensive damage. This is the first study presenting fragility curves for civil infrastructure in the DR and the proposed methodology can be extended to other structures to support disaster mitigation and post-disaster decision-making strategies © 2022. Techno-Press, Ltd","Civil infrastructure; Digital twins; Earthquake engineering; Fragility curves; Structural health monitoring","Concrete bridges; Decision making; Disasters; Earthquake engineering; Earthquakes; Faulting; Precast concrete; Reinforced concrete; Seismic response; Uncertainty analysis; Active seismic; Civil infrastructures; Fragility curves; Local community; Precast reinforced concrete; Reinforced concrete bridge; Seismic faults; Seismic fragility curves; Seismic regions; Structural health monitoring systems; Structural health monitoring",,,,,"Ministerio de Educación Superior, Ciencia y Tecnología, República Dominicana, MESCYT","This work was partially supported by the Ministry of Higher Education, Sciences and Technology of the Dominican Republic (MESCYT). 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Vib, 11 (3), pp. 331-342. , https://doi.org/10.1007/s11803-012-0125-1; Yamaguchi, N., Yamazaki, F., Fragility curves for buildings in Japan based damaged surveys after the 1995 Kobe Earthquake (2000) 12th World Conference of Earthquake Engineering, , Auckland, January; Yazdabad, M., Behnamfar, F, Samani, A.K., Seismic behavioral fragility curves of concrete cylindrical water tanks for sloshing, cracking and wall bending (2018) Earthq. Struct, 14 (2), pp. 95-102. , https://doi.org/10.12989/EAS.2018.14.2.095; Yon, B., Seismic vulnerability assessment of RC buildings according to the 2007 and 2018 Turkish seismic codes (2020) Earthq. Struct, 18 (6), pp. 709-718. , https://doi.org/10.12989/EAS.2020.18.6.709; Zhang, J.H., Hu, S.D., State of the Art of Bridge Seismic Vulnerability Analysis Research (2005) Struct. Eng, 21 (5), pp. 76-80. , https://doi.org/10.3969/j.issn.1005-0159.2005.05.017; Zhao, Y., Hu, H., Bai, L., Tang, M., Chen, H., Su, D., Fragility analyses of bridge structures using the logarithmic piecewise function-based probabilistic seismic demand model (2021) Sustainability, 13 (14), p. 7814. , https://doi.org/10.3390/su13147814","Erazo, K.; School of Engineering, Dominican Republic; email: erazo@intec.edu.do",,,"Techno-Press",,,,,20927614,,,,"English","Earthqu. Struct.",Article,"Final","",Scopus,2-s2.0-85131428696
"Guzina L., Ferko E., Bucaioni A.","57764627400;57225194704;56236820700;","Investigating Digital Twin: A Systematic Mapping Study",2022,"Advances in Transdisciplinary Engineering","21",,,"449","460",,,"10.3233/ATDE220164","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132800357&doi=10.3233%2fATDE220164&partnerID=40&md5=9b0172cf5e1354ac37b97125918e291b","Mälardalen University, Västerås, Sweden","Guzina, L., Mälardalen University, Västerås, Sweden; Ferko, E., Mälardalen University, Västerås, Sweden; Bucaioni, A., Mälardalen University, Västerås, Sweden","The term digital twin refers to a comprehensive digital representation of a physical system that serves as its real-time digital counterpart. Digital twin goes beyond traditional computer-aided applications and represents a two-way communication bridge between the physical and the digital worlds. In 2020, Gartner identified digital twin as one of the ten technology trends capable of a profound impact on modern society. While digital twin originates from the manufacturing domain, its recent underpinning technology maturation makes it suitable to all those domains where there is a need for studying virtual interactions with the physical environment. Despite its peak of research and adoption, there are still some grey areas related to certain aspects of digital twin such as enabling technologies and reported benefits. In this paper, we report on the planning, execution and results of a systematic mapping study, which aimed at providing a structured and detailed snapshot of the current application of digital twin, enabling technologies, reported benefits and application domains. Starting from an initial set of 675 publications, we identified 26 primary studies, which we have analysed through a rigorous data extraction, analysis and synthesis process. Based on the collected data, we drew relations between digital twin and the production domain. © 2022 The authors and IOS Press.","Digital twin; software engineering; systematic mapping study","Mapping; Computer-aided; Digital counterparts; Digital representations; Digital world; Enabling technologies; Physical systems; Real- time; Systematic mapping studies; Traditional computers; Two way communications; Software engineering",,,,,,,,,,,,,,,,"Kagermann, H., Lukas, W.D., Wahlster, W., Industrie 4. 0: Mit dem Internet der Dinge auf dem Weg zur 4. industriellen Revolution (2011) VDI nachrichten, 13 (1), pp. 2-3; Baheti, R., Gill, H., Cyber-physical systems (2011) The impact of control technology, 12 (1), pp. 161-166; Ashton, K., That 'internet of things' thing (2009) RFID journal, 22 (7), pp. 97-114; Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Digital twin-driven product design framework (2019) International Journal of Production Research, 57 (12), pp. 3935-3953; Cearley, D., Burke, B., Velosa, A., Kerremans, M., Top 10 Strategic Technology Trends for 2019: Digital Twins, , https://www.gartner.com/en/documents/3904569/top-10-strategic-technology-trends-for-2019-digital-twin, Accessed: 2021-10-07; Schroeder, G.N., Steinmetz, C., Rodrigues, R.N., Henriques, R.V.B., Rettberg, A., Pereira, C.E., A Methodology for Digital Twin Modeling and Deployment for Industry 4. 0 (2021) Proceedings of the IEEE, 109 (4), pp. 556-567; Glaessgen, E., Stargel, D., (2012) The digital twin paradigm for future NASA and U. S. Air force vehicles; Grieves, M., Digital twin: manufacturing excellence through virtual factory replication (2014) White paper, 1, pp. 1-7; Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Digital twin-driven product design framework (2018) International Journal of Production Research, 57, pp. 1-19. , 02; Leskovsky, R., Kucera, E., Haffner, O., Rosinova, D., Proposal of Digital Twin Platform Based on 3D Rendering and IIoT Principles Using Virtual / Augmented Reality (2020) 2020 Cybernetics Informatics (K I), pp. 1-8; Petersen, K., Vakkalanka, S., Kuzniarz, L., Guidelines for conducting systematic mapping studies in software engineering: An update (2015) Information and Software Technology, 64, pp. 1-18. , https://www.sciencedirect.com/science/article/pii/S0950584915000646; Wohlin, C., Guidelines for Snowballing in Systematic Literature Studies and a Replication in Software Engineering (2014) Procs of EASE. ACM, pp. 381-3810; Cruzes, D., Dyba, T., (2011) Recommended Steps for Thematic Synthesis in Software Engineering, pp. 275-284; Basili, V.R., Caldiera, G., Rombach, H.D., The Goal Question Metric Approach (1994) Encyclopedia of Software Engineering, 2, pp. 528-532. , Wiley; Kitchenham, B., Brereton, P., A systematic review of systematic review process research in software engineering (2013) Information and software technology, 55 (12), pp. 2049-2075; Gao, Y., Lv, H., Hou, Y., Liu, J., Xu, W., Real-time Modeling and Simulation Method of Digital Twin Production Line (2019) 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), pp. 1639-1642; Assawaarayakul, C., Srisawat, W., Ayuthaya, S.D.N., Wattanasirichaigoon, S., Integrate Digital Twin to Exist Production System for Industry 4. 0 (2019) 2019 4th Technology Innovation Management and Engineering Science International Conference (TIMES-iCON), pp. 1-5; Simon, G., Hantos, G.B., Patel, M.S., Tweedie, A., Harvey, G., Machine Learning Enabled FBAR Digital Twin for Rapid Optimization (2020) 2020 IEEE International Ultrasonics Symposium (IUS), pp. 1-4; Makarov, V.V., Frolov, Y.B., Parshina, I.S., Ushakova, M.V., The Design Concept of Digital Twin (2019) 2019 Twelfth International Conference Management of large-scale system development (MLSD), pp. 1-4; Ivanov, S., Nikolskaya, K., Radchenko, G., Sokolinsky, L., Zymbler, M., Digital Twin of City: Concept Overview (2020) 2020 Global Smart Industry Conference (GloSIC), pp. 178-186; Mihokovíc, V., Zalovíc, L., Zalovíci, V., Establishing the utility charges spatial database using digital twin technology (2020) 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO), pp. 437-441; Steinmetz, C., Rettberg, A., Ribeiro, F.G.C., Schroeder, G., Pereira, C.E., Internet of Things Ontology for Digital Twin in Cyber Physical Systems (2018) 2018 VIII Brazilian Symposium on Computing Systems Engineering (SBESC), pp. 154-159; Rasheed, A., San, O., Kvamsdal, T., Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2020) IEEE Access, 8, pp. 21980-22012; Conde, J., Munoz-Arcentales, A., Alonso, A., Lopez-Pernas, S., Salvachua, J., Modeling Digital Twin Data and Architecture: A Building Guide with FIWARE as Enabling Technology (2021) IEEE Internet Computing., p. 1; Erol, T., Mendi, A.F., Dogan, D., Digital Transformation Revolution with Digital Twin Technology (2020) 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), pp. 1-7; Moyne, J., Qamsane, Y., Balta, E.C., Kovalenko, I., Faris, J., Barton, K., A Requirements Driven Digital Twin Framework: Specification and Opportunities (2020) IEEE Access, 8, pp. 107781-107801; Lin, W.D., Low, M.Y.H., Concept and Implementation of a Cyber-Pbysical Digital Twin for a SMT Line (2019) 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 1455-1459; Alam, K.M., El Saddik, A., C2PS: A Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems (2017) IEEE Access, 5, pp. 2050-2062; Chakrabortty, R.K., Rahman, H.F., Mo, H., Ryan, M.J., Digital Twin-based Cyber Physical System for Sustainable Project Scheduling (2019) 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 820-824; Ala-Laurinaho, R., Autiosalo, J., Nikander, A., Mattila, J., Tammi, K., Data Link for the Creation of Digital Twins (2020) IEEE Access, 8, pp. 228675-228684; Fuller, A., Fan, Z., Day, C., Barlow, C., Digital Twin: Enabling Technologies, Challenges and Open Research (2020) IEEE Access, 8, pp. 108952-108971; Qamsane, Y., Chen, C.Y., Balta, E.C., Kao, B.C., Mohan, S., Moyne, J., A Unified Digital Twin Framework for Real-time Monitoring and Evaluation of Smart Manufacturing Systems (2019) 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), pp. 1394-1401; Schroeder, G., Steinmetz, C., Pereira, C.E., Muller, I., Garcia, N., Espindola, D., Visualising the digital twin using web services and augmented reality (2016) 2016 IEEE 14th International Conference on Industrial Informatics (INDIN), pp. 522-527; Skobelev, P., Laryukhin, V., Simonova, E., Goryanin, O., Yalovenko, V., Yalovenko, O., Developing a smart cyber-physical system based on digital twins of plants (2020) 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), pp. 522-527; Mikkonen, T., Kemell, K.K., Kettunen, P., Abrahamsson, P., Exploring Virtual Reality as an Integrated Development Environment for Cyber-Physical Systems (2019) 2019 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp. 121-125; Azangoo, M., Taherkordi, A., Olaf Blech, J., Digital Twins for Manufacturing Using UML and Behavioral Specifications (2020) 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 1, pp. 1035-1038; Diaz, R.A.C., Ghita, M., Copot, D., Birs, I.R., Muresan, C., Ionescu, C., Context Aware Control Systems: An Engineering Applications Perspective (2020) IEEE Access, 8, pp. 215550-215569; Peuhkurinen, A., Mikkonen, T., Embedding web apps in mixed reality (2018) 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), pp. 169-174; Demkovich, N., Yablochnikov, E., Abaev, G., Multiscale modeling and simulation for industrial cyberphysical systems (2018) 2018 IEEE Industrial Cyber-Physical Systems (ICPS), pp. 291-296; Roxin, A., Abdou, W., Ginhac, D., Derigent, W., Dragomirescu, D., Montegut, L., Digital Building Twins-Contributions of the ANR McBIM Project (2019) 2019 15th International Conference on Signal-Image Technology Internet-Based Systems (SITIS), pp. 404-410; Landolfi, G., Barni, A., Menato, S., Cavadini, F.A., Rovere, D., Dal Maso, G., Design of a multi-sided platform supporting CPS deployment in the automation market (2018) 2018 IEEE Industrial Cyber-Physical Systems (ICPS), pp. 684-689; Wieringa, R., Maiden, N., Mead, N., Rolland, C., Requirements engineering paper classification and evaluation criteria: A proposal and a discussion (2006) Requir Eng., 11, pp. 102-107. , 03; Nahavandi, S., Industry 5. 0-A human-centric solution (2019) Sustainability, 11 (16), p. 4371; Wanasinghe, T.R., Wroblewski, L., Petersen, B.K., Gosine, R.G., James, L.A., De Silva, O., Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges (2020) IEEE Access, 8, pp. 104175-104197; Perno, M., Hvam, L., Haug, A., Enablers and Barriers to the Implementation of Digital Twins in the Process Industry: A Systematic Literature Review (2020) 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 959-964; Melesse, T.Y., Pasquale, V.D., Riemma, S., Digital Twin Models in Industrial Operations: A Systematic Literature Review (2020) Procedia Manufacturing, 42, pp. 267-272. , https://www.sciencedirect.com/science/article/pii/S2351978920306491, International Conference on Industry 4. 0 and Smart Manufacturing (ISM 2019); Jones, D., Snider, C., Nassehi, A., Yon, J., Hicks, B., Characterising the Digital Twin: A systematic literature review (2020) CIRP Journal of Manufacturing Science and Technology, 29, pp. 36-52. , https://www.sciencedirect.com/science/article/pii/S1755581720300110","Bucaioni, A.; Mälardalen UniversitySweden; email: alessio.bucaioni@mdh.se","Ng A.H.C.Syberfeldt A.Hogberg D.Holm M.",,"IOS Press BV","10th Swedish Production Symposium, SPS 2022","26 April 2022 through 29 April 2022",,179964,,9781614994398,,,"English","Adv. Transdiscipl. Eng.",Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85132800357
"Chen Y., Xue X., Wang Y., Zhu H.","57242867100;57985943300;57272506000;57642089200;","Intelligent Upgrading and Application of Bridge Video Surveillance System Based on Computer Vision",2022,"IABSE Congress Nanjing 2022 - Bridges and Structures: Connection, Integration and Harmonisation, Report",,,,"1147","1153",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142890362&partnerID=40&md5=d56e2f2bd8506b8a5f43f59261c97d0b","CCCC Second Harbor Engineering Company LTD, Wuhan, China; Key Laboratory of Large-span Bridge Construction Technology, Wuhan, China","Chen, Y., CCCC Second Harbor Engineering Company LTD, Wuhan, China; Xue, X., CCCC Second Harbor Engineering Company LTD, Wuhan, China; Wang, Y., CCCC Second Harbor Engineering Company LTD, Wuhan, China, Key Laboratory of Large-span Bridge Construction Technology, Wuhan, China; Zhu, H., CCCC Second Harbor Engineering Company LTD, Wuhan, China, Key Laboratory of Large-span Bridge Construction Technology, Wuhan, China","The rapid development of computer vision provides a foundation for the intelligent upgrading of bridge video surveillance systems. In this paper, two intelligent upgrading methods were developed and deployed. The first method uses edge computing equipment as the core, to quickly identify and locate vehicles across the large-span bridge by YOLOv5, which was trained by synthesized vehicle dataset, and then a large-span bridge vehicle digital twin system was built and deployed in Baijusi Yangtze River Bridge, which is suitable for scenarios with high real-time requirements. The another one is based on cloud computing, relying on ShuffleNetV2 to build a waterlogging recognition model and early warning system, which is suitable for scenarios with low real-time requirements. The results show that the constructed intelligent system upgrades the traditional passive access system to an early warning system with active recognition, which improves the intelligence of the system and meets the needs of engineering applications. © IABSE Congress Nanjing 2022 - Bridges and Structures: Connection, Integration and Harmonisation, Report. All rights reserved.","deep learning; early warning; intelligent upgrading; video surveillance","Computer vision; Deep learning; Intelligent systems; Large dataset; Monitoring; Security systems; Deep learning; Early warning; Early Warning System; Edge computing; Intelligent upgrading; Large span; Real time requirement; Span bridges; Video surveillance; Video surveillance systems; Vehicles",,,,,,,,,,,,,,,,"Mneymneh, B.E., Abbas, M., Khoury, H., Automated hardhat detection for construction safety applications (2017) Procedia engineering, 196, pp. 895-902; Kolar, Z., Chen, H., Luo, X., Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images (2018) Automation in Construction, 89, pp. 58-70; Fang, Q., Li, H., Luo, X., Ding, L., Luo, H., Li, C., Computer vision aided inspection on falling prevention measures for steeplejacks in an aerial environment (2018) Automation in Construction, 93, pp. 148-164; Xuehui, A., Li, Z., Zuguang, L., Chengzhi, W., Pengfei, L., Zhiwei, L., Dataset and benchmark for detecting moving objects in construction sites (2021) Automation in Construction, 122, p. 103482; Fang, Q., Li, H., Luo, X., Ding, L., Luo, H., Rose, T.M., An, W., Detecting non-hardhat-use by a deep learning method from far-field surveillance videos (2018) Automation in Construction, 85, pp. 1-9; Fang, W., Ma, L., Love, P. E., Luo, H., Ding, L., Zhou, A.O., Knowledge graph for identifying hazards on construction sites: Integrating computer vision with ontology (2020) Automation in Construction, 119, p. 103310; Bernasconi, A., Kharshiduzzaman, M., Anodio, L.F., Bordegoni, M., Re, G.M., Braghin, F., Comolli, L., Development of a monitoring system for crack growth in bonded single-lap joints based on the strain field and visualization by augmented reality (2014) The journal of adhesion, 90 (5-6), pp. 496-510; Ye, X.W., Ni, Y.Q., Wai, T.T., Wong, K.Y., Zhang, X.M., Xu, F., A vision-based system for dynamic displacement measurement of long-span bridges: algorithm and verification (2013) Smart Structures and Systems, 12 (3-4), pp. 363-379; Yang, Y., Dorn, C., Mancini, T., Talken, Z., Kenyon, G., Farrar, C., Mascareñas, D., Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification (2017) Mechanical Systems and Signal Processing, 85, pp. 567-590; Dan, D., Ge, L., Yan, X., Identification of moving loads based on the information fusion of weigh-in-motion system and multiple camera machine vision (2019) Measurement, 144, pp. 155-166; Chen, Z., Li, H., Bao, Y., Li, N., Jin, Y., Identification of spatio-temporal distribution of vehicle loads on long-span bridges using computer vision technology (2016) Structural Control and Health Monitoring, 23 (3), pp. 517-534; Zhang, B., Zhou, L., Zhang, J., A methodology for obtaining spatiotemporal information of the vehicles on bridges based on computer vision (2019) Computer-Aided Civil and Infrastructure Engineering, 34 (6), pp. 471-487; Xia, Y., Jian, X., Yan, B., Su, D., Infrastructure safety oriented traffic load monitoring using multi-sensor and single camera for short and medium span bridges (2019) Remote Sensing, 11 (22), p. 2651","Chen, Y.; CCCC Second Harbor Engineering Company LTDChina; email: chenyuan20@ccccltd.cn",,,"International Association for Bridge and Structural Engineering (IABSE)","IABSE Congress Nanjing 2022 - Bridges and Structures: Connection, Integration and Harmonisation","21 September 2022 through 23 September 2022",,184084,,9783857481840,,,"English","IABSE Congr. Nanjing - Bridg. Struct.: Connect., Integr. Harmon., Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85142890362
"Ying G., Zhang C., Hu J., Chen W., Xing Y., Xu M., Xia Y.","57985747200;57986175200;57985637200;57721025500;57985856500;57986283300;55553987200;","Design of a bridge digital twin system for Intelligent operation and maintenance based on machine vision",2022,"IABSE Congress Nanjing 2022 - Bridges and Structures: Connection, Integration and Harmonisation, Report",,,,"1709","1714",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142883537&partnerID=40&md5=9729e29b567f99695892124d85ecae88","School of Civil Engineering & Architecture, NingboTech University, Ningbo, 315100, China; College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, 310000, China; Ningbo Langda Engineering Technology Co., LTD, Ningbo, 315100, China; Department of Bridge Engineering, Tongji University, Shanghai, 200092, China","Ying, G., School of Civil Engineering & Architecture, NingboTech University, Ningbo, 315100, China, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, 310000, China; Zhang, C., School of Civil Engineering & Architecture, NingboTech University, Ningbo, 315100, China, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, 310000, China; Hu, J., Ningbo Langda Engineering Technology Co., LTD, Ningbo, 315100, China; Chen, W., Ningbo Langda Engineering Technology Co., LTD, Ningbo, 315100, China; Xing, Y., Ningbo Langda Engineering Technology Co., LTD, Ningbo, 315100, China; Xu, M., Department of Bridge Engineering, Tongji University, Shanghai, 200092, China; Xia, Y., Department of Bridge Engineering, Tongji University, Shanghai, 200092, China","Under the background of the transformation and upgrading of bridge maintenance management, aiming at the problems of weak processing capacity, low management efficiency and intelligent degree of the bridge operation and maintenance process, this paper proposes a digital twin system solution of bridge intelligent operation and maintenance based on machine vision. Then framework of the bridge digital twin system for Intelligent operation and maintenance is proposed, followed by the realization methods based on machine vision. Finally, a case application of the concrete simply supported girder bridge is given. © IABSE Congress Nanjing 2022 - Bridges and Structures: Connection, Integration and Harmonisation, Report. All rights reserved.","bridge; digital twin; machine vision; operation and maintenance; system","Bridges; Maintenance; Bridge maintenance management; Intelligent maintenance; Intelligent operations; Machine-vision; Management efficiency; On-machines; Operation process; Operations and maintenance; Processing capacities; System; Computer vision",,,,,,,,,,,,,,,,"Zhang, X G, Tian, Y, Chen, A R., Review of Bridge Design Method for Multiple Hazards [J] (2018) Chian.J Highw. Transp, 31, pp. 7-19; He, S H, Zhao, X M, Ma, J, Review of Highway Bridge Inspection and Condition Assessment [J] (2017) Chian.J Highw. Transp, 30, pp. 63-80; Ye, C, Liam, B, Bartek, C, (2019) A Digital Twin of Bridges for Structural Health Monitoring for Proceedings of the 12th International Workshop on Structural HealthMonitoring; Mi, S H, Feng, Y X, Zheng, H, Prediction Maintenance Integrated Decision-making Approach Supported by Digital Twin-driven Cooperative A wareness and Interconnection Framework (2021) Journal of Manufacturing Systems, 58, pp. 329-345. , [J]; Lin, K, Xu, Y L, Lu, X Z, Digital Twin-based Collapse Fragility Assessment of a Long-span Cable-stayed Bridge Under Strong Earthquakes [J] (2021) Automation in Construction, 123, p. 103547; Ye, X W, Dong, C Z, Liu, T., Image-based structural dynamic displacement measurement using different multi-object tracking algorithms[J] (2016) Smart Structures and Systems, 17, pp. 35-956; Jian, X D, Xia, Y, Lozano-Galant, Jose A., Traffic Sensing Methodology Combining Influence Line Theory and Computer Vision Techniques for Girder Bridges[J] (2019) Journal of Sensors, 2019, p. 3409525; Xia, Y, Jian, X D, Yan, B, Infrastructure Safety Oriented Traffic Load Monitoring Using Multi-sensor and Single Camera for Short and Medium Span Bridges [J] (2019) Remote Sensing, 11, p. 2651; Kuddus, M A, Li, J, Hao, H, Target-free Vision-based Technique for Vibration Measurements of Structures Subjected to Out-of-plane Movements [J] (2019) Engineering Structures, 190, pp. 210-222; Wang, L B, Wang, Q L, Zhu, Z, Current Status and Prospects of Research on Bridge Health Monitoring Technology[J] (2021) Chian.J Highw. Transp, 34, pp. 25-45","Ying, G.; School of Civil Engineering & Architecture, China; email: 2017121077@chd.edu.cn",,,"International Association for Bridge and Structural Engineering (IABSE)","IABSE Congress Nanjing 2022 - Bridges and Structures: Connection, Integration and Harmonisation","21 September 2022 through 23 September 2022",,184084,,9783857481840,,,"English","IABSE Congr. Nanjing - Bridg. Struct.: Connect., Integr. Harmon., Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85142883537
"Fischer R.-P., Schnicke F., Beggel B., Oliveira Antonino P.","57955534900;57209138657;57954870000;36619591600;","Historical Data Storage Architecture Blueprints for the Asset Administration Shell",2022,"IEEE International Conference on Emerging Technologies and Factory Automation, ETFA","2022-September",,,"","",,,"10.1109/ETFA52439.2022.9921613","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141356743&doi=10.1109%2fETFA52439.2022.9921613&partnerID=40&md5=07b192939c23682e1bc3e96ef1974699","Fraunhofer Iese, Virtual Engineering, Kaiserslautern, Germany; Kaiserslautern University of Applied Sciences, Zweibrücken, Germany","Fischer, R.-P., Fraunhofer Iese, Virtual Engineering, Kaiserslautern, Germany; Schnicke, F., Fraunhofer Iese, Virtual Engineering, Kaiserslautern, Germany; Beggel, B., Kaiserslautern University of Applied Sciences, Zweibrücken, Germany; Oliveira Antonino, P., Fraunhofer Iese, Virtual Engineering, Kaiserslautern, Germany","The concept of the Digital Twin (DT) and its implementation as Asset Administration Shell (AAS) is one of the key technologies for implementing Industry 4.0. By utilizing the AAS, use cases can be implemented with high interoperability. These use cases include optimizations and improvements of physical systems, like predictive maintenance or other artificial intelligence applications. For these use cases, historical data is key. However, there exists no guidance on how to handle historical data with the AAS. Furthermore, no integrated architecture exists that enables seamless storage and retrieval of historical data using the unified AAS meta-model and interface. Thus, we bridge this gap between use case and unified implementation by presenting multiple blueprints for data storage and retrieval motivated by use cases from Industry 4.0, healthcare, and civil construction. These data storage blueprints range from a mandatory change in the AAS infrastructure to augmentations of the existing AAS concepts. Furthermore, we showcase how the data model of the AAS can be utilized for a unified retrieval of historical data. In consequence, practitioners can quickly realize use cases that require historical data by tailoring and implementing the presented blueprints. Additionally, researchers can extend the presented guidance to further use cases, possibly from other domains. © 2022 IEEE.","Architecture; Asset Administration Shell; Data Storage; Digital Twins; Historical Data; Industry 4.0","Architecture; Blueprints; Bridges; Digital storage; Asset administration shell; Data storage; Historical data; Integrated architecture; Key technologies; Optimisations; Physical systems; Predictive maintenance; Storage and retrievals; Storage architectures; Industry 4.0",,,,,,,,,,,,,,,,"Forschung und Innovation für die Menschen. Die Hightech-Strategie 2025 (2018) Bundesministerium für Bildung und Forschung (BMBF) Referat Grundsatzfragen der Innovationspolitik, , Die Bundesregierung, Ed., Sep; Rüsmann, M., Lorenz, M., Gerbert, P., Industry 4. 0: The future of productivity and growth in manufacturing industries (2015) Boston consulting group, 9 (1), pp. 54-89; Tao, F., Zhang, H., Liu, A., Nee, A.Y.C., Digital twin in industry: State-of-the-art (2019) IEEE Transactions on Industrial Informatics, 15 (4), pp. 2405-2415. , https://doi.org/10.1109/TII.2018.2873186, Apr; Bader, S.R., Maleshkova, M., The semantic asset administration shell (2019) Semantic Systems. The Power of AI and Knowledge Graphs, pp. 159-174. , https://doi.org/10.1007/978-3-030-33220-412, M. Acosta, P. Cudré-Mauroux, M. Maleshkova, T. Pellegrini, H. Sack, and Y. Sure-Vetter, Eds., Springer International Publishing; Wagner, C., Grothoff, J., Epple, U., The role of the industry 4. 0 asset administration shell and the digital twin during the life cycle of a plant (2017) 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1-8; Schnicke, F., Espen, D., Oliveira Antonino, P., Kuhn, T., Architecture blueprint enabling distributed digital twins (2021) 7th Conference on the Engineering of Computer Based Systems, ser. ECBS 2021, , https://doi.org/10.1145/3459960.3459978, Novi Sad, Serbia: Association for Computing Machinery; Schroeder, G.N., Steinmetz, C., Pereira, C.E., Espindola, D.B., Digital twin data modeling with automationml and a communication methodology for data exchange (2016) IFAC-PapersOnLine, 49 (30), pp. 12-17. , https://doi.org/10.1016/j.ifacol.2016.11.115, 4th IFAC Symposium on Telematics Applications TA 2016; Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F., Digital twin-driven product design, manufacturing and service with big data (2018) The International Journal of Advanced Manufacturing Technology, 94 (9), pp. 3563-3576. , https://doi.org/10.1007/s00170-017-0233-1, Feb; Liu, C., Le Roux, L., Körner, C., Tabaste, O., Lacan, F., Bigot, S., Digital twin-enabled collaborative data management for metal additive manufacturing systems (2020) Journal of Manufacturing Systems, , https://doi.org/10.1016/j.jmsy.2020.05.010; Qi, Q., Tao, F., Hu, T., Enabling technologies and tools for digital twin (2021) Journal of Manufacturing Systems, 58, pp. 3-21. , https://doi.org/10.1016/j.jmsy.2019.10.001, Mar; Grieves, M., Vickers, J., Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems (2017) Transdisciplinary perspectives on complex systems, pp. 85-113. , Springer; Uhlemann, T.H.-J., Schock, C., Lehmann, C., Freiberger, S., Steinhilper, R., The digital twin: Demonstrating the potential of real time data acquisition in production systems (2017) Procedia Manufacturing, 9, pp. 113-120. , https://doi.org/10.1016/j.promfg.2017.04.043, 7th Conference on Learning Factories, CLF 2017; Yun, S., Park, J.-H., Kim, W.-T., Data-centric middleware based digital twin platform for dependable cyber-physical systems (2017) 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), pp. 922-926. , https://doi.org/10.1109/ICUFN.2017.7993933, Jul; Martin, P., Egerstedt, M.B., Hybrid systems tools for compiling controllers for cyber-physical systems (2012) Discrete Event Dynamic Systems, 22 (1), pp. 101-119; Zhu, Y., Westbrook, E., Inoue, J., Mathematical equations as executable models of mechanical systems (2010) Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems, pp. 1-11; Huang, S., Wang, G., Yan, Y., Fang, X., Blockchainbased data management for digital twin of product (2020) Journal of Manufacturing Systems, 54, pp. 361-371. , https://doi.org/10.1016/j.jmsy.2020.01.009; (2003) European Parliament, Council of the European Union, Commission Directive 2003/94/EC, , http://data.europa.eu/eli/dir/2003/94/oj; Bader, S., (2020) Details of the Asset Administration Shell. Part 1-The exchange of information between partners in the value chain of Industrie 4. 0 (Version 3. 0RC01), , P. I. 4. 0, Ed. Federal Ministry for Economic Affairs and Energy (BMWi), Nov; Anderl, R., (2016) Aspects of the research roadmap in application scenarios, , https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/aspects-of-theresearch-roadmap.html, P. I. 4. 0, Ed. Apr. visited on 03/19/2022; Anderl, R., Fortschreibung der anwendungsszenarien der plattform industrie 4. 0 (2016) P. I. 4. 0, , https://www.plattform-i40.de/IP/Redaktion/DE/Downloads/Publikation/fortschreibunganwendungsszenarien.html, Ed., Oct. visited on 03/19/2022",,,,"Institute of Electrical and Electronics Engineers Inc.","27th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2022","6 September 2022 through 9 September 2022",,183811,19460740,9781665499965,85ROA,,"English","IEEE Int. Conf. Emerging Technol. Factory Autom., ETFA",Conference Paper,"Final","",Scopus,2-s2.0-85141356743
"Flaga S., Pacholczak K.","46061084000;57956607800;","Demonstrator of a Digital Twin for Education and Training Purposes as a Web Application",2022,"Advances in Science and Technology Research Journal","16","5",,"110","119",,,"10.12913/22998624/152927","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141355962&doi=10.12913%2f22998624%2f152927&partnerID=40&md5=28f4825ebe33c228b0bd4024d7014f42","Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, ul. Mickiewicza 30, Krakow, 30-059, Poland; Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, ul. Mickiewicza 30, Krakow, 30-059, Poland","Flaga, S., Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, ul. Mickiewicza 30, Krakow, 30-059, Poland; Pacholczak, K., Faculty of Metals Engineering and Industrial Computer Science, AGH University of Science and Technology, ul. Mickiewicza 30, Krakow, 30-059, Poland","The concept of Industry 4.0 has now become a fact. One of its key technological solutions – the digital twin – serves as a bridge between the real and virtual worlds. Designs for both products and tools to make these products are generated in the virtual world. Thanks to the simulation capabilities of these digital replicas, it is possible to eliminate design flaws well before the creation of physical prototypes. Thus, the question naturally arises as to what degree these mathematical models of objects, processes or services replicate their physical counterparts. A correctly generated digital twin is not only a model or visualisation of its counterpart; it also re-flects its dynamic behaviour. The issue of digital twins is a very broad one, and currently on the market, there are appearing an increasing number of tools available for the development of these twins. More and more often, 3D modelling software can be integrated with a control system model, facilitating the testing of newly designed objects in the virtual world. This paper presents the concept of building simplified digital twins in a web application environment. In addition to educational usage, the presented idea should find application in the design of small production lines, significantly affecting the cost of producing a digital twin. © 2022, Politechnika Lubelska. All rights reserved.","digital twin; Industry 4.0; web application",,,,,,,,,,,,,,,,,"Qin, H., Wang, H., Zhang, Y., Lin, L., Constructing Digital Twin for Smart Manufacturing (2021) IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 638-642; Wagg, D.J., Worden, K., Barthorpe, R.J., Gardner, P., Digital Twins: State-of-the-Art and Future Directions for Modeling and Simulation in Engineering Dynamics Applications (2020) ASME. ASME J. Risk Uncertainty Part B, 6 (3), p. 030901; Kutin, A.A., Bushuev, V.V., Molodtsov, V.V., Digital twins of mechatronic machine tools for modern manufacturing (2019) IOP Conference Series: Materials Science and Engineering, 568 (1), p. 012070; Cheng, K., Wang, Q., Yang, D., Dai, Q., Wang, M., Digital-Twins-Driven Semi-Physical Simulation for Testing and Evaluation of Industrial Software in a Smart Manufacturing System (2022) Machines, 10 (5), p. 388; Figueiras, P., Lourenço, L., Costa, R., Graça, D., Gar-cia, G., Jardim-Gonçalves, R., Big Data Provision for Digital Twins in Industry 4.0 Logistics Processes (2021) 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT), pp. 516-521; Maulshree, S., Srivastava, R., Fuenmayor, E., Kuts, V., Qiao, J., Murray, N., Devine, D., Applications of Digital Twin across Industries: A Review (2022) Applied Sciences, 12 (11), p. 5727; Lalik, K., Flaga, S., A real-time distance measurement system for a digital twin using mixed reality goggles (2021) Sensors, 21 (23), p. 7870; Oborski, P., Wysocki, P., Intelligent Visual Quality Control System Based on Convolutional Neural Networks for Holonic Shop Floor Control of Industry 4.0 Manufacturing Systems (2022) Advances in Science and Technology Research Journal, 16 (2), pp. 89-98; Gaska, P., Harmatys, W., Gruza, M., Gaska, A., Sladek, J., Simple Optical Coordinate Measuring System, Based on Fiducial Markers Detection, and its Accuracy Assessment (2020) Advances in Science and Technology Research Journal, 14 (4), pp. 213-219; Kuts, V., Sarkans, M., Otto, T., Tähemaa, T., Bonda-renko, Y., Digital Twin: Concept of Hybrid Programming for Industrial Robots – Use Case (2019) Proceedings of the ASME 2019 International Mechanical Engineering Congress and Exposition. Volume 2B: Advanced Manufacturing, , Salt Lake City, Utah, USA; Pizon, J., Gola, A., Swic, A., The Role and Meaning of the Digital Twin Technology in the Process of Implementing Intelligent Collaborative Robots (2022) Advances in Manufacturing III. MANUFACTURING 2022, , Gapinski, B., Ciszak, O., Ivanov, (eds) Springer; Staczek, P., Pizon, J., Danilczuk, W., Gola, A., A Digital Twin Approach for the Improvement of an Autonomous Mobile Robots (AMR’s) Operating Environment – A Case Study (2021) Sensors, 21 (23), p. 7830; Georgakopoulos, D., Bamunuarachchi, D., Digital Twins-based Application Development for Digital Manufacturing (2021) 2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC), pp. 87-95; Yasin, A., Pang, T.Y., Cheng, C.T., Miletic, M., A Roadmap to Integrate Digital Twins for Small and Medium-Sized Enterprises (2021) Appl. Sci, 11, p. 9479; Herbus, K., Ociepka, P., Gwiazda, A., Virtual acti-vating of a robotised production cell with use of the mechatronics concept designer module of the PLM Siemens NX system (2018) International Conference on Intelligent Systems in Production Engineering and Maintenancel, pp. 417-425. , Springer, Cham; Febronio, R., Martinez, G., Puga, J.A.V., Coro-nado, P.D.U., Ortigoza, A.A.G., Orta-Castañon, P., Ahuett-Garza, H., A flexible and open environment for discrete event simulations and smart manufac-turing (2021) International Journal on Interactive Design and Manufacturing, 15 (4), p. 509; Pizon, J., Gola, A., The Meaning and Directions of Development of Personalized Production in the Era of Industry 4.0 and Industry 5.0 (2022), et al. In-novations in Industrial Engineering II. Icieng, Lec-ture Notes in Mechanical Engineering,. Springer, Cham; Thapa, D., Park, C.M., Han, K.H., Park, S.C., Wang, G., Architecture for modeling, simulation, and exe- cution of PLC based manufacturing system (2008) Winter Simulation Conference, pp. 1794-1801; Ruzarovsky, R., Holubek, R., Delgado Sobrino, D.R., Janíček, M., The Simulation of Conveyor Control System Using the Virtual Commissioning and Virtual Reality (2018) Advances in Science and Technology Research Journal, 12 (4), pp. 164-171; Riera, B., Vigário, B., HOME I/O and FACTORY I/O: a virtual house and a virtual plant for control education (2017) IFAC-PapersOnLine, 50 (1), pp. 9144-9149; Lalik, K., Kozek, M., Dominik, I., Łukasiewicz, P., Adaptive MRAC Controller in the Effector Tra-jectory Generator for Industry 4.0 Machines (2020) Ad-vanced, Contemporary Control, Advances in Intelligent Systems and Computing, , Springer; Martin, R.C., (2017) Clean architecture: a craftsman’s guide to software structure and design, , Prentice-Hall; Evans, E., Evans, E.J., (2004) Domain-driven design: tack-ling complexity in the heart of software, , Addison-Wesley Professional; Gamma, E., Helm, R., Johnson, R., Vlissides, J., Design patterns: Abstraction and reuse of object-ori-ented design (1993) European Conference on Object-Ori-ented Programming, pp. 273-282. , Springer, Berlin, Heidelberg; Huda, M., Arya, Y.D.S., Khan, M.H., Quantifying reusability of object-oriented design: a testability perspective (2015) Journal of Software Engineering and Applications, 8 (4), p. 175; Bruel, J.M., Mazzara, M., Meyer, B., (2018) Software Engineering Aspects of Continuous Development and New Paradigms of Software Production and De-ployment: First International Workshop, , Chateau de Villebrumier, France; Nickoloff, J., Kuenzli, S., (2019) Docker in action, , Second Edition, Simon and Schuster","Flaga, S.; Faculty of Mechanical Engineering and Robotics, ul. Mickiewicza 30, Poland; email: stanislaw.flaga@agh.edu.pl",,,"Politechnika Lubelska",,,,,22998624,,,,"English","Adv. Sci. Technol. Res. J.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85141355962
"Zhao H.-W., Ding Y.-L., Li A.-Q., Chen B., Wang K.-P.","57191694306;55768944900;7403291516;57467103600;55648912700;","Digital modeling approach of distributional mapping from structural temperature field to temperature-induced strain field for bridges",2022,"Journal of Civil Structural Health Monitoring",,,,"","",,,"10.1007/s13349-022-00635-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140052808&doi=10.1007%2fs13349-022-00635-8&partnerID=40&md5=cfa45ece3e937a6c15a58997c2ce4560","Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing, 210096, China; School of Civil Engineering, Southeast University, Nanjing, 210096, China; Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China; China Railway Bridge and Tunnel Technologies Co., Ltd., Nanjing, 210061, China; CCCC Highway Bridges National Engineering Research Centre Co., Ltd., Beijing, 100088, China","Zhao, H.-W., Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing, 210096, China, School of Civil Engineering, Southeast University, Nanjing, 210096, China; Ding, Y.-L., Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing, 210096, China, School of Civil Engineering, Southeast University, Nanjing, 210096, China; Li, A.-Q., School of Civil Engineering, Southeast University, Nanjing, 210096, China, Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China; Chen, B., China Railway Bridge and Tunnel Technologies Co., Ltd., Nanjing, 210061, China; Wang, K.-P., CCCC Highway Bridges National Engineering Research Centre Co., Ltd., Beijing, 100088, China","Zero-point of strain data representing the structural state without stress is hard to determine, but change in strain can be accurately measured. It is a good choice to quantify the complex strain behavior under non-uniform temperature field by deep learning the variation of distributional features from different sensing points. Taking a long-span steel cable-stayed bridge as the case study, features of long-term time series data of temperature and temperature-induced strain are analyzed. A digital approach of distributional mapping from the structural temperature field to the temperature-induced strain field is presented. Based on the coordinates clustering of sensing points and the correlation knowledge between structural temperature and temperature-induced strain, clusters of sensing points of temperature and strain can be determined. Distributional feature parameters (difference sequence and adjacency matrix of difference) about the per-minute mean of each cluster’s structural temperature and temperature-induced strain data are calculated. The model of mapping relation from structural temperature field to temperature-induced strain field is established based on the learning of the big data of distributional feature parameters by the bidirectional long short-term memory regression network. The results demonstrated that redistribution of temperature-induced strain field can be perceived according to the residual between regression results of network models and real-time monitoring results, which means extreme changes of temperature field or potential deterioration in structure of bridge. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.","Cluster; Deep learning; Digital twin; Structural health monitoring; Temperature-induced strain","Cable stayed bridges; Deep learning; Deterioration; E-learning; Mapping; Structural health monitoring; Cluster; Deep learning; Digital modeling; Distributional features; Feature parameters; Induced strain; Strain data; Strain fields; Temperature-induced; Temperature-induced strain; Temperature",,,,,"National Natural Science Foundation of China, NSFC: 51978154, 52008099; Natural Science Foundation of Jiangsu Province: BK20190013, BK20200369; National Key Research and Development Program of China, NKRDPC: 2021YFF0500900; Fundamental Research Funds for the Central Universities: 2242022k3003, 2242022k30031","This research work was jointly supported by the National Key R&D Program of China (Grant. 2021YFF0500900), National Natural Science Foundation of China (Grants. 52008099 and 51978154), Natural Science Foundation of Jiangsu Province (Grants. BK20200369 and BK20190013), and Fundamental Research Funds for the Central Universities (Grants. 2242022k30031 and 2242022k3003).",,,,,,,,,,"Bao, Y.Q., Chen, Z.C., Wei, S.Y., Xu, Y., Tang, Z.Y., Li, H., The state of the art of data science and engineering in structural health monitoring (2019) Engineering, 5 (2), pp. 234-242; Sun, L., Shang, Z., Xia, Y., Bhowmick, S., Nagarajaiah, S., Review of bridge structural health monitoring aided by big data and artificial intelligence: from condition assessment to damage detection (2020) J Struct Eng, 146 (5), p. 04020073; Li, A.Q., Ding, Y.L., Wang, H., Guo, T., Analysis and assessment of bridge health monitoring mass data—progress in research/development of “Structural Health Monitoring (2012) Sci China Technol Sci, 55 (8), pp. 2212-2224; Huang, H.B., Yi, T.H., Li, H.N., Liu, H., Strain-based performance warning method for bridge main girders under variable operating conditions (2020) J Bridg Eng, 25 (4), p. 04020013; Zhang, J., Xia, Q., Cheng, Y.Y., Wu, Z.S., Strain flexibility identification of bridges from long-gauge strain measurements (2015) Mech Syst Signal Process, 62-63, pp. 272-283; 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Computer Science arXiv:1412.6980v9","Ding, Y.-L.; Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, China; email: civilchina@hotmail.com",,,"Springer Science and Business Media Deutschland GmbH",,,,,21905452,,,,"English","J. Civ. Struct. Health Monit.",Article,"Article in Press","",Scopus,2-s2.0-85140052808
"Catbas N., Avci O.","57204279590;6701761980;","A Review of Latest Trends in Bridge Health Monitoring",2022,"Proceedings of the Institution of Civil Engineers: Bridge Engineering",,,,"","",,,"10.1680/jbren.21.00093","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139495576&doi=10.1680%2fjbren.21.00093&partnerID=40&md5=c77293c0f2b75edf60d4c9fac47e895c","Department of Civil, Environmental and Construction Engineering, Civil Infrastructure Technologies for Resilience and Safety (CITRS), University of Central Florida, Orlando, FL, United States; Department of Civil and Environmental Engineering, West Virginia University, Morgantown, WV, United States","Catbas, N., Department of Civil, Environmental and Construction Engineering, Civil Infrastructure Technologies for Resilience and Safety (CITRS), University of Central Florida, Orlando, FL, United States; Avci, O., Department of Civil and Environmental Engineering, West Virginia University, Morgantown, WV, United States","Structural damage is inherent in civil engineering structures and bridges are no exception. It is vital to monitor and keep track of damage on bridge structures due to multiple mechanical, environmental, and traffic-induced factors. Monitoring the formation and propagation of structural damage is also pertinent for enhancing the service life of bridges. Bridge Health Monitoring (BHM) has always been an active research area for engineers and stakeholders. While all monitoring techniques intend to provide accurate and decisive information on the remaining useful life, safety, integrity, and serviceability of bridges; maintaining the uninterrupted operation of a bridge highly relies on understanding the development and propagation of damage.¯BHM methods have been extensively researched on bridges over the decades, and new methodologies have started to be used by domain experts, especially within the last decade.¯ Emerging methods, as the products of the technology advancements, resulted in handy tools that have been quickly adopted by bridge engineers. State-of-The-Art techniques such as LiDAR, Photogrammetry, Virtual Reality (VR) and Augmented Reality (AR), Digital Twins, Computer Vision, Machine Learning, and Deep Learning are now integrated part of the new-generation BHM operations.¯This paper presents a brief overview of these latest BHM technologies. © 2022 ICE Publishing: All rights reserved.","Augmented Reality (AR); Bridge Health Monitoring; Bridges; Computer Vision; Deep Learning; Digital Twins; LiDAR; Machine Learning; Photogrammetry; Structural Health Monitoring; UN SDG 11: Sustainable cities and communities; UN SDG 9: Industry innovation and infrastructure; United Nations Sustainable Development Goals; Virtual Reality (VR)","Augmented reality; Bridges; Computer vision; Deep learning; E-learning; Learning systems; Lithium compounds; Optical radar; Structural health monitoring; Sustainable development; Virtual reality; Augmented reality; Bridge health monitoring; Deep learning; Industry infrastructure; Industry innovations; LiDAR; Machine-learning; Sustainable cities; Sustainable communities; UN SDG 11: sustainable city and community; UN SDG 9: industry innovation and infrastructure; United nation sustainable development goal; United Nations; Virtual reality; Photogrammetry",,,,,,,,,,,,,,,,"Abdeljaber, O., Avci, O., Do, N.T., Gul, M., Celik, O., Necati Catbas, F., Quantification of structural damage with self-organizing maps (2016) Conference Proceedings of the Society for Experimental Mechanics Series; Abdeljaber, O., Avci, O., Kiranyaz, M.S., Boashash, B., Sodano, H., Inman, D.J., 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data (2017) Neurocomputing; Abdeljaber, O., Sassi, S., Avci, O., Kiranyaz, S., Ibrahim, A.A., Gabbouj, M., Fault detection and severity identification of ball bearings by online condition monitoring (2019) IEEE Transactions on Industrial Electronics; Abu Dabous, S., Feroz, S., Condition monitoring of bridges with non-contact testing technologies (2020) Automation in Construction; Adhikari, R.S., Moselhi, O., Bagchi, A., Image-based retrieval of concrete crack properties for bridge inspection (2014) Automation in Construction, 39, pp. 180-194. , Elsevier B.V; Almutairi, M., Avci, O., Nikitas, N., Efficiency of 1d cnns in finite element model parameter estimation using synthetic dynamic responses (2020) Proceedings of the International Conference on Structural Dynamic, EURODYN; Almutairi, M., Nikitas, N., Abdeljaber, O., Avci, O., Bocian, M., A methodological approach towards evaluating structural damage severity using 1D CNNs (2021) Structures, 34; A comprehensive assessment of America?s Infrastructure (2021) ASCE, , American Society of Civil Engineers; Failure to Act: Economic Impacts of Status Quo Investment Across Infrastructure Systems (2021) ASCE, , American Society of Civil Engineers; Apicella, A., Donnarumma, F., Isgrò, F., Prevete, R., A survey on modern trainable activation functions (2021) Neural Networks; Arias, P., Armesto, J., Di-Capua, D., González-Drigo, R., Lorenzo, H., Pérez-Gracia, V., Digital photogrammetry, GPR and computational analysis of structural damages in a mediaeval bridge (2007) Engineering Failure Analysis; Athanasiou, A., Salamone, S., Acquisition and management of field inspection data using augmented reality (2020); Avci, O., Abdeljaber, O., Self-organizing maps for structural damage detection: A novel unsupervised vibration-based algorithm (2016) Journal of Performance of Constructed Facilities, 30, p. 3; Avci, O., Abdeljaber, O., Kiranyaz, S., Structural Damage Detection in Civil Engineering with Machine-Learning: Current State of the Art (2021) Conference Proceedings of the Society for Experimental Mechanics Series; Avci, O., Abdeljaber, O., Kiranyaz, S., Boashash, B., Sodano, H., Inman, D.J., Efficiency Validation of One Dimensional Convolutional Neural Networks for Structural Damage Detection Using a SHM Benchmark Data (2018) 25th International Congress on Sound and Vibration; Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M., Inman, D.J., A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications (2021) Mechanical Systems and Signal Processing; Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Inman, D.J., Wireless and Real-Time Structural Damage Detection: A Novel Decentralized Method for Wireless Sensor Networks (2018) Journal of Sound and Vibration; Avci, O., Abdeljaber, O., Kiranyaz, S., Inman, D., Structural damage detection in real time: Implementation of 1d convolutional neural networks for shm applications (2017) Structural Health Monitoring & Damage Detection Volume 7: Proceedings of the 35th IMAC, A Conference and Exposition on Structural Dynamics 2017, C. 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Xu, Y., Brownjohn, J., Kong, D., A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge (2018) Structural Control and Health Monitoring, 25 (5), pp. 1-23; Ye, C., Butler, L., Calka, B., Iangurazov, M., Lu, Q., Gregory, A., Girolami, M., Middleton, C., A digital twin of bridges for structural health monitoring (2019) Structural Health Monitoring 2019: Enabling Intelligent Life-Cycle Health Management for Industry Internet of Things (IIOT)-Proceedings of the 12th International Workshop on Structural Health Monitoring; Ye, X.W., Jin, T., Yun, C.B., A review on deep learning-based structural health monitoring of civil infrastructures (2019) Smart Structures and Systems; Zhang, L., Shen, J., Zhu, B., A research on an improved Unet-based concrete crack detection algorithm (2020) Structural Health Monitoring, 29; Zollini, S., Alicandro, M., Dominici, D., Quaresima, R., Giallonardo, M., UAV photogrammetry for concrete bridge inspection using object-based image analysis (OBIA) (2020) Remote Sensing","Catbas, N.; Department of Civil, United States; email: catbas@ucf.edu",,,"ICE Publishing",,,,,14784637,,,,"English","Proc. Inst. Civ. Eng. Bridge Eng.",Review,"Article in Press","",Scopus,2-s2.0-85139495576
"Peddinti P.R.T., Kim B.","57190983544;55726495200;","Efficient Pavement Monitoring for South Korea Using Unmanned Aerial Vehicles",2022,"International Conference on Transportation and Development 2022: Application of Emerging Technologies - Selected Papers from the Proceedings of the International Conference on Transportation and Development 2022","5",,,"61","72",,,"10.1061/9780784484357.006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138746454&doi=10.1061%2f9780784484357.006&partnerID=40&md5=1e67e417e93d0ad91e3767bbecff1baf","Dept. of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea; Dept. of Civil Engineering, Pandit Deendayal Energy Univ., Raisan, Gujarat, India","Peddinti, P.R.T., Dept. of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea, Dept. of Civil Engineering, Pandit Deendayal Energy Univ., Raisan, Gujarat, India; Kim, B., Dept. of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea","The present study is aimed at using unmanned aerial vehicles (UAVs) for pavement monitoring. It was taken up as an initial pilot study to develop a network level asset management framework for South Korean conditions. A varying pavement stretch containing a bridge, culvert, streams, and merging traffic junctions was selected for the study. Using a quadcopter UAV, 139 overlapping images were acquired and processed using a structure from motion (SfM) program to develop a digital twin of the pavement. The generated orthomosaic and 3D digital twin were used to identify pavement damage and other infrastructural assets. UAV-based image acquisition was found to provide sufficient resolution, exposure, and key point matches, enabling an accurate 3D model generation with detailed feature extraction. Geometric measurements of various features depicted the potential and efficiency of UAV surveys. The research work is expected to aid in effective contactless pavement monitoring and asset management during regular surveys as well as disasters. © ASCE.","Cracking; damage; Monitoring; Pavement; UAV","Antennas; Asset management; Image acquisition; Pavements; Unmanned aerial vehicles (UAV); Aerial vehicle; Assets management; Condition; Damage; Management frameworks; Network level; Pavement monitoring; Pilot studies; South Korea; Unmanned aerial vehicle; Surveys",,,,,"National Research Foundation of Korea, NRF: 2021H1D3A2A02044785","This work was supported by a National Research Foundation of Korea (NRF) grant for the Brain Pool project (2021H1D3A2A02044785).",,,,,,,,,,"Barmpounakis, E.N., Vlahogianni, E.I., Golias, J.C., Unmanned Aerial Aircraft Systems for transportation engineering: Current practice and future challenges (2016) International Journal of Transportation Science and Technology, 5 (3), pp. 111-122; Congress, S.S.C., Puppala, A.J., Evaluation of UAV-CRP data for monitoring transportation infrastructure constructed over expansive soils (2020) Indian Geotechnical Journal, 50 (2), pp. 159-171; Congress, S.S.C., Puppala, A.J., (2021) International Conference on Transportation and Development 2021, pp. 321-331. , Digital Twinning Approach for Transportation Infrastructure Asset Management Using UAV Data."" In (pp.); Congress, S.S.C., Puppala, A.J., Kumar, P., Patil, U.D., (2021) International Conference on Transportation and Development 2021, pp. 332-343. , Assessment of Pavement Geometric Characteristics Using UAV-CRP Data."" In (pp.); Congress, S.S., Puppala, A.J., Lundberg, C.L., Total system error analysis of UAV-CRP technology for monitoring transportation infrastructure assets (2018) Engineering Geology, 247, pp. 104-116; Davis, B.J., (2018), http://scholarworks.uark.edu/etd/1468, Development of the MASW Method for Pavement Evaluation. Theses and Dissertations. 1468, University of Arkansas, USA; Dobson, R.J., Brooks, C., Roussi, C., Colling, T., (2013) 2013 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 235-243. , (, May). "" Developing an unpaved road assessment system for practical deployment with high-resolution optical data collection using a helicopter UAV."" In (pp.). IEEE; Du Tertre, A., (2010), Non-destructive evaluation of asphalt pavement joints using LWD and MASW tests. Master's thesis, University of Waterloo, Ontario, Canada; Ferrer-Espinoza, D., Atencio, E., Muñoz-La Rivera, F., Herrera, R.F., Evaluation of the use of Cascade Detection algorithms based on Machine Learning for Crack Detection in asphalt Pavements (2021) Solid State Technology, 64 (2), pp. 5588-5605; Gucunski, N., (2013) Nondestructive testing to identify concrete bridge deck deterioration, , National Research Council. Transportation Research Board 2013; Hong, Z., Yang, F., Pan, H., Zhou, R., Zhang, Y., Han, Y., Wang, J., Liu, J., Highway Crack Segmentation from Unmanned Aerial Vehicle Images Using Deep Learning (2021) IEEE Geoscience and Remote Sensing Letters; Inzerillo, L., Di Mino, G., Roberts, R., Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress (2018) Automation in Construction, 96, pp. 457-469; Knyaz, V.A., Chibunichev, A.G., Photogrammetric techniques for road surface analysis (2016) Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci, 41 (B5), pp. 515-520; (2020) Guidelines for drone usage, , www.koca.co.kr, KOCA (Korea office of civil aviation). &lt;&gt;(accessed on 15thSeptember, 2021); Lee, J.K., (2019), UAV-based Pothole Identification: A Photogrammetric Approach (Doctoral dissertation); Outay, F., Mengash, H.A., Adnan, M., Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges (2020) Transportation research part A: policy and practice, 141, pp. 116-129; Pan, Y., Chen, X., Sun, Q., Zhang, X., Monitoring Asphalt Pavement Aging and Damage Conditions from Low-Altitude UAV Imagery Based on a CNN Approach (2021) Canadian Journal of Remote Sensing, 47 (3), pp. 432-449; Pan, Y., Zhang, X., Cervone, G., Yang, L., Detection of asphalt pavement potholes and cracks based on the unmanned aerial vehicle multispectral imagery (2018) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11 (10), pp. 3701-3712; Puppala, A.J., Congress, S.S.C., (2019) International Conference on Information technology in Geo-Engineering, pp. 3-17. , (, September). "" A holistic approach for visualization of transportation infrastructure assets using UAV-CRP technology."" In (pp.). Springer, Cham; Saad, A.M., Tahar, K.N., Identification of rut and pothole by using multirotor unmanned aerial vehicle (UAV) (2019) Measurement, 137, pp. 647-654; Shanghlil, N., Khalafallah, A., (2018) Construction Research Congress, pp. 2-4. , (, March). "" Automating highway infrastructure maintenance using unmanned aerial vehicles."" In (pp.); Silva, L.A., Sanchez San Blas, H., Peral García, D., Sales Mendes, A., Villarubia González, G., An architectural multi-agent system for a pavement monitoring system with pothole recognition in UAV images (2020) Sensors, 20 (21), p. 6205; Tan, Y., Li, Y., UAV photogrammetry-based 3D road distress detection (2019) ISPRS International Journal of Geo-Information, 8 (9), p. 409; Wu, W., Qurishee, M.A., Owino, J., Fomunung, I., Onyango, M., Atolagbe, B., (2018) 2018 IEEE International Smart Cities Conference (ISC2), pp. 1-7. , (, September). "" Coupling deep learning and UAV for infrastructure condition assessment automation."" In (pp.). IEEE; Zhang, C., (2008) Proceedings of American Society for Photogrammetry and Remote Sensing Annual Conference, , (, July). "" Development of a UAV-based remote sensing system for unpaved road condition assessment."" In. Portland, OR; Zhang, C., Elaksher, A., An unmanned aerial vehicle-based imaging system for 3D measurement of unpaved road surface distresses (2012) Computer-Aided Civil and Infrastructure Engineering, 27 (2), pp. 118-129; Zhu, J., Zhong, J., Ma, T., Huang, X., Zhang, W., Zhou, Y., Pavement distress detection using convolutional neural networks with images captured via UAV (2022) Automation in Construction, 133, p. 103991","Kim, B.; Dept. of Urban and Environmental Engineering, South Korea; email: byungmin.kim@unist.ac.kr","Wei H.","The Transportation and Development Institute (T and DI) of the American Society of Civil Engineers (ASCE);Washington State Department of Transportation","American Society of Civil Engineers (ASCE)","International Conference on Transportation and Development 2022, ICTD 2022","31 May 2022 through 3 June 2022",,182450,,9780784484357,,,"English","Int. Conf. Transp. Dev.: Appl. Emerg. Technol. - Sel. Pap. Proc. Int. Conf. Transp. Dev.",Conference Paper,"Final","",Scopus,2-s2.0-85138746454
"Zerbinatti M., Fasana S.","56043868500;36542380700;","Approaches Proposal for Tools Coordinating in Maintenance and Reuse of Architectural Heritage. A Case Study on Urban Complexes of Modern Architectural Heritage",2022,"Lecture Notes in Networks and Systems","482 LNNS",,,"2648","2658",,,"10.1007/978-3-031-06825-6_253","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138654026&doi=10.1007%2f978-3-031-06825-6_253&partnerID=40&md5=a73570faaa16fd8eb574278d2d8e49f5","Politecnico di Torino, C.so Duca degli Abruzzi, 24, Turin, Italy","Zerbinatti, M., Politecnico di Torino, C.so Duca degli Abruzzi, 24, Turin, Italy; Fasana, S., Politecnico di Torino, C.so Duca degli Abruzzi, 24, Turin, Italy","Resilience assumed at a building scale often refers to efficiency and safety aspects, while extended to the urban and territorial scale it involves (among others) safety, energy efficiency and infrastructure aspects. Actually, it seems rather more complex imagine to bridge these goals together to a reference framework for Cultural Heritage, where technical and regulatory requirements must be usefully balanced with those of enhancing and conservation and, not at least, social involvement. In this perspective, authors deal with an on-going research, referred to an emblematic example of urban environment, recently added to UNESCO’s World Heritage List: Ivrea Industrial City of XX century. Here an innovative effort in maintenance program can interpret actual urgent needed in terms of conservation, but, at the same time, it can represent an instrument to govern and coordinate future sustainable transformative and regenerative planning. This paper presents the methodological approach and first results of the research program, which final aim is to develop an integrated BIM-GIS-based tool for coordinated and sustainable redesign and maintenance of complex built heritage environments. Original identity, strictly related to local resources, are considered, with the aim to reach a renewal of perspective, to promote and enhance a circular society, not far from Olivetti's ideals, but also consistent with goals proposed in Agenda 2030. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Digital tools for re-design architectures; Digital-Twin; Innovative maintenance program; Ivrea Industrial City of XX century",,,,,,,,,,,,,,,,,"Xu, X., Ding, L., Luo, H., Ma, L., From building information modeling to city information modeling (2014) J. Inf. Technol. Constr., 19, pp. 292-307; Patti, E., Ronzino, A., Osello, A., Verda, V., Acquaviva, A., Macii, E., District information modeling and energy management (2015) IT Professional, 17 (6), pp. 28-34; Murphy, M., McGovern, E., Pavia, S., Historic building information modelling (HBIM) (2009) Struct. Surv., 27 (4); Dimensions and levels of knowledge in heritage building information modelling, HBIM: The model of the charterhouse of Jerez (Cadiz, Spain) (2019) Digital App. Archaeol. Cultural Heritage., 14. , https://doi.org/10.1016/j.daach.2019.e00110; Buitrago, E., Schneider, N., Rugged LV Trench IGBT with Extreme Stability in Continuous SOA Operation: Next Generation LV Technology at Hitachi ABB Powergrids PCIM Europe Digital Days 2021; Brumana, R., Oreni, D., Raimondi, A., Georgopoulos, A., Bregianni, A., From survey to HBIM for documentation, dissemination and management of built heritage: The case study of St. Maria in Scaria d’Intelvi (2013) Digital Heritage Int. Congr. IEEE, 1, pp. 497-504; García-Valldecabres, J., Pellicer, E., Jordan-Palomar, I., BIM scientific literature review for existing buildings and a theoretical method: Proposal for heritage data management using HBIM (2016) Constr. Res. Congr., pp. 2228-2238; Brumana, R., Georgopoulos, A., Oreni, D., Raimondi, A., Bregianni, A., HBIM for documentation, dissemination and management of built heritage. The case study of St. Maria in Scaria d’Intelvi (2020) Int. J. Heritage Digital Era., 2 (3), pp. 433-451; García, E.S., García-Valldecabres, J.O.R.G.E., Blasco, M.J., The use of HBIM models as a tool for dissemination and public use management of historical architecture: A review (2018) Build. Inf. Syst. Constr. Indust., 101; Bruno, N., Roncella, R., HBIM for conservation: A new proposal for information modelling (2019) Remote Sens, 11 (15), p. 1751; Vacca, G., Quaquero, E., Pili, D., Brandolini, M., GIS-HBIM integration for the management of historical buildings (2018) Int. Arch. Photogramm. Remote. Sens. Spatial. Inf. Sci., 42 (2), pp. 1-7; Matrone, F., Colucci, E., de Ruvo, V., Lingua, A., Spanò, A., HBIM in a semantic 3D GIS database (2019) Int. Arch. Photogramm. Remote. Sens. Spatial. Inf. Sci., 42 (2), p. W11; Buitrago, E., Schneider, N., Rugged LV Trench IGBT with Extreme Stability in Continuous SOA Operation: Next Generation LV Technology at Hitachi ABB Powergrids PCIM Europe Digital Days 2021; Colucci, E., de Ruvo, V., Lingua, A., Matrone, F., Rizzo, G., HBIM-GIS integration: From IFC to cityGML standard for damaged cultural heritage in a multiscale 3D GIS (2020) Appl. Sci., 10 (4), p. 1356; Tsilimantou, E., Delegou, E.T., Nikitakos, I.A., Ioannidis, C., Moropoulou, A., GIS and BIM as integrated digital environments for modelling and monitoring of historic buildings (2020) Appl. Sci., 10 (3), p. 1078; Buitrago, E., Schneider, N., Rugged LV Trench IGBT with Extreme Stability in Continuous SOA Operation: Next Generation LV Technology at Hitachi ABB Powergrids PCIM Europe Digital Days 2021; Buitrago, E., Schneider, N., Rugged LV Trench IGBT with Extreme Stability in Continuous SOA Operation: Next Generation LV Technology at Hitachi ABB Powergrids PCIM Europe Digital Days 2021; Fiamma, P., Il B.I.M. per l’architettura tecnica: Ingegno e costruzione nell’epoca della com-plessita’. In: Ingegno e costruzione nell’epoca della complessità. Forma urbana e individ-ualità architettonica (2019) Proceedings of Col-Loqui.At.E, Turin, Italy, 2019, pp. 718-727. , Garda, E., Mele, C., Piantanida, P., (eds.) , Politecnico di Torino, Torino, ). ISBN: 978-88-85745-31-5; Megahed, N.A., Towards a theoretical framework for HBIM approach. Historic preservation and management (2015) Int. J. Archit. Res., 9, pp. 130-147; Bianchini, C., Attenni, M., Potestà, G., Regenerative design tools for the existing city: HBIM potentials (2021) Rethinking Sustainability Towards a Regenerative Economy. FC, 15, pp. 23-43. , https://doi.org/10.1007/978-3-030-71819-0_2, Andreucci, M.B., Marvuglia, A., Baltov, M., Hansen, P. (eds.), Springer, Cham; Colucci, E., Iacono, E., Matrone, F., Ventura, G.M., A BIM-GIS integrated database to support planned maintenance activities of historical built heritage (2022) Geomatics and Geospatial Technologies. CCIS, 1507, pp. 182-194. , https://doi.org/10.1007/978-3-030-94426-1_14, Borgogno-Mondino, E., Zamperlin, P. (eds.) , Springer, Cham; de Ruvo, V., Development of integrated management tools for a maintenance plan of historical heritage (2021) 9Th ARQUEOLÓGICA 2.0 and 3Rd GEORES 2021 Proceedengs, pp. 26-28. , April; Buitrago, E., Schneider, N., Rugged LV Trench IGBT with Extreme Stability in Continuous SOA Operation: Next Generation LV Technology at Hitachi ABB Powergrids PCIM Europe Digital Days 2021","Fasana, S.; Politecnico di Torino, C.so Duca degli Abruzzi, 24, Italy; email: sara.fasana@polito.it","Calabro F.Della Spina L.Pineira Mantinan M.J.",,"Springer Science and Business Media Deutschland GmbH","5th International Symposium on New Metropolitan Perspectives, NMP 2022","25 May 2022 through 27 May 2022",,282399,23673370,9783031068249,,,"English","Lect. Notes Networks Syst.",Conference Paper,"Final","",Scopus,2-s2.0-85138654026
"Congress S.S.C., Escamilla J., Chimauriya H., Puppala A.J.","57202911843;57899809700;57221910669;35586394600;","Challenges of 360° Inspection of Bridge Infrastructure Using Unmanned Aerial Vehicles (UAVs)",2022,"International Conference on Transportation and Development 2022: Application of Emerging Technologies - Selected Papers from the Proceedings of the International Conference on Transportation and Development 2022","6",,,"96","108",,,"10.1061/9780784484364.009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138490066&doi=10.1061%2f9780784484364.009&partnerID=40&md5=3c71c73736971c3b3f3e806657f30393","Zachry Dept. of Civil and Environmental Engineering, Texas A and M Univ., College Station, TX, United States; Bridge Design Division, Alaska Dot and Pf, Juneau, AK, United States","Congress, S.S.C., Zachry Dept. of Civil and Environmental Engineering, Texas A and M Univ., College Station, TX, United States; Escamilla, J., Bridge Design Division, Alaska Dot and Pf, Juneau, AK, United States; Chimauriya, H., Zachry Dept. of Civil and Environmental Engineering, Texas A and M Univ., College Station, TX, United States; Puppala, A.J., Zachry Dept. of Civil and Environmental Engineering, Texas A and M Univ., College Station, TX, United States","Bridge asset management is key as it connects the transportation network spanning across the United States. According to the recent 2021 ASCE Infrastructure Report Card, 42% of 617,000 bridges in the US are over 50 years old and 7.5% of the total bridges are considered structurally deficient. A structurally deficient bridge means that it requires monitoring, inspection, and maintenance rather than it being unsafe to travel. FHWA and the state Departments of Transportation (DOTs) have coordinated frequent inspections to keep the number of structurally deficient bridges to a minimum. Traditional inspections are sometimes limited by access, cost, and safety. Recently, many DOTs have adopted unmanned aerial vehicles (UAVs) to access hard-to-reach areas and quickly monitor the conditions of the bridges. Not many studies have reported about 360° inspection of bridges using three-dimensional (3D) models generated from aerial imagery. This study demonstrated the application of UAVs for conducting 360° inspection of bridge infrastructure assets. Three-dimensional models of the bridge deck and under-span elements were developed to provide an immersive navigation experience to the bridge inspector. Several key observations were made based on the data collection and processing of models for a 360° bridge inspection. This study helped understand the limitations and best practices of the application of UAVs for conducting a 360° bridge inspection. © 2022 International Conference on Transportation and Development","360° Inspection; Bridges; Digital Twin; Infrastructure; Steel Truss; UAV","Aerial photography; Antennas; Bridges; Data handling; Three dimensional computer graphics; Unmanned aerial vehicles (UAV); 360° inspection; Aerial vehicle; Assets management; Bridge infrastructure; Bridge inspection; Deficient bridges; Infrastructure; Management IS; Steel truss; Unmanned aerial vehicle; Inspection",,,,,"Federal Highway Administration, FHWA: 2521H033; Alaska Department of Transportation and Public Facilities, DOT&PF","We would like to thank The Alaska Department of Transportation & Public Facilities (Alaska DOT&PF) and Federal Highway Administration (FHWA) for funding project 2521H033. We acknowledge Anna Bosin, Ryan Marlow, Erin Anderson, and Kamron Jafari for their support towards the project tasks.",,,,,,,,,,"(2017) Alaska Bridges and Structures Manual, , AKDOT&PF; (2021) America's Infrastructure Report Card 2021, , https://infrastructurereportcard.org/, ASCE. Accessed March 6, 2021; Barfuss, S., Jensen, A., Clemens, S., (2012) Evaluation and development of unmanned aircraft (UAV) for UDOT needs, , Utah. Dept. of Transportation. Research Division; Brooks, C., Dobson, R.J., Banach, D.M., Dean, D., Oommen, T., Wolf, R.E., Havens, T.C., Hart, B., (2015) Evaluating the use of unmanned aerial vehicles for transportation purposes; Congress, S.S.C., (2018) Novel Infrastructure Monitoring Using Multifaceted Unmanned Aerial Vehicle Systems-Close Range Photogrammetry (UAV-CRP) Data Analysis; Congress, S.S.C., Puppala, A.J., Gajurel, A., Jafari, N.H., Transforming Aerial Reconnaissance Data of Pavement Infrastructure into Knowledge for Better Response to Natural Disasters (2021) Geo-Extreme 2021, pp. 183-193; Congress, S.S.C., Puppala, A.J., Khan, M.D.A., Biswas, N., Application of Unmanned Aerial Technologies for Inspecting Pavement and Bridge Infrastructure Asset Condition (2020) TRB Centennial Circular, p. 134; Congress, S.S.C., Puppala, A.J., Lundberg, C.L., Total system error analysis of UAV-CRP technology for monitoring transportation infrastructure assets (2018) Engineering Geology, , Elsevier; Dorafshan, S., Maguire, M., Hoffer, N.V., Coopmans, C., Challenges in bridge inspection using small unmanned aerial systems: Results and lessons learned (2017) 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1722-1730; Gillins, D.T., Parrish, C., Gillins, M.N., Simpson, C., (2018) Eyes in the Sky: Bridge Inspections with Unmanned Aerial Vehicles; Inzerillo, L., Di Mino, G., Roberts, R., Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress (2018) Automation in Construction, 96, pp. 457-469. , Elsevier; McGlone, J.C., Mikhail, E.M., Bethel, J.S., Mullen, R., (2004) Manual of photogrammetry, , American society for photogrammetry and remote sensing Bethesda, MD; Metni, N., Hamel, T., A UAV for bridge inspection: Visual servoing control law with orientation limits (2007) Automation in construction, 17 (1), pp. 3-10. , Elsevier; Mikhail, E.M., Bethel, J.S., McGlone, J.C., (2001) Introduction to modern photogrammetry, p. 19. , New York; Neubauer, K., Bullard, E., Blunt, R., Collection of Data with Unmanned Aerial Systems (UAS) for Bridge Inspection and Construction Inspection (2021) United States. Federal Highway Administration, , Office of Infrastructure; Puppala, A.J., Congress, S.S.C., (2021) Implementation of Unmanned Aerial Systems Using Close-Range Photogrammetry Techniques (UAS-CRP) For Quantitative (Metric) And Qualitative (Inspection) Tasks Related to Roadway Assets And Infrastructures; Ryan, T.W., EricMann, J., Chill, Z.M., Ott, B.T., (2012) Bridge Inspector's Reference Manual. No. FHWA NHI 12-049; Ryan, T.W., EricMann, J., Chill, Z.M., Ott, B.T., (2012) Bridge Inspector's Reference Manual. No. FHWA NHI 12-049; Tomiczek, A.P., Whitley, T.J., Bridge, J.A., Ifju, P.G., Bridge inspections with small unmanned aircraft systems: Case studies (2019) Journal of Bridge Engineering, 24 (4), p. 5019003. , American Society of Civil Engineers; Wells, J., Lovelace, B., Engineers, C., (2017) Unmanned aircraft system bridge inspection demonstration project phase II final report, , Minnesota. Dept. of Transportation. Research Services & Library; Zhang, C., An UAV-based photogrammetric mapping system for road condition assessment (2008) Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci, 37, pp. 627-632",,"Wei H.","The Transportation and Development Institute (T and DI) of the American Society of Civil Engineers (ASCE);Washington State Department of Transportation","American Society of Civil Engineers (ASCE)","International Conference on Transportation and Development 2022, ICTD 2022","31 May 2022 through 3 June 2022",,182450,,9780784484364,,,"English","Int. Conf. Transp. Dev.: Appl. Emerg. Technol. - Sel. Pap. Proc. Int. Conf. Transp. Dev.",Conference Paper,"Final","",Scopus,2-s2.0-85138490066
"Pohlmann J., Matthe M., Kronauer T., Auerbach P., Fettweis G.","57211883458;23989107600;57215744258;57885593600;7007049752;","ROS2-based Small-Scale Development Platform for CCAM Research Demonstrators",2022,"IEEE Vehicular Technology Conference","2022-June",,,"","",,,"10.1109/VTC2022-Spring54318.2022.9860981","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137754563&doi=10.1109%2fVTC2022-Spring54318.2022.9860981&partnerID=40&md5=1387b3105b0507a1ac10d41f36291608","Barkhausen Institut GGmbH, Germany","Pohlmann, J., Barkhausen Institut GGmbH, Germany; Matthe, M., Barkhausen Institut GGmbH, Germany; Kronauer, T., Barkhausen Institut GGmbH, Germany; Auerbach, P., Barkhausen Institut GGmbH, Germany; Fettweis, G., Barkhausen Institut GGmbH, Germany","This work proposes an architecture and platform for researching and demonstrating use-cases for connected cars based on small-scale vehicles. The proposal bridges the gap between a lab setup to test individual algorithms and deployments on real cars. It allows researchers to communicate their results with a small-scale indoor demonstrator. The platform employs ROS2 and MicroROS to allow for a modular and scalable hardware and software setup. Moreover, it allows running all control algorithms in a graphical simulation to ease development of complex scenarios. We successfully apply the platform to build a demonstrator for a platooning use-case and point out limitations such as lacking photorealism of the simulation and limited processing power of the platform. Our results indicate that using a well-designed platform and architecture can significantly reduce required effort for implementing connected cars use-cases. © 2022 IEEE.","CCAM; CPS; DDS; DDS-XRCE; demonstrator; digital twin; LGSVL; microROS; platform; real-time; ROS2; science communication; simulation; V2V; V2X","Simulation platform; CCAM; CPS; DDS; DDS-XRCE; Demonstrator; LGSVL; Microros; Platform; Real- time; ROS2; Science communications; Simulation; V2V; V2X; Vehicle to Everything",,,,,,,,,,,,,,,,"Bagheri, H., Noor-A-Rahim, M., Liu, Z., Lee, H., Pesch, D., Moessner, K., Xiao, P., 5g nr-v2x: Toward connected and cooperative autonomous driving (2021) IEEE Communications Standards Magazine, 5 (1), pp. 48-54; Liu, J., Liu, J., Intelligent and connected vehicles: Current situation, future directions, and challenges (2018) IEEE Communications Standards Magazine, 2 (3), pp. 59-65; Centenaro, M., Berlato, S., Carbone, R., Burzio, G., Cordella, G.F., Riggio, R., Ranise, S., Safety-related cooperative, connected, and automated mobility services: Interplay between functional and security requirements (2021) IEEE Vehicular Technology Magazine, 16 (4), pp. 78-88; Leilabadi, S.H., Katzorke, N., Moosmann, M., Schmidt, S., Systematic test case design for autonomous vehicles (2020) 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1-6; (2018) Road Vehicles-Functional Safety, , International Organization for Standardization, Standard; Daigmorte, H., Boyer, M., Migge, J., Reducing CAN latencies by use of weak synchronization between stations (2017) 16th International CAN Conference, pp. 1-8. , https://oatao.univ-toulouse.fr/23021/, Nuremberg, DE; Garcia, M.H.C., Molina-Galan, A., Boban, M., Gozalvez, J., Coll-Perales, B., Ahin, T., Kousaridas, A., A tutorial on 5g nr v2x communications (2021) IEEE Communications Surveys Tutorials, 23 (3), pp. 1972-2026; La, H.M., Lim, R.S., Du, J., Sheng, W., Li, G., Zhang, S., Chen, H., A small-scale research platform for intelligent transportation systems (2011) 2011 IEEE International Conference on Robotics and Biomimetics, pp. 1373-1378; Pandi, S., Fitzek, F.H.P., Lehmann, C., Nophut, D., Kiss, D., Kovacs, V., Nagy, A., Liebhart, R., Joint design of communication and control for connected cars in 5g communication systems (2016) 2016 IEEE Globecom Workshops (GC Wkshps), pp. 1-7; Valtl, J., Mendez Gomez, J., Cullar, M., Issakov, V., (2021) Autonomous Platform Based on Small-Scale Car for Versatile Data Collection and Algorithm Verification, , 04; Quartey, B., Ayorkor Korsah, G., Affordable modular autonomous vehicle development platform (2018) 2018 IEEE 7th International Conference on Adaptive Science Technology (ICAST), pp. 1-8; Profanter, S., Tekat, A., Dorofeev, K., Rickert, M., Knoll, A., OPC UA versus ROS, DDS, and MQTT: Performance Evaluation of Industry 4. 0 Protocols (2019) 2019 IEEE International Conference on Industrial Technology (ICIT), pp. 955-962; An Opensource DIY Self Driving Platform for Small Scale Cars, , https://web.archive.org/web/20220224103151/https://www.donkeycar.com/; (2022), https://zeromq.org, Accessed: 23rd Feb; (2022) DDS-Data Distribution Service, , https://www.dds-foundation.org/, Accessed: 23rd Feb; (2022) RTI Connext Drive, , https://web.archive.org/web/20220114191550/https://www.rti.com/drive, Real-Time Innovations; (2022) Connected Autonomous Vehicle Solutions, , https://web.archive.org/web/20220216174229/https://www.adlinktech.com/en/connected-autonomous-vehicle-solutions; Thomas, D., Woodall, W., Fernandez, E., Next-generation ROS: Building on DDS (2014) ROSCon Chicago 2014, , https://vimeo.com/106992622, Mountain View, CA: Open Robotics. Sep; Kronauer, T., Pohlmann, J., Matth, M., Smejkal, T., Fettweis, G., Latency analysis of ros2 multi-node systems (2021) 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 1-7; Dehnavi, S., Goswami, D., Koedam, M., Nelson, A., Goossens, K., Modeling, implementation, and analysis of XRCE-DDS applications in distributed multi-processor real-time embedded systems (2021) 2021 Design, Automation Test in Europe Conference Exhibition (DATE), pp. 1148-1151; Rong, G., (2020) LGSVL Simulator: A High Fidelity Simulator for Autonomous Driving, , https://arxiv.org/abs/2005.03778, CoRR abs/2005. 03778",,,"Huawei;Nokia;pix moving;Samsung;Technology Innovation Institute (TII)","Institute of Electrical and Electronics Engineers Inc.","95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring","19 June 2022 through 22 June 2022",,182307,15502252,9781665482431,IVTCD,,"English","IEEE Veh Technol Conf",Conference Paper,"Final","",Scopus,2-s2.0-85137754563
"Karaaslan E., Zakaria M., Catbas F.N.","57193868803;57880106700;6603396768;","Mixed reality-assisted smart bridge inspection for future smart cities",2022,"The Rise of Smart Cities: Advanced Structural Sensing and Monitoring Systems",,,,"261","280",,,"10.1016/B978-0-12-817784-6.00002-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137578880&doi=10.1016%2fB978-0-12-817784-6.00002-3&partnerID=40&md5=b3ad80f5ab16e5579a1214546941c050","Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, United States","Karaaslan, E., Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, United States; Zakaria, M., Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, United States; Catbas, F.N., Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, United States","Smart infrastructures aim more efficient and accurate methods of routine inspection for long-term monitoring of the infrastructure to make smarter decision on maintenance and rehabilitation. Although some recent technologies (i.e., robotic techniques) that are currently in practice can collect objective, quantified data, the inspector’s own expertise is still critical in many instances. Yet, these technologies are designed to replace human expertise, or are ineffective in terms of saving time and labor. This chapter investigates a new methodology for structural inspections with the help of mixed reality technology and real-time machine learning to accelerate certain tasks of the inspector such as detection, measurement, and assessment of defects, and easy accessibility to defect locations. A functional, real-time machine learning system that can be ideally deployed in mixed reality devices and headsets which can be used by inspectors during their routine concrete infrastructure inspection is introduced. The deep learning models to be employed in the AI system can localize a concrete defect in real time and further analyze it by performing pixel wise segmentation while running on a mobile device architecture. First, a sufficiently large database of concrete defect images is gathered from various sources including publicly available crack and spalling datasets, real-world images taken during bridge inspections, and the public images from the internet search results. For defect localization, various state-of-the-art deep learning model architectures are investigated based on their memory allocation, inference speed, and flexibility to deploy different deep learning platforms. YoloV5s model was found to be the optimal model architecture for concrete defect localization to be deployed in the mixed reality system. For defect quantification, several segmentation architectures with three different classification backbones are trained on the collected image dataset with segmentation labels. Based on the model evaluation results, the PSPNet with EfficientNet-b0 backbone is found to be the best performing model in terms of inference speed and accuracy. The selected models for defect localization and quantification are deployed to the mixed reality platform and image tracking libraries are configured in the platform environment, and accurate distance estimation is accomplished using a calibration process. Lastly, a methodology for condition assessment of concrete defects using the mixed reality system is discussed. The proposed methodology can locate and track the defects using the mixed reality platform, which can eventually be transferred to cloud data and potentially used for remote assessments or updating a digital twins or BIMs. © 2022 Elsevier Inc. All rights reserved.","Human-computer interaction; Mixed reality; Real-time machine learning; Smart cities; Smart infrastructure inspection; Structural health monitoring",,,,,,,,,,,,,,,,,"Adhikari, R.S., Moselhi, O., Bagchi, A., Image-based retrieval of concrete crack properties for bridge inspection (2014) Automation in Construction, 39, pp. 180-194; Alavi, A.H., Hasni, H., Jiao, P., Aono, K., Lajnef, N., Chakrabartty, S., Self-charging and self-monitoring smart civil infrastructure systems: Current practice and future trends (2019) Sensors and smart structures technologies for civil, mechanical, and aerospace systems 2019 International Society for Optics and Photonics, 10970, p. 109700W. , March; Azuma, R., Behringer, R., Feiner, S., Julier, S., Macintyre, B., Recent advances in augmented reality (2001), 2011, pp. 1-27. , IEEE computer graphics and applications, December; Badrinarayanan, V., Kendall, A., Cipolla, R., SegNet: A deep convolutional encoder-decoder architecture for image segmentation (2017) IEEE Transactions on Pattern Analysis and Machine Intelligence, 39 (12), pp. 2481-2495; Bae, H., Golparvar-Fard, M., White, J., High-precision vision-based mobile augmented reality system for context-aware architectural, engineering, construction and facility management (AEC/FM) applications (2013) Visualization in Engineering, 1 (1), p. 3; Behzadan, A.H., Dong, S., Kamat, V.R., Augmented reality visualization: A review of civil infrastructure system applications (2015) Advanced Engineering Informatics, 29 (2), pp. 252-267; Behzadan, A.H., Kamat, V.R., Georeferenced registration of construction graphics in mobile outdoor augmented reality (2007) Journal of Computing in Civil Engineering, 21 (4), pp. 247-258; Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M., YOLOv4: Optimal speed and accuracy of object detection (2020), arXiv preprint arXiv:2004.10934; Catbas, F.N., Gul, M., Zaurin, R., Gokce, H.B., Terrell, T., Dumlupinar, T., Long term bridge maintenance monitoring demonstration on a movable bridge (2010), p. 186. , June,, Final Report for Research Project, No; Catbas, F.N., Hiasa, S., Dong, C., Pan, Y., Celik, O., Karaaslan, E., Comprehensive structural health monitoring at local and global level with vision-based technologies 26th ASNT research symposium.; https://doi.org/10.1061/(ASCE)ST.1943-541X.0000682, Catbas, F. N., T. Kijewski-Correa, T. Kijewski-Correa, and A. M. Asce. Structural identification of constructed systems: Collective effort toward an integrated approach that reduces barriers to adoption. doi:; Coutrix, C., Nigay, L., Mixed reality: A model of mixed interaction (2006), pp. 43-50. , Proceedings of the working conference on advanced visual interfaces—AVI’06; Dong, C.-Z., Bas, S., Catbas, F.N., A portable monitoring approach using cameras and computer vision for bridge load rating in smart cities (2020) Journal of Civil Structural Health Monitoring, 10 (5), pp. 1001-1021; German, S., Brilakis, I., Desroches, R., Rapid entropy-based detection and properties measurement of concrete spalling with machine vision for post-earthquake safety assessments (2012) Advanced Engineering Informatics, 26 (4), pp. 846-858; Hiasa, S., Investigation of infrared thermography for subsurface damage detection of concrete structures (2016), UCF Libraries, UCF Electronic Theses and Dissertations (2004-2019); Hiasa, S., Catbas, F.N., Matsumoto, M., Mitani, K., Monitoring concrete bridge decks using infrared thermography with high speed vehicles (2016) Structural Monitoring and Maintenance, 3 (3), pp. 277-296; Hill, M., Overview of human-computer collaboration (1995), pp. 67-81. , Vol. 8, June; Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Searching for MobileNetV3 (2019) Proceedings of the IEEE/CVF international conference on computer vision, pp. 1314-1324; Ioannis, B., Mixed reality constructs a new frontier for maintaining the built environment (2017) Proceedings of the Institution of Civil Engineers - Civil Engineering, 170 (2), p. 53; Jahanshahi, M.R., Masri, S.F., Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures (2012) Automation in Construction, 22, pp. 567-576; Jocher, G., Stoken, A., Borovec, J., Chaurasia, A., Ultralytics/Yolov5: V5.0—YOLOv5-P6 1280 Models, AWS, Supervise.Ly and YouTube integrations (2021); Johnson, M.B., Casey, W., O’Donnell, L., Soden, D., Allec, P., Marshall, A.R., Manual for bridge element inspection (2019), 2nd ed., AASHTO, American Association of State Highway and Transportation Officials; Kamat, V.R., El-Tawil, S., Evaluation of augmented reality for rapid assessment of earthquake-induced building damage (2007) Journal of Computing in Civil Engineering, 21 (5), pp. 303-310; Karaaslan, E., Bagci, U., Catbas, F.N., Artificial intelligence assisted infrastructure assessment using mixed reality systems (2019) Transportation Research Record 2673, no. 12, pp. 413-424; Karaaslan, E., Bagci, U., Catbas, F.N., Attention-guided analysis of infrastructure damage with semi-supervised deep learning (2021) Automation in Construction, 125. , 103634; Karaaslan, E., Catbas, F.N., Bagci, U., A novel decision support system for long term management of bridge networks (2021) Applied Sciences, 11, p. 5928; Knott, P., Carroll, M., Devlin, S., Ciosek, K., Hofmann, K., Dragan, A.D., Evaluating the robustness of collaborative agents (2021), arXiv preprint arXiv:2101.05507; Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., Fieguth, P., A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure (2015) Advanced Engineering Informatics, 29 (2), pp. 196-210; LaLonde, R., Bagci, U., Capsules for object segmentation (2018) arXiv, pp. 1-9. , No. Midl; LaViola, Jr., Joseph, J., Kruijff, E., McMahan, R.P., Bowman, D., Poupyrev, I.P., 3D user interfaces: Theory and practice (2017), Addison-Wesley Professional; Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., SSD: Single shot multibox detector (2016) Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), pp. 21-37. , Vol. 9905 LNCS; Long, J., Shelhamer, E., Darrell, T., Fully convolutional networks for semantic segmentation (2015) In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431-3440; Maierhofer, C., Reinhardt, H.-W., Dobmann, G., Non-destructive evaluation of reinforced concrete structures (2010), 1. , Woodhead, Boca Raton; Oxford: CRC Press; The leader in mixed reality technology | HoloLens Microsoft; Mihelj, M., Novak, D., Beguš, S., Virtual reality technology and applications (2014), Springer; Milgram, P., Kishino, F., A taxonomy of mixed reality visual displays (1994) IEICE Transactions on Information and Systems, 77 (12), pp. 1321-1329; Moreu, F., Bleck, B., Vemuganti, S., Rogers, D., Mascarenas, D., Augmented reality tools for enhanced structural inspection (2017) Structural Health Monitoring 2017, pp. 3124-3130; Rashidi, M., Samali, B., Sharafi, P., A new model for bridge management: Part B: Decision support system for remediation planning (2016) Australian Journal of Civil Engineering, 14 (1), pp. 46-53; Ronneberger, O., Fischer, P., Brox, T., U-net: Convolutional networks for biomedical image segmentation (2015) International conference on medical image computing and computer-assisted intervention, pp. 234-241. , Cham: Springer; Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition (2014), arXiv preprint arXiv:1409.1556; Tan, M., Pang, R., Le, Q.V., EfficientDet: Scalable and efficient object detection (2020) Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10781-10790; Game engine, tools and multiplatform Unity Technologies; Wada, K., Labelme: Image Polygonal Annotation with Python ; Xiong, Y., Liu, H., Gupta, S., Akin, B., Bender, G., Wang, Y., MobileDets: Searching for object detection architectures for mobile accelerators (2021) Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3825-3834; Yokoyama, S., Matsumoto, T., Development of an automatic detector of cracks in concrete using machine learning (2017) Procedia Engineering, 171, pp. 1250-1255; Zaurin, R., Khuc, T., Catbas, F.N., Asce, F., Hybrid sensor-camera monitoring for damage detection: Case study of a real bridge (2015) Journal of Bridge Engineering, 21 (6), pp. 1-27; Zhang, Q., Barri, K., Babanajad, S.K., Alavi, A.H., Real-time detection of cracks on concrete bridge decks using deep learning in the frequency domain (2020) Engineering; Zhu, X., Goldberg, A.B., Introduction to semi-supervised learning (2009) Synthesis Lectures on Artificial Intelligence and Machine Learning, 3 (1), pp. 1-130",,,,"Elsevier",,,,,,9780128177846; 9780128177853,,,"English","The Rise of Smart Cities: Advanced Structural Sens. and Monitoring Systems",Book Chapter,"Final","",Scopus,2-s2.0-85137578880
"Ramonell C., Chacón R.","57226484386;22978642900;","Towards Automated Pipelines for Processing Load Test Data on a HS Railway Bridge in Spain using a Digital Twin",2022,"Proceedings of the International Symposium on Automation and Robotics in Construction","2022-July",,,"231","237",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137109993&partnerID=40&md5=1c4ede63e282754a00c37bfb475526c3","Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya, Spain","Ramonell, C., Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya, Spain; Chacón, R., Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya, Spain","This document presents an automated pipeline to process sensor-based data produced during load tests on digitally twinned HS railway bridges. The research is developed within the frame of the H2020 European project ASHVIN, related to Assistants for Healthy, Safe, and Productive Virtual Construction, Design, Operation & Maintenance using Digital Twins. The pipeline is developed within a digital twin application based on event-driven microservices, which integrates the ASHVIN IoT platform, the IFC building information model and an array of services dedicated to automating processes performed during the operation stage of structural assets. A load test carried out on a bridge located on a HS railway in Spain is used as a demonstrator. © 2022 International Association on Automation and Robotics in Construction.","Automated Pipeline; BIM; Data processing; Digital Twin; Event-based Microservice Architecture; IoT; Load Tests","Automation; Data handling; Internet of things; Load testing; Railroads; Automated pipeline; BIM; Event-based; Event-based microservice architecture; IoT; Load test; Process sensor; Processing load; Railway bridges; Test data; Pipelines",,,,,"Horizon 2020 Framework Programme, H2020: 958161","All authors acknowledge the funding of ASHVIN, ""Assistants for Healthy, Safe, and Productive Virtual Construction Design, Operation & Maintenance using a Digital Twin"" an H2020 project under agreement 958161. DEBES INCLUIR A LA AGAUR",,,,,,,,,,"Gha, A., Building Information Modelling (BIM) uptake: Clear benefits, understanding its implementation, risks, and challenges (2017) Renewable and Sustainable Energy Reviews, 75, pp. 1046-1053; Gao, X., Pishdad-Bozorgi, P., BIM-enabled facilities operation and maintenance: A review (2019) Advanced engineering informatics, 39, pp. 227-247; Colakovi, A., Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues (2018) Computer Networks, 144, pp. 17-39; buildingSMART, , http://www.buildingsmart.org/, Accessed: 7/02/2022; Borrman, A., The IFC-Bridge project- Extending the IFC standard to enable high-quality exchange of bridge information models (2019) En Proceedings of the 2019 European Conference on Computing in Construction, pp. 377-386. , Chania, Greece; Desogus, G., Bim and iot sensors integration: A framework for consumption and indoor conditions data monitoring of existing buildings (2021) Sustainability, 13 (8), p. 4496; Quinn, C., Building automation system-BIM integration using a linked data structure (2020) Automation in Construction, 118, p. 103257; Moretti, N., An openbim approach to iot integration with incomplete as-built data (2020) Applied Sciences, 10 (22), p. 8287; Tang, S., A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends (2019) Automation in Construction, 101, pp. 127-139; Deng, M., From BIM to digital twins: A systematic review of the evolution of intelligent building representations in the AEC-FM industry (2021) Journal of Information Technology in Construction (ITcon), 26 (5), pp. 58-83; Khriji, S., Design and implementation of a cloud-based event-driven architecture for real-time data processing in wireless sensor networks (2021) The Journal of Supercomputing, pp. 1-28; http://ifcopenshell.org/, On-line: Accessed: 22/02/2022; Van Rossum, G., Drake, F. L., (1995) Python reference manual, , Centrum voor Wiskunde en Informatica Amsterdam; Bezanson, J, Julia: A fresh approach to numerical computing (2017) SIAM Review, 59 (1), pp. 65-98; Scholl, S., Fourier, Gabor, Morlet or Wigner: Comparison of Time-Frequency Transforms, , Signal processing Cornell University; Ghalishooyan, M., Shooshtari, A., Operational modal analysis techniques and their theoretical and practical aspects: A comprehensive review and introduction (2015) 6th International Operational Modal Analysis Conference IOMAC 2015",,,"Autodesk;Camara Colombiana de la Construccion (CAMACOL);Construsoft;MCad - Training and Consulting","International Association for Automation and Robotics in Construction (IAARC)","39th International Symposium on Automation and Robotics in Construction, ISARC 2022","13 July 2022 through 15 July 2022",,181877,24135844,9789526952420,,,"English","Proc. Int. Symp. Autom. Robot. Constr.",Conference Paper,"Final","",Scopus,2-s2.0-85137109993
"Giorgadze I.M., Vahdatikhaki F., Voordijk J.H.","57870101400;36474119300;15063651000;","Conceptual Modeling of Lifecycle Digital Twin Architecture for Bridges: A Data Structure Approach",2022,"Proceedings of the International Symposium on Automation and Robotics in Construction","2022-July",,,"199","206",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137087198&partnerID=40&md5=6c46bacaaea1e49c698754e43223cec3","Department of Construction Management & Engineering, University of Twente, Netherlands","Giorgadze, I.M., Department of Construction Management & Engineering, University of Twente, Netherlands; Vahdatikhaki, F., Department of Construction Management & Engineering, University of Twente, Netherlands; Voordijk, J.H., Department of Construction Management & Engineering, University of Twente, Netherlands","The concept of Digital Twin (DT) has emerged in recent years to facilitate the use of Building Information Modeling during the entire projects' lifecycle. In the DT concept, cyber-physical system theory is utilized to collect condition data about an existing asset and then integrate this data into the digital model. The major limitation though is that the current scope of DT is limited to the operation and maintenance phase. Nevertheless, the DT concept can be extended to the entire lifecycle of the asset if the relevant sensory and non-sensory data are incorporated into the digital model in an automated and systematic way. However, in the current literature, there is no clear insight about such a holistic and life-cycle DT concept for infrastructure projects. Especially, there is very little understanding about how various sensory and non-sensory data from construction and operation phases can be seamlessly integrated into the 3D BIM models. Therefore, this research aims to develop a conceptual model for the architecture of Lifecycle DT (LDT) focusing on bridges. To this end, an ontological modeling approach is adopted. The proposed ontology is validated through a workshop session where domain experts assessed the results with respect to some competency questions. The outcome of the session indicated that the proposed ontology scored sufficiently in all the criteria and succeeded in satisfying the information needs of the LDT. Overall, the proposed model offers an insight into a lifecycle modeling practice as well as automated data incorporation, enabling a smooth transition towards an upgraded modeling practice. © 2022 International Association on Automation and Robotics in Construction.","Bridge information modelling; Digital twin; Lifecycle digital twin; Ontological modelling","Architectural design; Bridges; Embedded systems; Information theory; Ontology; Robotics; 'current; Bridge information modeling; Building Information Modelling; Conceptual model; Digital modeling; Information Modeling; Lifecycle digital twin; Ontological modeling; Ontology's; Sensory data; Life cycle",,,,,,,,,,,,,,,,"White paper: National digital twin | Bits & Pieces, , https://global.royalhaskoningdhv.com/digital/resources/publications/national-digital-twin, (accessed Feb. 14, 2022); Borangiu, T., Trentesaux, D., Leitão, P., Boggino, A. G., Botti, V., Studies in Computational Intelligence 853 Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future, , http://www.springer.com/series/7092, [Online]. Available; Stojanovic, V., Trapp, M., Richter, R., Hagedorn, B., Dollner5, J., Semantic Enrichment of Indoor Point Clouds An Overview of Progress towards Digital Twinning; Işikdaǧ, Ü., Enhanced Building Information Models Using IoT Services and Integration Patterns; (1959) Wulfsberg, 1. interdisziplinäre Konferenz zur Zukunft der Wertschöpfung Konferenzband, , J; Becerik-Gerber, B., Jazizadeh, F., Li, N., Calis, G., Application Areas and Data Requirements for BIM-Enabled Facilities Management (2012) Journal of Construction Engineering and Management, 138 (3), pp. 431-442. , Mar; Chen, J., Bulbul, T., Taylor, J. E., Olgun, G., A Case Study of Embedding Real Time Infrastructure Sensor Data to BIM; Glaessgen, E. H., Stargel, D. S., (2012) The digital twin paradigm for future NASA and U.S. Air force vehicles; Haag, S., Anderl, R., Digital twin - Proof of concept (2018) Manufacturing Letters, 15, pp. 64-66. , Jan; Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F., Digital twin-driven product design, manufacturing and service with big data (2018) International Journal of Advanced Manufacturing Technology, 94 (9-12), pp. 3563-3576. , Feb; Opoku, D.-G. J., Perera, S., Osei-Kyei, R., Rashidi, M., Famakinwa, T., Bamdad, K., Drivers for Digital Twin Adoption in the Construction Industry: A Systematic Literature Review (2022) Buildings, 12 (2), p. 113. , Jan; Opoku, D. G. J., Perera, S., Osei-Kyei, R., Rashidi, M., Digital twin application in the construction industry: A literature review (2021) Journal of Building Engineering, 40. , Elsevier Ltd, Aug. 01; Gervasio, H., Dimova, S., Pinto, A., Benchmarking the life-cycle environmental performance of buildings (2018) Sustainability (Switzerland), 10 (5). , May; Hu, W., Zhang, T., Deng, X., Liu, Z., Tan, J., Digital twin: a state-of-the-art review of its enabling technologies, applications and challenges (2021) Journal of Intelligent Manufacturing and Special Equipment, 2 (1), pp. 1-34. , Aug; Jiang, F., Ma, L., Broyd, T., Chen, K., Digital twin and its implementations in the civil engineering sector (2021) Automation in Construction, 130. , Elsevier B.V., Oct. 01; Vahdatikhaki, F., el Ammari, K., Langroodi, A. K., Miller, S., Hammad, A., Doree, A., Beyond data visualization: A context-realistic construction equipment training simulators (2019) Automation in Construction, 106. , Oct; Vahdatikhaki, F., (2015) TOWARDS SMART EARTHWORK SITES USING LOCATIONBASED GUIDANCE AND MULTI-AGENT SYSTEMS; Raad, J., Cruz, C., Cruz, C. A., (2015) A Survey on Ontology Evaluation Methods; Delir Haghighi, P., Burstein, F., Zaslavsky, A., Arbon, P., Development and evaluation of ontology for intelligent decision support in medical emergency management for mass gatherings (2013) Decision Support Systems, 54 (2), pp. 1192-1204. , Jan",,,"Autodesk;Camara Colombiana de la Construccion (CAMACOL);Construsoft;MCad - Training and Consulting","International Association for Automation and Robotics in Construction (IAARC)","39th International Symposium on Automation and Robotics in Construction, ISARC 2022","13 July 2022 through 15 July 2022",,181877,24135844,9789526952420,,,"English","Proc. Int. Symp. Autom. Robot. Constr.",Conference Paper,"Final","",Scopus,2-s2.0-85137087198
"Neumann T., Symalla F., Strunk T., Feidai A., Kaiser S., Friederich P., Wenzel W.","56854797300;55875296100;28568069200;57836449500;57208442294;56363217900;57204318540;","Bottom-up oled development by virtual design: Systematic elimination of performance bottlenecks using a microscopic simulation approach",2022,"Digest of Technical Papers - SID International Symposium","53","1",,"322","325",,,"10.1002/sdtp.15485","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135636355&doi=10.1002%2fsdtp.15485&partnerID=40&md5=f387de2092d6b3bc6afd13f854af10f6","Nanomatch GmbH, Karlsruhe, Germany; Karlsruhe Institute of Technology, Karlsruhe, Germany","Neumann, T., Nanomatch GmbH, Karlsruhe, Germany; Symalla, F., Nanomatch GmbH, Karlsruhe, Germany; Strunk, T., Nanomatch GmbH, Karlsruhe, Germany; Feidai, A., Nanomatch GmbH, Karlsruhe, Germany, Karlsruhe Institute of Technology, Karlsruhe, Germany; Kaiser, S., Karlsruhe Institute of Technology, Karlsruhe, Germany; Friederich, P., Karlsruhe Institute of Technology, Karlsruhe, Germany; Wenzel, W., Karlsruhe Institute of Technology, Karlsruhe, Germany","The gap between computational chemistry and parametric device simulations limits the potentially immense impact of computer models on OLED R&D. We present a review on a bottom-up multiscale modeling approach to bridge this gap and systematically eliminate performance bottlenecks by virtual design. In several case studies we demonstrate how microscopic simulations can support experimental R&D by identifying fundamental reasons for performance bottlenecks, and by deriving strategies for their elimination. © 2022. John Wiley and Sons Inc. AIAA. All rights reserved.","Blue OLED; Digital Twin; Multiscale Simulation; Virtual Design","Computational chemistry; Blue OLEDs; Bottom up; Computer models; Device simulations; Microscopic simulation; Multi-scale simulation; Multiscale modelling approach; Performance bottlenecks; Simulation approach; Virtual design; Organic light emitting diodes (OLED)",,,,,"Baden-Württemberg Stiftung, BWS; Deutsche Forschungsgemeinschaft, DFG: GRK2450","The authors acknowledge support by the state of Baden-Württemberg through bwHPC. WW acknowledges funding from the Deutsche Forschungsgemeinschaft in the research and training group GRK2450. SK received funding by the High-Performance Computing 2 program of the Baden-Württemberg Stiftung (Project MSMEE).",,,,,,,,,,"Friederich, P., Fediai, A., Kaiser, S., Konrad, M., Jung, N., Wenzel, W., Organic Semiconductors: Toward Design of Novel Materials for Organic Electronics (Adv. Mater. 26/2019), , [Internet]. Vol. 31, Advanced Materials. 2019. p. 1970188. Available from: http://dx.doi.org/10.1002/adma.201970188; Liang, X., Tu, Z.-L., Zheng, Y.-X., Thermally Activated Delayed Fluorescence Materials: Towards Realization of High Efficiency through Strategic Small Molecular Design (2019) Chemistry, 25 (22), pp. 5623-5642. , Apr 17; Mondal, A., Paterson, L., Cho, J., Lin, K.-H., van der Zee, B., G-Jah, W., Molecular library of OLED host materials—Evaluating the multiscale simulation workflow (2021) Chem Phys Rev, 2 (3). , Sep; 4. Massé A, Friederich P, Symalla F, Liu F, Nitsche R, Coehoorn R, et al. Ab initiocharge-carrier mobility model for amorphous molecular semiconductors. Phys Rev B Condens Matter [Internet]. 2016 May 19;93(19). Available from: https://link.aps.org/doi/10.1103/PhysRevB.93.195209; Lee, J.-H., Chen, C.-H., Lee, P.-H., Lin, H.-Y., Leung, M.-K., Chiu, T.-L., Blue organic light-emitting diodes: Current status, challenges, and future outlook (2019) J Mater Chem, 7 (20), pp. 5874-5888. , May 23; Wang, Y., Yun, J.H., Wang, L., Lee, J.Y., High triplet energy hosts for blue organic lightemitting diodes (2021) Adv Funct Mater, 31 (12). , Mar; Nam, S., Kim, J.W., Bae, H.J., Maruyama, Y.M., Jeong, D., Kim, J., Improved Efficiency and Lifetime of Deep-Blue Hyperfluorescent Organic Light-Emitting Diode using Pt(II) Complex as Phosphorescent Sensitizer (2021) Adv Sci, 8 (16). , Aug; Gómez-Bombarelli, R., Aguilera-Iparraguirre, J., Hirzel, T.D., Duvenaud, D., Maclaurin, D., Blood-Forsythe, M.A., Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach (2016) Nat Mater, 15 (10), pp. 1120-1127. , Oct; Ruhstaller, B., Beierlein, T., Riel, H., Karg, S., Scott, J.C., Riess, W., Simulating electronic and optical processes in multilayer organic light-emitting devices (2003) IEEE J Sel Top Quantum Electron, 9 (3), pp. 723-731. , May; Friederich, P., Häse, F., Proppe, J., Aspuru-Guzik, A., Machine-learned potentials for next-generation matter simulations (2021) Nat Mater, 20 (6), pp. 750-761. , May 27; Friederich, P., Symalla, F., Meded, V., Neumann, T., Wenzel, W., Ab Initio Treatment of Disorder Effects in Amorphous Organic Materials: Toward Parameter Free Materials Simulation (2014) J Chem Theory Comput, 10 (9), pp. 3720-3725. , Sep 9; Armleder, J., Strunk, T., Symalla, F., Friederich, P., Peña, J.E.O., Neumann, T., Computing Charging and Polarization Energies of Small Organic Molecules Embedded into Amorphous Materials with Quantum Accuracy (2021) Journal of Chemical Theory and Computation, 17, pp. 3727-3738. , http://dx.doi.org/10.1021/acs.jctc.1c00036, Internet]. Vol. , . p. , Available from; Neumann, T., Danilov, D., Lennartz, C., Wenzel, W., Modeling disordered morphologies in organic semiconductors (2013) J Comput Chem, 34 (31), pp. 2716-2725. , Dec 5; Symalla, F., Fediai, A., Armleder, J., Kaiser, S., Strunk, T., Neumann, T., 433: Abinitio simulation of doped injection layers (2020) Dig Tech Papers, 51 (1), pp. 630-633. , Aug; Kaiser, S., Neumann, T., Symalla, F., Schlöder, T., Fediai, A., Friederich, P., De Novo Calculation of the Charge Carrier Mobility in Amorphous Small Molecule Organic Semiconductors (2021) Front Chem, 9. , Dec 24; Kaiser, S., Kotadiya, N.B., Rohloff, R., Fediai, A., Symalla, F., Neumann, T., De Novo Simulation of Charge Transport through Organic Single-Carrier Devices (2021) J Chem Theory Comput, 17 (10), pp. 6416-6422. , Oct 12; Rühle, V., Lukyanov, A., May, F., Schrader, M., Vehoff, T., Kirkpatrick, J., Microscopic simulations of charge transport in disordered organic semiconductors (2011) J Chem Theory Comput, 7 (10), pp. 3335-3345. , Oct 11; Friederich, P., Fediai, A., Kaiser, S., Konrad, M., Jung, N., Wenzel, W., Toward design of novel materials for organic electronics (2019) Adv Mater, 31 (26). , Jun; 19. Reineke S, Walzer K, Leo K. Triplet-exciton quenching in organic phosphorescent light-emitting diodes with Ir-based emitters [Internet]. Vol. 75, Physical Review B. 2007. Available from: http://dx.doi.org/10.1103/physrevb.75.125328; Salzmann, I., Heimel, G., Oehzelt, M., Winkler, S., Koch, N., Molecular Electrical Doping of Organic Semiconductors: Fundamental Mechanisms and Emerging Dopant Design Rules (2016) Acc Chem Res, 49 (3), pp. 370-378. , Mar 15; Lüssem, B., Keum, C.-M., Kasemann, D., Naab, B., Bao, Z., Leo, K., Doped Organic Transistors (2016) Chem Rev, 116 (22), pp. 13714-13751. , Nov 23; Fediai, A., Emering, A., Symalla, F., Wenzel, W., Disorder-driven doping activation in organic semiconductors (2020) Phys Chem Chem Phys, 22 (18), pp. 10256-10264. , May 13; Paterson, L., May, F., Andrienko, D., Computer aided design of stable and efficient OLEDs (2020) J Appl Phys, 128 (16). , Oct 28; Friederich, P., Meded, V., Poschlad, A., Neumann, T., Rodin, V., Stehr, V., Molecular origin of the charge carrier mobility in small molecule organic semiconductors (2016) Adv Funct Mater, 26 (31), pp. 5757-5763. , Aug; Symalla, F., Heidrich, S., Friederich, P., Strunk, T., Neumann, T., Minami, D., Multiscale simulation of photoluminescence quenching in phosphorescent OLED materials (2020) Adv Theory Simul, 3 (4). , Apr; Symalla, F., Heidrich, S., Kubillus, M., Strunk, T., Neumann, T., Wenzel, W., 194: Boosting OLED performance with ab initio modeling of rolloff and quenching processes (2019) Dig Tech Papers, 50 (1), pp. 259-262. , Jun; Symalla, F., Friederich, P., Massé, A., Meded, V., Coehoorn, R., Bobbert, P., Charge Transport by Superexchange in Molecular Host-Guest Systems (2016) Phys Rev Lett, 117 (27). , Dec 30; Friederich, P., Gómez, V., Sprau, C., Meded, V., Strunk, T., Jenne, M., Rational In Silico Design of an Organic Semiconductor with Improved Electron Mobility (2017) Adv Mater, 29 (43). , http://dx.doi.org/10.1002/adma.201703505, Internet, Nov, Available from; Inanlou, S., Cortés-Mejía, R., Özdemir, A.D., Höfener, S., Klopper, W., Wenzel, W., Understanding excited state properties of host materials in OLEDs: Simulation of absorption spectrum of amorphous 4,4-bis(carbazol-9-yl)-2,2-biphenyl (CBP) (2022) Phys Chem Chem Phys, 24 (7), pp. 4576-4587. , Feb 16; Fediai, A., Symalla, F., Friederich, P., Wenzel, W., Disorder compensation controls doping efficiency in organic semiconductors (2019) Nat Commun, 10 (1). , Oct 7; Friederich, P., Coehoorn, R., Wenzel, W., Molecular origin of the anisotropic dye orientation in emissive layers of organic light emitting diodes (2017) Chem Mater, 29 (21), pp. 9528-9535. , Nov 14; Friederich, P., Rodin, V., von Wrochem, F., Wenzel, W., Built-In Potentials Induced by Molecular Order in Amorphous Organic Thin Films (2018) ACS Appl Mater Interfaces, 10 (2), pp. 1881-1887. , Jan 17; Friederich, P., Meded, V., Symalla, F., Elstner, M., Wenzel, W., QM/QM approach to model energy disorder in amorphous organic semiconductors (2015) J Chem Theory Comput, 11 (2), pp. 560-567. , Feb 10; Özdemir, A.D., Kaiser, S., Neumann, T., Symalla, F., Wenzel, W., Systematic kMC Study of Doped Hole Injection Layers in Organic Electronics (2021) Front Chem, 9",,"Donelan J.",,"John Wiley and Sons Inc","59th International Symposium, Seminar and Exhibition, Display Week 2022","8 May 2022 through 13 May 2022",,280799,0097966X,,,,"English","Dig. Tech. Pap. SID Int. Symp.",Conference Paper,"Final","",Scopus,2-s2.0-85135636355
"Taraben J., Helmrich M., Morgenthal G.","57199260229;57203918886;16304701500;","Bridge Condition Assessment Based on Image Data and Digital Twins",2022,"IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report",,,,"735","742",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133552961&partnerID=40&md5=8bc442a615a8cefa1506c990ecca6a42","Bauhaus-Universität Weimar, Institute of Structural Engineering, Weimar, Germany","Taraben, J., Bauhaus-Universität Weimar, Institute of Structural Engineering, Weimar, Germany; Helmrich, M., Bauhaus-Universität Weimar, Institute of Structural Engineering, Weimar, Germany; Morgenthal, G., Bauhaus-Universität Weimar, Institute of Structural Engineering, Weimar, Germany","Many different approaches using modern digital technologies were recently developed to support engineers with the acquisition of visual inspection data, such as the usage of small unmanned aircraft systems (UAS) equipped with high-quality cameras. The images obtained are used, amongst others, for photogrammetric reconstruction methods or image-based anomaly detection, which leads to a high potential of automation in condition assessment, reducing time and costs. This article presents approaches for the integration of image-based inspection data sets into an automated workflow towards condition rating of damaged infrastructures. To this end, it is shown how 3D annotations are combined with information from a digital twin, such that further properties are assigned to the detected structural anomalies, in order to enrich the digital twin. Finally, the proposed methods are applied to a case study to show the feasibility in a practical use case. © 2022 IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report. All rights reserved.","bridge inspection; condition rating; damage modelling; digital twin","Damage detection; Photogrammetry; Unmanned aerial vehicles (UAV); Bridge inspection; Condition; Condition assessments; Condition rating; Damage modelling; Digital technologies; Image data; Image-based; Inspection datum; Visual inspection; Anomaly detection",,,,,"1314N 657; Bundesministerium für Bildung und Forschung, BMBF: 03WK H01B","The authors would like to acknowledge the support of the Federal Ministry of Education and Research of erG many to the funding of the project s ScanSim (N o. 03WK H01B) and AISTEC (oN . 1314N 657).",,,,,,,,,,"Figueiredo, E., Moldovan, I., Marques, M. B., (2013) Condition Assessment of Bridges: Past, Present, and Future. A Complementary Approach, , Universidade Católica Editora. ISBN: 978-972-54-0402-7; Morgenthal, G., Hallermann, N., Kersten, J., Taraben, J., Debus, P., Helmrich, M., Rodehorst, V., Framework for automated UAS-based structural condition assessment of bridges (2019) Automation in Construction, 97, pp. 77-95. , https://doi.org/10.1016/j.autcon.2018.10.006; Wenzel, H., Pakrashi, V., Guidelines and Recommendations from COST TU 1406 (2019) IABSE Symposium 2019: Towards a Resilient Built Environment-Risk and Asset Management, pp. 818-824. , Guimarães, Portugal, 27-29 March 2019; Chase, S. B., Adu-Gyamfi, Y., Aktan, A. E., Minaie, E., (2016) Synthesis of national and international methodologies used for bridge health indices, , Report No. FHWA-HRT-15-081; Strauss, A., Mandić Ivanković, A., Performance indicators for road bridges-categorization overview (2016) TU1406-Quality specifications for roadway bridges, standardization at a European level; Hajdin, R., Kušar, M., Mašović, S., Linneberg, P., Amado, J., Tanasić, N., Establishment of a Quality Control Plan (2018) TU1406-Quality specifications for roadway bridges, standardization at a European level, , ISBN: 978-86-7518-200-9; Debus, P., Rodehorst, V., Multi-scale Flight Path Planning for UAS Building Inspection (2020) ICCCBE 2020. 18th International Conference on Computing in Civil and Building Engineering, pp. 1069-1085. , https://doi.org/10.1007/978-3-030-51295-8_74, Toledo Santos E., Scheer S., editors. Aug 18-20; Sao Paulo, Brazil. Cham (CH): Springer; 2021; Benz, C., Debus, P., Ha, H., Rodehorst, V., Crack Segmentation on UAS-based Imagery using Transfer Learning (2019) International Conference on Image and Vision Computing New Zealand (IVCNZ), pp. 1-6. , https://doi.org/10.1109/IVCNZ48456.2019.8960998, Dec 2-4; Dunedin, New Zealand. IEEE, 2019; Taraben, J., Helmrich, M., Methode zur automatisierten Verortung von Inspektionsdaten aus bildbasierten Bauwerksprüfungen. 30 (2018), https://doi.org/10.25643/bauhausuniversitaet, Forum Bauinformatik Bauhaus-Universität Weimar; Taraben, J., Morgenthal, G., Methods for the Automated Assignment and Comparison of Building Damage Geometries (2021) Advanced Engineering Informatics, 47, p. 101186. , https://doi.org/10.1016/j.aei.2020.101186; (2017) Richtlinie zur einheitlichen Erfassung, Bewertung, Aufzeichnung und Auswertung von Ergebnissen der Bauwerksprüfungen (RI-EBW-PRÜF) nach DIN 1076, , BASt","Taraben, J.; Bauhaus-Universität Weimar, Germany; email: jakob.taraben@uni-weimar.de
Helmrich, M.; Bauhaus-Universität Weimar, Germany; email: marcel.helmrich@uni-weimar.de",,"ALLPlan;BBR VT International Ltd;Ceska asociace ocelovych konstrukci (Czech Constructional Steelwork Association);et al.;IDEA Statica;REDAELLI TECNA S.p.A.","International Association for Bridge and Structural Engineering (IABSE)","IABSE Symposium Prague 2022: Challenges for Existing and Oncoming Structures","25 May 2022 through 27 May 2022",,180214,,9783857481833,,,"English","IABSE Symp. Prague,: Challenges Exist. Oncoming Struct. - Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85133552961
"Wenzel B., Möller E., Schmid B., Weber C., Morgenthal G.","57658564700;55546498500;57660764700;57205890321;16304701500;","The New Little Belt Bridge - the role of the physical model and it's digital twin",2022,"IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report",,,,"702","709",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133551145&partnerID=40&md5=40973c1e31f6518277f1ce48cbc3e9bf","Karlsruhe University of Applied Sciences, Karlsruhe, Germany; University of Innsbruck, Innsbruck, Austria; Bauhaus University Weimar, Weimar, Germany","Wenzel, B., Karlsruhe University of Applied Sciences, Karlsruhe, Germany; Möller, E., Karlsruhe University of Applied Sciences, Karlsruhe, Germany; Schmid, B., University of Innsbruck, Innsbruck, Austria; Weber, C., University of Innsbruck, Innsbruck, Austria; Morgenthal, G., Bauhaus University Weimar, Weimar, Germany","The New Little Belt Bridge, designed by the consulting engineers Christian Ostenfeld and Wriborg Jønson, was to be Denmark's first suspension bridge. The construction of this Danish piece of engineering history took place between 1965 and 1970 and could only succeed with the help of tests on physical models. Most of the physical models have disappeared, only the three-dimensional dynamic model of the bridge has survived. This paper looks at the question which role the surviving and other models played in the planning process. The model still existing is a store of knowledge of engineering practice and shall be recorded and evaluated as one of the last witnesses of model statics. Therefore, the authors will create a digital twin of the model to examine aspects like boundary conditions, structural parameters of all bridge components and the well-documented research results. © 2022 IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report. All rights reserved.","digital twin; New Lillebælt Bridge; New Little Belt Bridge; physical model; static and dynamic model tests; structural modelling; suspension bridge","Bridges; Dynamic models; Christians; Consulting engineers; Denmark; Dynamic model test; New lillebaelt bridge; New little belt bridge; Physical modelling; Static and dynamic model test; Static and dynamic modeling; Structural modeling; Dynamics",,,,,,,,,,,,,,,,"https://cowi.b-cdn.net/-/media/srenandersen-little-belt-old-schoolpodcast.mp3?la=en, [place unknown]: [publisher unknown]; [date unknown] [cited 2021 September 01]; Ostenfeld, C, Frandsen, AG, Jessen, JJ, Haas, G., (1970) Motorway Bridge across Lillebælt: Publication III Design and Construction of the Bridge, , Copenhagen: Chr. Ostenfeld & W. Jønson Consulting Engineers; Weber, C., (2011) Fritz Leonhardt „Leichtbau - eine Forderung unserer Zeit. Anregungen für den Hoch- und Brückenbau“: Zur Einführung baukonstruktiver Prizipien im Leichtbau in den 1930er- und 1940er-Jahren, , Karlsruhe: KIT Scientific Publishing; Mehlhorn, G, (2010) Handbuch Brücken: Entwerfen, Konstruieren, Berechnen, Bauen und Erhalten, , editor. 2nd edition. Berlin, Heidelberg: Springer-Verlag; Leonhardt, F., Moderne Stahl- und Brückenbau (1965) Schweizerische Bauzeitung, 83 (24), pp. 421-427. , June 17; Leonhardt, F., Aerodynamisch stabile Hängebrücken für große Spannweiten (1964) IVBH Kongressbericht, 7 (1), pp. 155-167. , March; Leonhardt, F, Zellner, W., Vergleiche zwischen Hängebrücken und Schrägkabelbrücken für Spannweiten über 600 m (1972) IVBH Abhandlungen, 32 (1), pp. 127-165. , July; Ostenfeld, C, Haas, G, Frandsen, AG., (1970) Motorway Bridge across Lillebælt: Publication XI Model Tests for the Superstructure of the Suspension Bridge, , Copenhagen: Chr. Ostenfeld & W. Jønson Consulting Engineers; Leonhardt, F., (1984) Baumeister in einer umwälzenden Zeit: Erinnerungen, , Stuttgart: Deutsche Verlags-Anstalt; Sirtl, C., (2012) Schwingungsverhalten von Hängebrücken - Erprobung neuartiger Messtechnik am Experimentalmodell: Lillebælt Brücke - Dänemark, , Weimar: [unpublished]; Nedashkovskiy, AL, Azheganova, EK, Gupta, RO, Teyhssier, AR., (2020) Application of digital twin in industry 4.0, , ResearchGate; Hitesh, HI, Shreya, KE, Smruti, CH, Hrishikesh Bharadwa, CH., (2020) Industry 4.0: Digital Twin and its Industrial Applications, , ResearchGate; Dankert, J, Dankert, H., (2013) Technische Mechanik: Statik, Festigkeitslehre, Kinematik/Kinetik, , Wiesbaden: Springer Verlag","Wenzel, B.; Karlsruhe University of Applied SciencesGermany; email: baris.wenzel@h-ka.de",,"ALLPlan;BBR VT International Ltd;Ceska asociace ocelovych konstrukci (Czech Constructional Steelwork Association);et al.;IDEA Statica;REDAELLI TECNA S.p.A.","International Association for Bridge and Structural Engineering (IABSE)","IABSE Symposium Prague 2022: Challenges for Existing and Oncoming Structures","25 May 2022 through 27 May 2022",,180214,,9783857481833,,,"English","IABSE Symp. Prague,: Challenges Exist. Oncoming Struct. - Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85133551145
"Červenka J., Rymeš J., Pukl R.","7103036677;57192986869;6507525719;","Advanced modelling of concrete bridges",2022,"IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report",,,,"1321","1328",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133548836&partnerID=40&md5=71c795c78912fd5fbe081bba01f07b21","Červenka Consulting, s.r.o., Prague, Czech Republic","Červenka, J., Červenka Consulting, s.r.o., Prague, Czech Republic; Rymeš, J., Červenka Consulting, s.r.o., Prague, Czech Republic; Pukl, R., Červenka Consulting, s.r.o., Prague, Czech Republic","The combination of non-linear finite element analysis with structural monitoring can considerably improve the prognosis of bridge behaviour, deterioration, and damage. Recently, a digital twin concept is utilized in which a digital replica of a real structure is developed. Based on the data obtained from the monitoring system installed on the Wonka bridge, Czech Republic, a computational model was calibrated. Then, advanced deterioration models accounting for chloride-induced reinforcement corrosion were applied to assess the long-term development of the structure's load-bearing capacity. It is shown that the proposed integrated system can be used as a tool for the ageing management of concrete structures ensuring their long-term safe operation. © 2022 IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report. All rights reserved.","ageing management; bridge monitoring; durability assessment; finite element method (FEM); non-linear analysis","Chlorine compounds; Concrete bridges; Concretes; Deterioration; Electrochemical corrosion; Monitoring; Advanced modeling; Ageing management; Bridge monitoring; Digital replicas; Durability assessment; Finite element method; Non-linear analysis; Nonlinear finite element analyses (FEA); Real structure; Structural monitoring; Finite element method",,,,,"Technologická Agentura České Republiky: TF06000016","The durability modelling presented in this study was supported by the Czech Technological Agency under the project TF06000016 “ Advanced system for monitoring, diagnosis and reliability assessment of large-scale concrete infrastructures” .",,,,,,,,,,"(2021) Infrastructure maintenance (indicator), , OECD; Haag, S, Anderl, R., Digital twin - Proof of concept (2018) Manuf Lett, 15, pp. 64-66. , Jan 1; Červenka, V, Jendele, L, Červenka, J., ATENA Program Documentation: Part 1 Theory, p. 360. , Prague; Červenka, J, Červenka, V, Eligehausen, R., Fracture-plastic material model for concrete, application to analysis of powder actuated anchors (1998) Proceedings FRAMCOS, (3), pp. 1107-1116; Červenka, J, Papanikolaou, VK., Three dimensional combined fracture-plastic material model for concrete (2008) Int J Plast, 24 (12), pp. 2192-2220. , Dec 1; Hájková, K, Šmilauer, V, Jendele, L, Červenka, J., Prediction of reinforcement corrosion due to chloride ingress and its effects on serviceability (2018) Eng Struct, 174, pp. 768-777. , (February); Vergoossen, RPH, Wolfert, ARM, Koenders, EAB., Objective risk based structural assessment of existing concrete structures (2017) Life-Cycle Eng Syst Emphas Sustain Civ Infrastruct - 5th Int Symp Life-Cycle Eng IALCCE 2016, pp. 1406-1413. , (February 2017); (2000) Probabilistic performance based durability design of concrete structures: Final technical report of Duracrete project; Luping, T, Boubitsas, D, Utgenannt, P, Abbas, Z., (2018) Chloride Ingress and Reinforcement Corrosion - After 20 years' field exposure in a highway environment, , Borås; Hartt, WH, Lee, S-K., (2018) Corrosion Forecasting and Failure Projection of Post-Tension Tendons in Deficient Cementitious Grout; (2002) Eurocode 1: Actions on structures - Part 1-1: General actions - Densities, self-weight, imposed loads for buildings; González, JA, Andrade, C, Alonso, C, Feliu, S., Comparison of rates of general corrosion and maximum pitting penetration on concrete embedded steel reinforcement (1995) Cem Concr Res, 25 (2), pp. 257-264. , Feb 1; (2012) ISO 9223:2012 Corrosion of metals and alloys - Corrosivity of atmospheres - Classification, determination and estimation, , ISO/TC 156","Červenka, J.; Červenka Consulting, Czech Republic; email: jan.cervenka@cervenka.cz",,"ALLPlan;BBR VT International Ltd;Ceska asociace ocelovych konstrukci (Czech Constructional Steelwork Association);et al.;IDEA Statica;REDAELLI TECNA S.p.A.","International Association for Bridge and Structural Engineering (IABSE)","IABSE Symposium Prague 2022: Challenges for Existing and Oncoming Structures","25 May 2022 through 27 May 2022",,180214,,9783857481833,,,"English","IABSE Symp. Prague,: Challenges Exist. Oncoming Struct. - Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85133548836
"Joye S., De Witt M.","57783644500;57783481000;","Structural Health Monitoring of the Çanakkale Bridge in Turkey, the largest monitoring system for the longer bridge in the world",2022,"IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report",,,,"314","317",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133531808&partnerID=40&md5=d50939d40114ea74530c7b001dbe5c14","Sixense Monitoring, Nanterre, France; Sixense Monitoring, Brussels, Belgium","Joye, S., Sixense Monitoring, Nanterre, France; De Witt, M., Sixense Monitoring, Brussels, Belgium","This bridge is outstanding and so will be its Structural health Monitoring. Designed and installed by Sixense upon the specification of the Client, the system will manage more than 1000 sensors. This monitoring architecture will allow to measure any external event happening on the bridge such as strong, wind, earthquake, lightning, heavy traffic. It will also record all the reaction behavior of the structure such as temperature, displacement, vibration, fatigue stresses. Amongst all the recorded data, dynamic record of such a slender structure is key. The acquisition system provided by Sixense will allow to record, compute, sample, analyses, store and analyse a tremendous quantity of data from all kind of mechanical beaviour of the structure. It will for instance allow rainflow treatment of the stress of the orthotropic slab in order to survey and prevent fatigue failure. But more than simply recording data for visualization and immediate maintenance, the system provided by Sixense will also allow to perform predictive computation of the future behavior of the infrastructure: regression analysis from the available data combined with tuneable environment parameters (temperature, traffic), will allow the owner of the bride to anticipate the behavior of the bridge and plan the maintenance operation far in advance out of critical service activity phases. This will increase the safety of the bridge, save money for the owner and allow this signature bridge to last for a very long time. © 2022 IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report. All rights reserved.","Bridge; Digital twin; Longest; record; Sensors; SHM; Strain Gages; Structural Health Monitoring; Suspension; Turkey; World","Bridges; Fatigue of materials; Regression analysis; Structural health monitoring; Long; Long bridges; Monitoring architecture; Monitoring system; Record; SHM; Strain-gages; Strong winds; Turkey; World; Data visualization",,,,,,,,,,,,,,,,,"Joye, S.; Sixense MonitoringFrance; email: stephane.joye@sixense-group.com",,"ALLPlan;BBR VT International Ltd;Ceska asociace ocelovych konstrukci (Czech Constructional Steelwork Association);et al.;IDEA Statica;REDAELLI TECNA S.p.A.","International Association for Bridge and Structural Engineering (IABSE)","IABSE Symposium Prague 2022: Challenges for Existing and Oncoming Structures","25 May 2022 through 27 May 2022",,180214,,9783857481833,,,"English","IABSE Symp. Prague,: Challenges Exist. Oncoming Struct. - Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85133531808
"Partala E.","57783644400;","Data driven BIM models in bridges",2022,"IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report",,,,"716","719",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133518998&partnerID=40&md5=835dd76fe83543aa89697a92313c25c0","Sweco Structures Ltd, Finland","Partala, E., Sweco Structures Ltd, Finland","Design automation tools are rarely used in a context of existing structures. The methods of modelling can also be harnessed to create digital twins of structures with the help of data from the structural health management systems. With 10 pilot bridges, the workflow was studied and key parameters for the modelling specified. Semi-automated modelling process made it possible to create models from bridges that were designed and built before the BIM era. © 2022 IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report. All rights reserved.","BIM; bridges; data driven design; SHM; structural health management","Architectural design; Automation; Computer aided design; Information management; BIM; Data driven; Data-driven design; Design-automation tools; Existing structure; Health management systems; Method of modeling; SHM; Structural health managements; Work-flows; Bridges",,,,,,,,,,,,,,,,,"Partala, E.; Sweco Structures LtdFinland; email: eetu.partala@sweco.fi",,"ALLPlan;BBR VT International Ltd;Ceska asociace ocelovych konstrukci (Czech Constructional Steelwork Association);et al.;IDEA Statica;REDAELLI TECNA S.p.A.","International Association for Bridge and Structural Engineering (IABSE)","IABSE Symposium Prague 2022: Challenges for Existing and Oncoming Structures","25 May 2022 through 27 May 2022",,180214,,9783857481833,,,"English","IABSE Symp. Prague,: Challenges Exist. Oncoming Struct. - Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85133518998
"Mafipour M.S., Vilgertshofer S., Borrmann A.","57211838747;57188750778;14824718700;","Creating digital twins of existing bridges through AI-based methods",2022,"IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report",,,,"727","734",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133502465&partnerID=40&md5=15d5c21841314c6db45cb7f79797c271","Computational Modeling and Simulation, Technical University of Munich, Germany","Mafipour, M.S., Computational Modeling and Simulation, Technical University of Munich, Germany; Vilgertshofer, S., Computational Modeling and Simulation, Technical University of Munich, Germany; Borrmann, A., Computational Modeling and Simulation, Technical University of Munich, Germany","Bridges require regular inspection and maintenance during their service life, which is costly and time-consuming. Digital twins (DT), which incorporate a geometric-semantic model of an existing bridge, can support the operation and maintenance process. The process of creating such DT models can be based on Point cloud data (PCD), created via photogrammetry or laser scanning. However, the semantic segmentation of PCD and parametric modeling is a challenging process, which is nonetheless necessary to support DT modeling. This paper aims to propose a segmentation method that is the basis for a parametric modeling approach to enable the semi-automatic geometric modeling of bridges from PCD. To this end, metaheuristic algorithms, fuzzy C-mean clustering, and signal processing algorithms are used. The results of this paper show that the scan to BIM process of bridges can be automated to a large extent and provide a model that meets the industry's demand. © 2022 IABSE Symposium Prague, 2022: Challenges for Existing and Oncoming Structures - Report. All rights reserved.","artificial intelligence; bridge; building information modeling; digital twin; fuzzy C-mean clustering; metaheuristic algorithms; parametric modeling; semantic segmentation","Architectural design; Bridges; Clustering algorithms; Parameter estimation; Signal processing; Building Information Modelling; Existing bridge; Fuzzy C-Means clustering; Geometric semantics; Inspection and maintenance; Meta-heuristics algorithms; Parametric models; Point cloud data; Regular inspections; Semantic segmentation; Semantics",,,,,"Bundesministerium für Verkehr und Digitale Infrastruktur, BMVI","The research presented has been performed in the scope of the research project TwinGen. We thank the German Ministry of Transport and Digital Infrastructure (BMVI) for funding this research.","The research presented has been performed in the scope of the research project TwinenG . We thank the Ge rman Ministry of Transport and Digital Infrastructure (BMVI) for funding this research.",,,,,,,,,"Lu, Q., Semi-automatic geometric digital twinning for existing buildings based on images and CAD drawings (2020) Automation in Construction, 115, p. 103183; Pan, Y., Built Environment Digital Twinning (2019) Report of the International Workshop on Built Environment Digital Twinning presented by TUM Institute for Advanced Study and Siemens AG, , Technical University of Munich, Germany; Zhu, Z., German, S., Brilakis, I., Detection of large-scale concrete columns for automated bridge inspection (2010) Automation in construction, 19 (8), pp. 1047-1055; (2015) SeeBridge-Semantic enrichment engine for bridges, p. 77. , Technion, Technion; Bosché, F., The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components (2015) Automation in Construction, 49, pp. 201-213; Adán, A., Scan-to-BIM for 'secondary'building components (2018) Advanced Engineering Informatics, 37, pp. 119-138; Laing, R., Scan to BIM: the development of a clear workflow for the incorporation of point clouds within a BIM environment (2015) WIT Transactions on The Built Environment, 149, pp. 279-289; Rocha, G., A scan-to-BIM methodology applied to heritage buildings (2020) Heritage, 3 (1), pp. 47-67; Lu, R., Brilakis, I., Middleton, C.R., Detection of structural components in point clouds of existing RC bridges (2019) Computer-Aided Civil and Infrastructure Engineering, 34 (3), pp. 191-212; Lee, J.H., Park, J.J., Yoon, H., Automatic bridge design parameter extraction for scan-to-BIM (2020) Applied Sciences, 10 (20), p. 7346; Hu, F., Structure‐aware 3D reconstruction for cable‐stayed bridges: A learning‐based method (2021) Computer‐Aided Civil and Infrastructure Engineering, 36 (1), pp. 89-108; Qin, G., Automated Reconstruction of Parametric BIM for Bridge Based on Terrestrial Laser Scanning Data (2021) Advances in Civil Engineering, 2021; Lee, J.S., Park, J., Ryu, Y.-M., Semantic segmentation of bridge components based on hierarchical point cloud model (2021) Automation in Construction, 130, p. 103847; Yan, Y., Hajjar, J.F., Automated extraction of structural elements in steel girder bridges from laser point clouds (2021) Automation in Construction, 125, p. 103582; Girardet, A., Boton, C., A parametric BIM approach to foster bridge project design and analysis (2021) Automation in Construction, 126, p. 103679; Mafipour, M.S., Vilgertshofer, S., Borrmann, A., Deriving Digital Twin Models of Existing Bridges from Point Cloud Data Using Parametric Models and Metaheuristic Algorithms (2021) Proc. of the EG-ICE Conference 2021; Kennedy, J., Eberhart, R., Particle swarm optimization (1995) Proceedings of ICNN'95-international conference on neural networks, , IEEE","Mafipour, M.S.; Computational Modeling and Simulation, Germany; email: m.saeed.mafipour@tum.de",,"ALLPlan;BBR VT International Ltd;Ceska asociace ocelovych konstrukci (Czech Constructional Steelwork Association);et al.;IDEA Statica;REDAELLI TECNA S.p.A.","International Association for Bridge and Structural Engineering (IABSE)","IABSE Symposium Prague 2022: Challenges for Existing and Oncoming Structures","25 May 2022 through 27 May 2022",,180214,,9783857481833,,,"English","IABSE Symp. Prague,: Challenges Exist. Oncoming Struct. - Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85133502465
"Mozo A., Karamchandani A., Sanz M., Moreno J.I., Pastor A.","24479201000;57713253300;57214241978;35611835100;57210395292;","B5GEMINI: Digital Twin Network for 5G and Beyond",2022,"Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022",,,,"","",,,"10.1109/NOMS54207.2022.9789810","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133161518&doi=10.1109%2fNOMS54207.2022.9789810&partnerID=40&md5=e38779bec78866acb43363fe4705d299","Universidad Politécnica de Madrid, Etsi Sistemas Informáticos, Departamento de Sistemas Informáticos, Spain; Universidad Politécnica de Madrid, Etsi Telecomunicación, Departamento de Ingenieriá de Sistemas Telemáticos, Spain; Telefónica Investigación y Desarrollo, Spain","Mozo, A., Universidad Politécnica de Madrid, Etsi Sistemas Informáticos, Departamento de Sistemas Informáticos, Spain; Karamchandani, A., Universidad Politécnica de Madrid, Etsi Sistemas Informáticos, Departamento de Sistemas Informáticos, Spain; Sanz, M., Universidad Politécnica de Madrid, Etsi Telecomunicación, Departamento de Ingenieriá de Sistemas Telemáticos, Spain; Moreno, J.I., Universidad Politécnica de Madrid, Etsi Telecomunicación, Departamento de Ingenieriá de Sistemas Telemáticos, Spain; Pastor, A., Telefónica Investigación y Desarrollo, Spain","Digital Twin Network (DTN) is a new technology that builds on the concept of Digital Twins (DT) to create a virtual representation of the physical objects of a telecommunications network. DTN bridges physical and virtual spaces to enable coordination and synchronization of physical parts while eliminating the need to directly interact with them. In this work, we present B5GEMINI a DTN for 5G and beyond networks that makes an extensive use of artificial intelligence (AI). First, we present the infrastructural and architectural components that support B5GEMINI. Next, we explore five paradigmatic use cases where AI can leverage B5GEMINI for building new AI-powered applications. Finally, we identify the main components of the AI ecosystem of B5GEMINI. © 2022 IEEE.","artificial intelligence; digital twin; digital twin networks; machine learning; telecommunications","5G mobile communication systems; E-learning; Architectural components; Digital twin network; Machine-learning; Network bridges; Physical objects; Telecommunications networks; TWIN networks; Virtual representations; Virtual spaces; Machine learning",,,,,"Horizon 2020 Framework Programme, H2020: 833685, 871808, INSPIRE-5Gplus","VII. ACKNOWLEDGMENTS This work was supported in part by the Spanish Ministerio de Asuntos Económicos y Transformación Digital UNICO-5G I+D Programme under project B5GEMINI. This work was also partially supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 833685 (SPIDER) and Grant 871808 (INSPIRE-5Gplus).",,,,,,,,,,"Wu, Y., Zhang, K., Zhang, Y., Digital twin networks: A survey (2021) IEEE Internet of Things Journal, 8 (18), pp. 13789-13804. , Sep; Nguyen, H.X., Trestian, R., To, D., Tatipamula, M., Digital twin for 5g and beyond (2021) IEEE Communications Magazine, 59 (2), pp. 10-15. , Feb; Sesto-Castilla, D., Garcia-Villegas, E., Lyberopoulos, G., Theodoropoulou, E., Use of Machine Learning for energy efficiency in present and future mobile networks (2019) 2019 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1-6. , Apr; Zhou, C., Digital twin network: Concepts and reference architecture (2021) Internet Engineering Task Force, Internet Draft draft-zhou-nmrg-digitaltwin-network-concepts-06, , Dec; Ai, Y., Peng, M., Zhang, K., Edge computing technologies for Internet of Things: A primer (2018) Digital Communications and Networks, 4 (2), pp. 77-86. , Apr; Lu, Y., Maharjan, S., Zhang, Y., Adaptive edge association for wireless digital twin networks in 6g (2021) IEEE Internet of Things Journal, 8 (22), pp. 16219-16230. , Nov; Dong, R., She, C., Hardjawana, W., Li, Y., Vucetic, B., Deep learning for hybrid 5g services in mobile edge computing systems: Learn from a digital twin (2019) IEEE Transactions on Wireless Communications, 18 (10), pp. 4692-4707. , Oct; Groshev, M., Guimarães, C., Martín-Pérez, J., De La Oliva, A., Toward intelligent cyber-physical systems: Digital twin meets artificial intelligence (2021) IEEE Communications Magazine, 59 (8), pp. 14-20. , Aug; Zhao, L., Han, G., Li, Z., Shu, L., Intelligent digital twin- based software-defined vehicular networks (2020) IEEE Network, 34 (5), pp. 178-184. , Sep; Alsboui, T., Qin, L., Hill, R., Al-Aqrabi, H., Distributed intelligence in the internet of things: Challenges and opportunities (2021) Computer Science, 2 (4). , Jul; Pastor, A., Mozo, A., Lopez, D.R., Folgueira, J., Kapodistria, A., The mouseworld, a security traffic analysis lab based on nfv/sdn (2018) Proc. of the 13th International Conference on Availability, Reliability and Security, pp. 1-6. , New York, NY, USA, Aug; Mozo, A., González-Prieto, Á., Pastor, A., Gómez-Canaval, S., Talavera, E., Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks (2022) Sci Rep, 12 (1), p. 2091. , Dec; González-Prieto, Á., Mozo, A., Gómez-Canaval, S., Talavera, E., Improving the quality of generative models through Smirnov transformation, , Oct; Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S., A comprehensive survey on graph neural networks (2021) IEEE Trans. Neural Netw. Learning Syst., 32 (1), pp. 4-24. , Jan; Rusek, K., Suárez-Varela, J., Almasan, P., Barlet-Ros, P., Cabellos-Aparicio, A., Routenet: Leveraging graph neural networks for network modeling and optimization in sdn (2020) IEEE J. Select. Areas Commun., 38 (10), pp. 2260-2270. , Oct","Mozo, A.; Universidad Politécnica de Madrid, Spain; email: a.mozo@upm.es","Varga P.Granville L.Z.Galis A.Godor I.Limam N.Chemouil P.Francois J.Pahl M.-O.",,"Institute of Electrical and Electronics Engineers Inc.","2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022","25 April 2022 through 29 April 2022",,179994,,9781665406017,,,"English","Proc. IEEE/IFIP Netw. Oper. Manag. Symp.: Netw. Serv. Manag. Era Cloudification, Softwarization Artif. Intell., NOMS",Conference Paper,"Final","",Scopus,2-s2.0-85133161518
"Jacob G., Raddatz F.","57742483300;56922056100;","Data Fusion for the Efficient NDT of Challenging Aerospace Structures – A Review",2022,"Proceedings of SPIE - The International Society for Optical Engineering","12049",,"120490C","","",,,"10.1117/12.2612357","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132010494&doi=10.1117%2f12.2612357&partnerID=40&md5=3dbd2ee033095e923f3b074c42a608ca","DLR Institute for Maintenance, Repair and Overhaul, Hein-Sass-Weg 22, Hamburg, 21129, Germany","Jacob, G., DLR Institute for Maintenance, Repair and Overhaul, Hein-Sass-Weg 22, Hamburg, 21129, Germany; Raddatz, F., DLR Institute for Maintenance, Repair and Overhaul, Hein-Sass-Weg 22, Hamburg, 21129, Germany","High performance multifunctional structural components and other system components are evolving for applications in the aerospace industry. The efficient operation and reliability of these structures must be ensured by suitable means for inspection and maintenance. However, inspection on complex structural elements via traditional non-destructive testing (NDT) methods presents challenges for the accurate detection and characterization of flaws. The combination of NDT methods offers considerable advantages over existing NDT technologies and ensures not only accuracy in the inspection, but also another perspective on flaws which otherwise would not have been identified. Large sets of data are generated through these inspections and require robust data fusion technologies for visualisation and interpretation. Emerging technologies such as artificial intelligence, internet of things and automation direct towards the new paradigms NDT 4.0 and digital twin. The measurement data for assessing the health and faulty conditions of the structure using integrated sensors and NDT methods can be represented in a digital replication of the structure called the digital twin. Technologies such as drones, augmented reality and remote NDT can help to improve the efficiency of inspections. Therefore, this review represents current technologies and concepts for NDT 4.0 and the digital twin concepts which are suitable to save time, optimize processes and maintenance costs. © 2022 SPIE.","Data fusion; Digital twin; Digitalisation; Multifunctional composites; Non-destructive testing (NDT); Thermography; Ultrasonic testing","Aerospace industry; Augmented reality; Bridge decks; Data fusion; Inspection; Thermography (imaging); Ultrasonic testing; Aerospace structure; Digitalization; Inspection and maintenance; Multifunctional composites; Non-destructive testing; Performance; Robust data fusion; Structural component; Structural elements; System components; Nondestructive examination",,,,,,,,,,,,,,,,"Mall, S., Dosedel, S., Holl, M., The performance of graphite-epoxy composite with embedded optical fibers under compression (1996) Smart materials and structures, 5 (2), p. 209; Pototzky, A., Stefaniak, D., Hühne, C., Potentials of load carrying, structural integrated conductor tracks (2017) 47th International SAMPE Symposium and Exhibition, p. 47. , [Books 1 and 2]; Andreades, C., Mahmoodi, P., Ciampa, F., Characterisation of smart CFRP composites with embedded PZT transducers for nonlinear ultrasonic applications (2018) Composite Structures, 206, pp. 456-466; Chilles, J. S., Koutsomitopoulou, A. F., Croxford, A. J., Bond, I. P., Monitoring cure and detecting damage in composites with inductively coupled embedded sensors (2016) Composites Science and Technology, 134, pp. 81-88; Schmidt, D., Kolbe, A., Kaps, R., Wierach, P., Linke, S., Steeger, S., von Dungern, F., Newman, B., Development of a door surround structure with integrated structural health monitoring system (2016) Smart Intelligent Aircraft Structures (SARISTU), pp. 935-945. , Springer; Jeon, B. S., Lee, J. J., Kim, J. K., Huh, J. S., Low velocity impact and delamination buckling behavior of composite laminates with embedded optical fibers (1999) Smart Materials and structures, 8 (1), p. 41; Lees-Miller, J. D., Data Fusion in Non-Destructive Testing ZfP - TUM Wiki, , https://wiki.tum.de/display/zfp/Data+Fusion+in+Non-Destructive+Testing, (Accessed: 15 September 2021); Cormerais, R., Duclos, A., Wasselynck, G., Berthiau, G., Longo, R., A data fusion method for nondestructive testing by means of artificial neural networks (2021) Sensors (Basel, Switzerland), 21 (8); Liu, Z., Forsyth, D. S., Komorowski, J. P., Hanasaki, K., Kirubarajan, T., Survey: State of the art in NDE data fusion techniques (2007) IEEE Transactions on Instrumentation and Measurement, 56 (6), pp. 2435-2451; Raddatz, F., (2016) Lokalisierung der Interaktionsorte von Lambwellen in komplexen Faserverbundstrukturen, , PhD thesis, Institut für Faserverbundleichtbau und Adaptronik; Roy, R., Stark, R., Tracht, K., Takata, S., Mori, M., Continuous maintenance and the future – foundations and technological challenges (2016) CIRP Annals, 65 (2), pp. 667-688; Roach, D. P., Rice, T. M., (2018) Addressing technical and regulatory requirements to deploy structural health monitoring systems on commercial aircraft, , tech. rep., Sandia National Lab.(SNL-NM), Albuquerque, NM (United States); Gros, X. E., (2001) Applications of NDT Data Fusion, , ed., Springer US, Boston, MA and s.l; Liu, Z., Meyendorf, N., Mrad, N., The role of data fusion in predictive maintenance using digital twin (2018) AIP Conference Proceedings, p. 020023. , Author(s); Meyer, H., Zimdahl, J., Kamtsiuris, A., Meissner, R., Raddatz, F., Haufe, S., Bäßler, M., Development of a digital twin for aviation research (2020) Deutscher Luft- und Raumfahrt Kongress, , (September); Wilken, A., Heilemann, F., Turgut, L., Helfrich, G., (2021) Digitisation of inspection process using hybrid tracking of part and probe for future maintenance and digital twins; Yilmaz, B., Ba, A., Jasiuniene, E., Bui, H.-K., Berthiau, G., Evaluation of bonding quality with advanced nondestructive testing (NDT) and data fusion (2020) Sensors (Basel, Switzerland), 20 (18); Mook, G., Pohl, J., Michel, F., Non-destructive characterization of smart CFRP structures (2003) Smart Materials and Structures, 12, pp. 997-1004; Constantin, N., Mihai, A., Anghel, V., Gavan, M., Sorohan, Ş., Hillger, W., Sheerer, M., Results of nondestructive inspection of layered composites using IR thermography and ultrasonics (2009) Key Engineering Materials, 413, pp. 343-350. , Trans Tech Publ; Gros, X. E., Bousigue, J., Takahashi, K., Ndt data fusion at pixel level (1999) NDT & E International, 32 (5), pp. 283-292; Towsyfyan, H., Biguri, A., Boardman, R., Blumensath, T., Successes and challenges in non-destructive testing of aircraft composite structures (2020) Chinese Journal of Aeronautics, 33 (3), pp. 771-791; Raddatz, F., Wierach, P., Sinapius, M., Damage reconstruction in complex composite structures using lamb waves (2016) 19th World Conference on Non-Destructive Testing 2016; Steigmann, R., Iftimie, N., Sturm, R., Vizureanu, P., Savin, A., Complementary methods for nondestructive testing of composite materials reinforced with carbon woven fibers (2015) IOP Conference Series: Materials Science and Engineering, 95 (1), p. 012091. , IOP Publishing; Mall, S., Coleman, J. M., Monotonic and fatigue loading behavior of quasi-isotropic graphite/epoxy laminate embedded with piezoelectric sensor (1998) Smart Materials and Structures, 7, pp. 822-832; Mall, S., Integrity of graphite/epoxy laminate embedded with piezoelectric sensor/actuator under monotonic and fatigue loads* (2002) Smart Materials and Structures, 11, pp. 527-533; Yocum, M., Abramovich, H., Grunwald, A., Mall, S., Fully reversed electromechanical fatigue behavior of composite laminate with embedded piezoelectric actuator/sensor (2003) Smart Materials and Structures, 12, pp. 556-564; Butler, S., Gurvich, M. R., Ghoshal, A., Welsh, G., Attridge, P., Winston, H. A., Urban, M. R., Bordick, N. E., Effect of embedded sensors on interlaminar damage in composite structures (2011) Journal of Intelligent Material Systems and Structures, 22, pp. 1857-1868; Yoo, S., Khatibi, A. 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H., Active health monitoring of thick composite structures by embedded and surface-mounted piezo diagnostic layer (2020) Sensors (Basel, Switzerland), 20; Kim, H., Park, M., Hsieh, K., Fatigue fracture of embedded copper conductors in multifunctional composite structures (2006) Composites Science and Technology, 66 (7), pp. 1010-1021; Kim, H., Hsieh, K., Measurement and prediction of embedded copper foil fatigue crack growth in multifunctional composite structure (2012) Composites Part A: Applied Science and Manufacturing, 43 (3), pp. 492-506; Kim, H., González, M., Fatigue failure of printed circuit board chemically etched copper traces in multifunctional composite structures (2014) Journal of Composite Materials, 48, pp. 985-996; Ouroua, Y., Abdi, S., Bachirbey, I., Rupture by impact-induced fatigue of a copper foil strip embedded in a multifunctional composite material (2021) Journal of Composite Materials, 55, pp. 2631-2643; Schmidt, D., Moix-Bonet, M., Wierach, P., (2015) Demonstration of a full-scale aircraft structure with integrated structural health monitoring network; Güemes, A., Fernandez-Lopez, A., Pozo, A. R., Sierra-Pérez, J., Structural health monitoring for advanced composite structures: a review (2020) Journal of Composites Science, 4 (1), p. 13; Kralovec, C., Schagerl, M., Review of structural health monitoring methods regarding a multi-sensor approach for damage assessment of metal and composite structures (2020) Sensors, 20 (3); Wu, R.-T., Jahanshahi, M. R., Data fusion approaches for structural health monitoring and system identification: past, present, and future (2020) Structural Health Monitoring, 19 (2), pp. 552-586; Gros, X. E., Pixel level ndt data fusion (2001) Applications of NDT Data Fusion, pp. 25-57. , Gros, X. E., ed., Springer US, Boston, MA and s.l; Dabetwar, S., Ekwaro-Osire, S., Dias, J. P., Fatigue damage diagnostics of composites using data fusion and data augmentation with deep neural networks (2022) Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, 5 (2); Saxena, A., Goebel, K., Larrosa, C., Chang, F., (2015) Cfrp composites dataset, nasa ames prognostics data repository; Wilcox, P. D., Croxford, A. J., Budyn, N., Bevan, R. L. T., Zhang, J., Kashubin, A., Cawley, P., Fusion of multi-view ultrasonic data for increased detection performance in non-destructive evaluation (2020) Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 476 (2243); Bevan, R. L. T., Budyn, N., Zhang, J., Croxford, A. J., Kitazawa, S., Wilcox, P. D., Data fusion of multiview ultrasonic imaging for characterization of large defects (2020) IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67 (11), pp. 2387-2401; Jobst, M., Koetz, A., Clendening, S., (2010) 10th european conference on non-destructive testing; Vrana, J., The core of the fourth revolutions: Industrial internet of things, digital twin, and cyber-physical loops (2021) Journal of Nondestructive Evaluation, 40 (2), pp. 1-21; Practice for digital imaging and communication in nondestructive evaluation (DICONDE) (2016) ASTM International, , www.astm.org, ASTM E2339-15; Practice for digital imaging and communication in nondestructive evaluation (DICONDE) for ultrasonic test methods (2018) ASTM International, , www.astm.org, ASTM E2663-14","Jacob, G.; DLR Institute for Maintenance, Hein-Sass-Weg 22, Germany; email: geo.jacob@dlr.de","Meyendorf N.G.Meyendorf N.G.Farhangdoust S.Niezrecki C.","The Society of Photo-Optical Instrumentation Engineers (SPIE)","SPIE","NDE 4.0, Predictive Maintenance, and Communication and Energy Systems in a Globally Networked World 2022","4 April 2022 through 10 April 2022",,179700,0277786X,9781510649736,PSISD,,"English","Proc SPIE Int Soc Opt Eng",Conference Paper,"Final","",Scopus,2-s2.0-85132010494
"Futai M.M., Bittencourt T.N., Carvalho H., Ribeiro D.M.","12142761800;6603036318;56656114300;25930078000;","Challenges in the application of digital transformation to inspection and maintenance of bridges",2022,"Structure and Infrastructure Engineering","18","10-11",,"1581","1600",,,"10.1080/15732479.2022.2063908","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129636635&doi=10.1080%2f15732479.2022.2063908&partnerID=40&md5=b016f92daa1c3b1fd9ef68ce9768efb2","Polytechnic School, University of São Paulo, São Paulo, Brazil; Department of Structural Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil; PhDsoft, Rio de Janeiro, Brazil","Futai, M.M., Polytechnic School, University of São Paulo, São Paulo, Brazil; Bittencourt, T.N., Polytechnic School, University of São Paulo, São Paulo, Brazil; Carvalho, H., Department of Structural Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil; Ribeiro, D.M., PhDsoft, Rio de Janeiro, Brazil","Bridges constitute an important part of the infrastructure and are subjected to damage and deterioration of materials and support conditions, as well as exposure to adverse environmental conditions. Continuous or repeated monitoring of structural responses may add important information for decision-making regarding their maintenance, repair and reinforcement. The use of these data, in conjunction with techniques of structural reliability for the treatment of the uncertainties, allows a better understanding of the structural behaviour and integrity. Modern Information and Communication Technologies can greatly contribute to the improvement of the maintenance capacity and, consequently, to the reliability of the assets and to their operational availability. New wireless communication technologies, such as 5 G networks, are considered as the enabling technologies of the digital transformation, integrated with the concept of the Internet of Things. High connectivity capacity and intensive automation enable, for example, changes in inspection paradigms and asset maintenance, by transferring the product focus to service platforms, bringing gains to productivity, comfort, operational safety and costs. New predictive maintenance approaches, which make use of a large amount of data available, can improve the efficiency of maintenance processes, producing more accurate and reliable anticipated diagnostics. The Digital Twins incorporate all these tools and allow a real-time view of the evolution of the asset behaviour. This concept applied to a railway bridge is presented and discussed in detail in this paper. © 2022 Informa UK Limited, trading as Taylor & Francis Group.","bridge inspection; bridge maintenance; Digital transformation; digital twin; information and communication technologies; Internet of Things; structural health monitoring","5G mobile communication systems; Decision making; Deterioration; Inspection; Internet of things; Life cycle; Repair; Bridge inspection; Bridges maintenance; Decisions makings; Digital transformation; Environmental conditions; Information and Communication Technologies; Inspection and maintenance; Material conditions; Structural response; Support conditions; Structural health monitoring",,,,,"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq","Authors would like to acknowledge CNPq (Brazilian Ministry of Science and Technology Agency), CAPES (Higher Education Improvement Agency) and VALE Catedra Under Rail for providing an important part of the financial support needed to develop this paper. The work described in this paper has been partially supported by VLI and VALE Railway Companies. The opinions and conclusions presented in this paper are those of the authors and do not necessarily reflect the views of the sponsoring organizations. We also acknowledge the members of lab-Infra, GeoInfraUSP and GMEC research group: A.P. da Conceição Neto, A. Colombo, A.L.D. Pereira Filho, F.Y. Toriume, F.K. Toome, G.V. Menezes, J.J. Arrieta Baldovino, L.B. Machado and R. R. Santos. The interaction and the support provided by PhDsoft is deeply appreciated.",,,,,,,,,,"Abu Dabous, S., Feroz, S., Condition monitoring of bridges with non-contact testing technologies (2020) Automation in Construction, 116, pp. 103224-103220; Akiyama, M., Frangopol, D.M., (2018) Life-cycle reliability of bridges under independent and interacting hazards, , London: Taylor & Francis Group; Alani, A.M., Tosti, F., Ciampoli, L.B., Gagliardi, V., Benedetto, A., An integrated investigative approach in health monitoring of masonry arch bridges using GPR and InSAR Technologies (2020) NDT & E International, 115, pp. 102288-102217; Biondini, F., Frangopol, D.M., Life-cycle performance of deteriorating structural systems under uncertainty: Review (2016) Journal of Structural Engineering, 142 (9), pp. 1-17; Biondini, F., Frangopol, D.M., Life-cycle performance of civil structure and infrastructure systems: Survey (2018) Journal of Structural Engineering, 144 (1), p. 06017008; Biscarini, C., Catapano, I., Cavalagli, N., Ludeno, G., Pepe, F.A., Ubertini, F., UAV photogrammetry, infrared thermography and GPR for enhancing structural and material degradation evaluation of the Roman masonry bridge of Ponte Lucano in Italy (2020) NDT & E International, 115, p. 102287; Bittencourt, T.N., Beck, A.T., Frangopol, D., (2016) Maintenance, monitoring, safety, risk and resilience of bridges and bridge networks, 1, p. 616. , London: CRC Press - Taylor and Francis, &, (, p; Bocchini, P., Frangopol, D.M., Ummenhofer, T., Zinke, T., Resilience and sustainability of civil infrastructure: Toward a unified approach (2014) Journal of Infrastructure System, 20 (2), pp. 1-16; Bolourian, N., Hammad, A., LiDAR-equipped UAV path planning considering potential locations of defects for bridge inspection (2020) Automation in Construction, 117, p. 103250; Briaud, J.L., Gardoni, P., Yao, C., Statistical, risk, and reliability analyses of bridge scour (2014) Journal of Geotechnical and Geoenvironmental Engineering, 140 (2), p. 04013011; Chen, B., Yang, Y., Zhou, J., Zhuang, Y., McFarland, M., Damage detection of underwater foundation of a Chinese ancient stone arch bridge via sonar-based techniques (2021) Measurement, 169, p. 108283; Chen, G., Liang, Q., Zhong, W., Gao, X., Cui, F., Homography-based measurement of bridge vibration using UAV and DIC method (2021) Measurement, 170, p. 108683; Chen, J.G., Wadhwa, N., Cha, Y.-J., Durand, F., Freeman, W.T., Buyukozturk, O., Modal identification of simple structures with high-speed video using motion magnification (2015) Journal of Sound and Vibration, 345, pp. 58-71; Civera, M., Fragonara, Z.L., Surace, C., An experimental study of the feasibility of phase-based video magnification for damage detection and localisation in operational deflection shapes (2020) Strain, 56 (1), p. 19; Dong, C.Z., Celik, O., Catbas, F.N., Marker-free monitoring of the grandstand structures and modal identification using computer vision methods (2019) Structural Health Monitoring, 18 (5-6), pp. 1491-1509; Ellingwood, B.R., Risk-informed condition assessment of civil infrastructure: State of practice and research issues (2005) Structure and Infrastructure Engineering, 1 (1), pp. 7-18; Erdenebat, D., Waldmann, D., Application of the DAD method for damage localisation on an existing bridge structure using close-range UAV photogrammetry (2020) Engineering Structures, 218, p. 110727; Fan, W., Chen, Y., Li, J., Sun, Y., Feng, J., Hassanin, H., Sareh, P., Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications (2021) Structures, 33, pp. 3954-3963; Faridafshin, F., Anvari, M., Hellevig, N.C., Risk-based approaches for planning the inspections of large-scale topside structural systems (2019) Proceedings of the SPE Offshore Europe Conference and Exhibition, , Society of Petroleum Engineers, UK; Feng, D., Feng, M.Q., Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection–A review (2018) Engineering Structures, 156, pp. 105-117; Feroz, S., Abu Dabous, S., UAV-based remote sensing applications for bridge condition assessment (2021) Remote Sensing, 13 (9), pp. 1809-1838; Frangopol, D.M., Sabatino, S., Dong, Y., Bridge life-cycle performance and cost: Analysis, prediction, optimization and decision-making (2016) CRC Press - Taylor and Francis, pp. 3-17. , Foz do Iguaçu, Brazil; Frangopol, D.M., Soliman, M., (2015) Life-cycle of structural systems: Recent achievements and future directions. 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Infrastructure Eng.",Article,"Final","",Scopus,2-s2.0-85129636635
"Jiang F., Ding Y., Song Y., Geng F., Wang Z.","57204694266;55768944900;55494118800;36637279300;36723167900;","Digital Twin-driven framework for fatigue lifecycle management of steel bridges",2022,"Structure and Infrastructure Engineering",,,,"","",,,"10.1080/15732479.2022.2058563","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129166934&doi=10.1080%2f15732479.2022.2058563&partnerID=40&md5=7ea32343d178c2ab01e99baa1a63a261","Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing, China; School of Architecture Engineering, Jinling Institute of Technology, Nanjing, China; School of Architecture Engineering, Nanjing Institute of Technology, Nanjing, China; Shenzhen Express Engineering Consulting Co. Ltd, Shenzhen, China","Jiang, F., Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing, China; Ding, Y., Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing, China; Song, Y., School of Architecture Engineering, Jinling Institute of Technology, Nanjing, China; Geng, F., School of Architecture Engineering, Nanjing Institute of Technology, Nanjing, China; Wang, Z., Shenzhen Express Engineering Consulting Co. Ltd, Shenzhen, China","This paper presents a Digital Twin-driven framework for fatigue lifecycle management of steel bridges. A probabilistic multi-scale fatigue deterioration model is proposed to predict the entire fatigue process of steel bridges. Bayesian inference of the deterioration parameters realizes the real-time updating of the predicted lifecycle fatigue evolution process, which provides a good basis for lifecycle optimization. To avoid an empirically predefined repair crack size for maintenance, an optimization process for maintenance strategies is included. The relationship of the extended lifetime and the design repair crack size is constructed by numerical experimental design and surrogate modeling. The solution for optimum repair crack size is obtained while maximizing the extended fatigue life and minimizing the maintenance costs. Based on the occurrence time distribution of the optimum repair crack size, the inspection/monitoring planning is determined from a probabilistic optimization process based on the minimization of the expected damage detection delay and the lifecycle costs. The uncertainties associated with the damage occurrence and detection ability are considered during the formulation of the expected damage detection delay by decision tree analysis. Based on Digital Twin concept, the predicted deterioration process, derived maintenance, and inspection/monitoring planning are timely updated until a defined stopping rule is met. © 2022 Informa UK Limited, trading as Taylor & Francis Group.","damages; Digital Twin; Fatigue; lifecycle management; optimization; steel bridges; uncertainties","Bayesian networks; Damage detection; Deterioration; Inference engines; Life cycle; Probability distributions; Repair; Steel bridges; Uncertainty analysis; Crack sizes; Damage; Damage detection delays; Fatigue deterioration; Fatigue life cycles; Lifecycle management; Multi-scales; Optimisations; Probabilistics; Uncertainty; Decision trees",,,,,,,,,,,,,,,,"Ahmad, R., Kamaruddin, S., An overview of time-based and condition-based maintenance in industrial application (2012) Computers & Industrial Engineering, 63 (1), pp. 135-149; Amaireh, L.K., Al-Tamimi, A., Optimum configuration of CFRP composites for strengthening of reinforced concrete beams considering the contact constraint (2020) Procedia Manufacturing, 44, pp. 350-357; Ansari, F., Glawar, R., Nemeth, T., PriMa: A prescriptive maintenance model for cyber-physical production systems (2019) International Journal of Computer Integrated Manufacturing, 32 (4-5), pp. 482-503; Aqra, F., Ayyad, A., Surface energies of metals in both liquid and solid states (2011) Applied Surface Science, 257 (15), pp. 6372-6379; Božić, Ž., Schmauder, S., Mlikota, M., Hummel, M., Multiscale fatigue crack growth modelling for welded stiffened panels (2014) Fatigue & Fracture of Engineering Materials & Structures, 37 (9), pp. 1043-1054; (2005) BS 7910:2005: Guide to methods for assessing the acceptability of flaws in metallic structures, , London: British Standards Institution; Catbas, F.N., Susoy, M., Frangopol, D.M., Structural health monitoring and reliability estimation: Long span truss bridge application with environmental monitoring data (2008) Engineering Structures, 30 (9), pp. 2347-2359; (2003) EN 1991-2: EUROCODE 1: Actions on structures - Part 2: Traffic loads on bridges, , London: British Standards Institution; Chung, H.-Y., Manuel, L., Frank, K.H., Optimal inspection scheduling of steel bridges using nondestructive testing techniques (2006) Journal of Bridge Engineering, 11 (3), pp. 305-319; Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II (2002) IEEE Transactions on Evolutionary Computation, 6 (2), pp. 182-197; Dorafshan, S., Thomas, R.J., Maguire, M., Fatigue crack detection using unmanned aerial systems in fracture critical inspection of steel bridges (2018) Journal of Bridge Engineering, 23 (10), p. 04018078; Ehlen, M.A., Life-cycle costs of fiber-reinforced-polymer bridge decks (1999) Journal of Materials in Civil Engineering, 11 (3), pp. 224-230; Erdogan, F., Sih, G.C., On the crack extension in plates under plane loading and transverse shear (1963) Journal of Basic Engineering, 85 (4), pp. 519-525; Errandonea, I., Beltrán, S., Arrizabalaga, S., Digital Twin for maintenance: A literature review (2020) Computers in Industry, 123, p. 103316; Farhey, D.N., Bridge instrumentation and monitoring for structural diagnostics (2005) Structural Health Monitoring, 4 (4), pp. 301-318; Fine, M.E., Bhat, S.P., A model of fatigue crack nucleation in single crystal iron and copper (2007) Materials Science and Engineering: A, 468-470 (SPEC. ISS), pp. 64-69; Fisher, J.W., Barsom, J.M., Evaluation of cracking in the rib-to-deck welds of the Bronx–Whitestone bridge (2016) Journal of Bridge Engineering, 21 (3), p. 04015065; Fisher, J.W., Roy, S., Fatigue of steel bridge infrastructure (2011) Structure and Infrastructure Engineering, 7 (7-8), pp. 457-475; Frangopol, D.M., Dong, Y., Sabatino, S., Bridge life-cycle performance and cost: Analysis, prediction, optimisation and decision-making (2017) Structure and Infrastructure Engineering, 13 (10), pp. 1239-1257; Frangopol, D.M., Lin, K.-Y., Estes, A.C., Life‐cycle cost design of deteriorating structures (1998) Journal of Structural Engineering, 124 (11), pp. 1368-1369; Frangopol, D.M., Strauss, A., Kim, S., Bridge reliability assessment based on monitoring (2008) Journal of Bridge Engineering, 13 (3), pp. 258-270; Frangopol, D.M., Strauss, A., Kim, S., Use of monitoring extreme data for the performance prediction of structures: General approach (2008) Engineering Structures, 30 (12), pp. 3644-3653; Glaessgen, E., Stargel, D., The Digital Twin paradigm for future NASA and U.S. air force vehicles (2012) American Institute of Aeronautics and Astronautics, pp. 1-14. , Reston, VA:, &, April; Huang, Y., Zhang, Q., Bao, Y., Bu, Y., Fatigue assessment of longitudinal rib-to-crossbeam welded joints in orthotropic steel bridge decks (2019) Journal of Constructional Steel Research, 159, pp. 53-66; Jiang, F., Ding, Y., Song, Y., Geng, F., Wang, Z., An architecture of lifecycle fatigue management of steel bridges driven by Digital Twin (2021) Structural Monitoring and Maintenance, 8 (2), pp. 187-201; Jiang, F., Ding, Y., Song, Y., Geng, F., Wang, Z., Digital Twin-driven framework for fatigue life prediction of steel bridges using a probabilistic multiscale model: Application to segmental orthotropic steel deck specimen (2021) Engineering Structures, 241, p. 112461; Kim, S., Frangopol, D.M., Optimum inspection planning for minimizing fatigue damage detection delay of ship hull structures (2011) International Journal of Fatigue, 33 (3), pp. 448-459; Kim, S., Frangopol, D.M., Probabilistic bicriterion optimum inspection/monitoring planning: Applications to naval ships and bridges under fatigue (2011) Structure and Infrastructure Engineering, 8 (10), pp. 912-927; Kim, S., Frangopol, D.M., Efficient multi-objective optimisation of probabilistic service life management (2017) Structure and Infrastructure Engineering, 13 (1), pp. 147-159; Kim, S., Frangopol, D.M., Decision making for probabilistic fatigue inspection planning based on multi-objective optimization (2018) International Journal of Fatigue, 111, pp. 356-368; Leander, J., Al-Emrani, M., Reliability-based fatigue assessment of steel bridges using LEFM–A sensitivity analysis (2016) International Journal of Fatigue, 93, pp. 82-91; Li, L., Shen, L., Proust, G., Fatigue crack initiation life prediction for aluminium alloy 7075 using crystal plasticity finite element simulations (2015) Mechanics of Materials, 81, pp. 84-93; Liu, H., Al-Mahaidi, R., Zhao, X.-L., Experimental study of fatigue crack growth behaviour in adhesively reinforced steel structures (2009) Composite Structures, 90 (1), pp. 12-20; Liu, M., Frangopol, D.M., Kim, S., Bridge safety evaluation based on monitored live load effects (2009) Journal of Bridge Engineering, 14 (4), pp. 257-269; Liu, J., Guo, T., Feng, D., Liu, Z., Fatigue performance of rib-to-deck joints strengthened with FRP angles (2018) Journal of Bridge Engineering, 23 (9), p. 04018060; Mabkhot, M., Al-Ahmari, A., Salah, B., Alkhalefah, H., Requirements of the smart factory system: A survey and perspective (2018) Machines, 6 (2), p. 23; Okasha, N.M., Frangopol, D.M., Integration of structural health monitoring in a system performance based life-cycle bridge management framework (2010) Structure and Infrastructure Engineering, 8 (11), pp. 999-1016; Okasha, N.M., Frangopol, D.M., Decò, A., Integration of structural health monitoring in life-cycle performance assessment of ship structures under uncertainty (2010) Marine Structures, 23 (3), pp. 303-321; Peng, J., Yang, Y., Bian, H., Zhang, J., Wang, L., Optimisation of maintenance strategy of deteriorating bridges considering sustainability criteria (2022) Structure and Infrastructure Engineering, , 18(3), 395–411; Schönecker, S., Li, X., Johansson, B., Kwon, S.K., Vitos, L., Thermal surface free energy and stress of iron (2015) Scientific Reports, 5 (1), p. 14860; Soliman, M., Frangopol, D.M., Life-cycle management of fatigue-sensitive structures integrating inspection information (2014) Journal of Infrastructure Systems, 20 (2), p. 04014001; Soliman, M., Frangopol, D.M., Mondoro, A., A probabilistic approach for optimizing inspection, monitoring, and maintenance actions against fatigue of critical ship details (2016) Structural Safety, 60, pp. 91-101; Teng, J.G., Yu, T., Fernando, D., Strengthening of steel structures with fiber-reinforced polymer composites (2012) Journal of Constructional Steel Research, 78, pp. 131-143; Vafaei, N., Ribeiro, R.A., Camarinha-Matos, L.M., Normalization techniques for multi-criteria decision making: Analytical hierarchy process case study (2016) Technological innovation for cyber-physical systems, pp. 261-269. , Camarinha-Matos L.M., Falcão A.J., Vafaei N., Najdi S., (eds), Cham: Springer, &,. (Eds; Wang, Y., Fu, Z., Ge, H., Ji, B., Hayakawa, N., Cracking reasons and features of fatigue details in the diaphragm of curved steel box girder (2019) Engineering Structures, 201, p. 109767; Wang, B., Nagy, W., De Backer, H., Chen, A., Fatigue process of rib-to-deck welded joints of orthotropic steel decks (2019) Theoretical and Applied Fracture Mechanics, 101, pp. 113-126; Wu, C., Zhao, X.L., Al-Mahaidi, R., Emdad, M.R., Duan, W.H., Fatigue tests on steel plates with longitudinal weld attachment strengthened by ultra high modulus carbon fibre reinforced polymer plate (2013) Fatigue & Fracture of Engineering Materials & Structures, 36 (10), pp. 1027-1038; Wu, C., Zhao, X., Al-Mahaidi, R., Emdad, M.R., Duan, W., Fatigue tests of cracked steel plates strengthened with UHM CFRP plates (2012) Advances in Structural Engineering, 15 (10), pp. 1801-1815; Yang, D.Y., Frangopol, D.M., Probabilistic optimization framework for inspection/repair planning of fatigue-critical details using dynamic Bayesian networks (2018) Computers & Structures, 198, pp. 40-50; Yang, D.Y., Frangopol, D.M., Teng, J.-G., Probabilistic life-cycle optimization of durability-enhancing maintenance actions: Application to FRP strengthening planning (2019) Engineering Structures, 188, pp. 340-349; Yeratapally, S.R., Leser, P.E., Hochhalter, J.D., Leser, W.P., Ruggles, T.J., A digital twin feasibility study (Part I): Non-deterministic predictions of fatigue life in aluminum alloy 7075-T651 using a microstructure-based multi-scale model (2020) Engineering Fracture Mechanics, 228, p. 106888; Yu, Q.Q., Chen, T., Gu, X.L., Zhao, X.L., Xiao, Z.G., Fatigue behaviour of CFRP strengthened steel plates with different degrees of damage (2013) Thin-Walled Structures, 69, pp. 10-17; Zhang, K.-S., Ju, J.W., Li, Z., Bai, Y.-L., Brocks, W., Micromechanics based fatigue life prediction of a polycrystalline metal applying crystal plasticity (2015) Mechanics of Materials, 85, pp. 16-37","Ding, Y.; Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, China; email: civilchina@hotmail.com",,,"Taylor and Francis Ltd.",,,,,15732479,,,,"English","Struct. Infrastructure Eng.",Article,"Article in Press","",Scopus,2-s2.0-85129166934
"Kim J., Ham Y.","57209808963;55651299500;","Real-Time Participatory Sensing-Driven Computational Framework toward Digital Twin City Modeling",2022,"Construction Research Congress 2022: Computer Applications, Automation, and Data Analytics - Selected Papers from Construction Research Congress 2022","2-B",,,"281","289",,,"10.1061/9780784483961.030","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128979097&doi=10.1061%2f9780784483961.030&partnerID=40&md5=20a3add963ea00b40c17c2b634d18b06",,"Kim, J.; Ham, Y.","A concept of ""digital twin"" as a model to bridge between a real-world and a virtual twin has emerged in manufacturing and product management. In the context of civil and infrastructure domains, the term ""digital twin"" has been redefined as up-to-date digital representations of built environments. This paper proposes a real-time participatory sensing-driven computational framework to model the up-to-date state of built environments in a virtual environment. In the proposed framework, crowdsourced visual data are obtained from citizens' participation to automatically identify the up-to-date states of infrastructure that would negatively impact the community resilience in extreme weather conditions. Then, to update the associated built environment information, real-time participatory sensing data are processed by using deep learning algorithms. Finally, the identified geometric and geospatial information of infrastructure is fed into the virtual environment toward a digital twin city model. Case studies in the context of power distribution infrastructure systems were conducted in Houston, TX, and it was demonstrated that the proposed method robustly updates the up-to-date condition of infrastructure into the digital twin city model. The proposed approach for updating virtual models based on participatory sensing-based real-time information has a great potential to facilitate data-driven decision-making for urban planning and infrastructure management in a smart city digital twin. © 2022 Construction Research Congress 2022: Computer Applications, Automation, and Data Analytics - Selected Papers from Construction Research Congress 2022. All rights reserved.",,"Data Analytics; Decision making; Deep learning; Smart city; Virtual reality; Built environment; City model; Computational framework; Digital representations; Manufacturing management; Participatory Sensing; Product management; Real- time; Real-world; Visual data; Human computer interaction",,,,,"National Science Foundation, NSF: 1832187","This material is in part based upon work supported by the National Science Foundation (NSF) under CMMI Award #1832187. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.",,,,,,,,,,"Ahn, C., Ham, Y., Kim, J., Kim, J., (2020) A Digital Twin City Model for Age-Friendly Communities: Capturing Environmental Distress from Multimodal Sensory Data, , "" Hawaii International Conference on System Sciences 2020 (HICSS-53); (2020) Amazon Web Services (AWS), , https://aws.amazon.com/?nc2=h_lg, AWS (Amazon Web Sevices). "" "" <>(Sep. 2, 2021); Boje, C., Guerriero, A., Kubicki, S., Rezgui, Y., Towards a semantic Construction Digital Twin: Directions for future research (2020) Automation in Construction, , "" Elsevier B.V; Chatfield, A.T., Reddick, C.G., Customer agility and responsiveness through big data analytics for public value creation: A case study of Houston 311 on-demand services (2018) Government Information Quarterly, , Elsevier Ltd; Chen, X., Kang, E., Shiraishi, S., Preciado, V.M., Jiang, Z., (2018) Digital behavioral twins for safe connected cars, pp. 144-154. , "" Proceedings - 21st ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2018, Association for Computing Machinery, Inc; Cheng, Y., Mean Shift, Mode Seeking, and Clustering (1995) IEEE Transactions on Pattern Analysis and Machine Intelligence, 17 (8), pp. 790-799; (2020) COHGIS DATA HUB, , https://cohgis-mycity.opendata.arcgis.com/, City of Houston. "" "" <>(Jun. 4, 2021); (2021) Houston 311 service, , https://www.houstontx.gov/311/, City of Houston. "" "" <>(Jun. 2, 2021); Fan, C., Jiang, Y., Mostafavi, A., Social Sensing in Disaster City Digital Twin: Integrated Textual-Visual-Geo Framework for Situational Awareness during Built Environment Disruptions (2020) Journal of Management in Engineering, 36 (3), pp. 1-13; Fan, C., Zhang, C., Yahja, A., Mostafavi, A., Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management (2019) International Journal of Information Management, p. 102049. , "" Elsevier Ltd; Ford, D.N., Wolf, C.M., Smart Cities with Digital Twin Systems for Disaster Management (2020) Journal of Management in Engineering, 36 (4). , American Society of Civil Engineers (ASCE), 04020027; Francisco, A., Mohammadi, N., Taylor, J.E., Smart City Digital Twin-Enabled Energy Management: Toward Real-Time Urban Building Energy Benchmarking (2020) Journal of Management in Engineering, 36 (2). , "" American Society of Civil Engineers (ASCE), 04019045; Fuller, A., Fan, Z., Day, C., Barlow, C., Digital Twin: Enabling Technologies, Challenges and Open Research (2020) IEEE Access, 8, pp. 108952-108971. , "" Institute of Electrical and Electronics Engineers Inc; Grieves, M., (2014) Digital Twin »: Manufacturing Excellence through Virtual Factory Replication, pp. 1-7. , White Paper, NASA, Washington D.C; Ham, Y., Kim, J., Participatory Sensing and Digital Twin City: Updating Virtual City Models for Enhanced Risk-Informed Decision-Making (2020) Journal of Management in Engineering, 36 (3). , American Society of Civil Engineers (ASCE), 04020005; Ham, Y., Kim, J., Participatory Sensing and Digital Twin City »: Updating Virtual City Models for Enhanced Risk-Informed Decision-Making (2020) Journal of Management in Engineering, 36 (3), pp. 1-12; He, K., Zhang, X., Ren, S., Sun, J., (2015) Deep Residual Learning for Image Recognition, pp. 770-778. , "" Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, 2016-December; Kamari, M., Ham, Y., (2018) Automated filtering big visual data from drones for enhanced visual analytics in construction, pp. 398-409. , Construction Research Congress 2018: Construction Information Technology - Selected Papers from the Construction Research Congress 2018, American Society of Civil Engineers (ASCE); Kamari, M., Ham, Y., (2020) Analyzing Potential Risk of Wind-induced Damage in Construction Sites and Neighboring Communities using Large-scale Visual Data from Drones, , Construction Research Congress 2020, Tempe, Arizona; Kim, J., Ham, Y., (2020) Vision-Based Analysis of Utility Poles Using Drones and Digital Twin Modeling in the Context of Power Distribution Infrastructure Systems, pp. 954-963. , Construction Research Congress 2020: Computer Applications - Selected Papers from the Construction Research Congress 2020, American Society of Civil Engineers (ASCE); Kim, J., Kim, H., Ham, Y., (2019) Mapping Local Vulnerabilities into a 3D City Model through Social Sensing and the CAVE System toward Digital Twin City, pp. 451-458. , (a). "" "" ASCE International Conference on Computing in Civil Engineering 2019, Computing in Civil Engineering; Kim, J., Kim, H., Ham, Y., (2019) Mapping Local Vulnerabilities into a 3D City Model through Social Sensing and the CAVE System toward Digital Twin City, pp. 451-458. , (b). "" "" Computing in Civil Engineering, Atlanta; Lee, S., Ham, Y., (2020) Probabilistic Analysis of Structural Reliability of Leaning Poles in Power Distribution Infrastructure Systems Under Extreme Wind Loads, , Construction Research Congress 2020: Construction Information Technology, American Society of Civil Engineers (ASCE); Lee, S., Ham, Y., Probabilistic framework for assessing the vulnerability of power distribution infrastructures under extreme wind conditions (2021) Sustainable Cities and Society, 65, p. 102587. , Elsevier Ltd; Lehner, H., Dorffner, L., Digital geoTwin Vienna: Towards a Digital Twin City as Geodata Hub (2020) PFG-Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 88 (1), pp. 63-75. , Springer Science and Business Media LLC; Lin, J., Zha, L., Xu, Z., Consolidated cluster systems for data centers in the cloud age: A survey and analysis (2013) Frontiers of Computer Science, , "" Higher Education Press Limited Company; Lin, Y.-C., Cheung, W.-F., Developing WSN/BIM-Based Environmental Monitoring Management System for Parking Garages in Smart Cities (2020) Journal of Management in Engineering, 36 (3). , American Society of Civil Engineers (ASCE), 04020012; Lu, Q., Parlikad, A.K., Woodall, P., Don Ranasinghe, G., Xie, X., Liang, Z., Konstantinou, E., Schooling, J., Developing a Digital Twin at Building and City Levels: Case Study of West Cambridge Campus (2020) Journal of Management in Engineering, 36 (3). , "" American Society of Civil Engineers (ASCE), 05020004; Marzouk, M., Othman, A., Planning utility infrastructure requirements for smart cities using the integration between BIM and GIS (2020) Sustainable Cities and Society, 57, p. 102120. , Elsevier Ltd; Misra, I., Manthira Moorthi, S., Dhar, D., Ramakrishnan, R., (2012) An automatic satellite image registration technique based on Harris corner detection and Random Sample Consensus (RANSAC) outlier rejection model, pp. 68-73. , "" 2012 1st International Conference on Recent Advances in Information Technology, RAIT-2012; Mohammadi, N., Taylor, J.E., (2018) Smart city digital twins, , 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings; (2020) MongoDB, , https://www.mongodb.com/cloud/atlas/lp/try2?utm_content=controlhterms&utm_source=google&utm_campaign=gs_americas_united_states_search_core_brand_atlas_desktop&utm_term=mongo&utm_medium=cpc_paid_search&utm_ad=e&utm_ad_campaign_id=12212624338&gclid=Cj0KCQjw7MGJBhD-ARIsAMZ0eevxzq4POsEOg4ZclEdy-NC7XCsAWq1C7zQRZ3G8b9Kd70AodxUGGDwaAoeHEALw_wcB, MongoDB. "" "" <>(Sep. 2, 2021); Ruohomaki, T., Airaksinen, E., Huuska, P., Kesaniemi, O., Martikka, M., Suomisto, J., (2018) Smart City Platform Enabling Digital Twin, pp. 155-161. , "" 9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018 - Procreeedings, Institute of Electrical and Electronics Engineers Inc; Silva, B.N., Khan, M., Han, K., Internet of Things: A Comprehensive Review of Enabling Technologies, Architecture, and Challenges (2018) IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India), , "" Taylor and Francis Ltd; Silva, B.N., Khan, M., Han, K., Integration of Big Data analytics embedded smart city architecture with RESTful web of things for efficient service provision and energy management (2020) Future Generation Computer Systems, 107, pp. 975-987. , "" Elsevier B.V; Zhao, D., Thakur, N., Chen, J., Optimal Design of Energy Storage System to Buffer Charging Infrastructure in Smart Cities (2020) Journal of Management in Engineering, 36 (2). , "" American Society of Civil Engineers (ASCE), 04019048","Ham, Y.; Dept. of Construction Science, United States; email: yham@tamu.edu","Jazizadeh F.Shealy T.Garvin M.J.","Construction Institute (CI) of the American Society of Civil Engineers (ASCE);Construction Research Council;Virginia Polytechnic Institute and State University","American Society of Civil Engineers (ASCE)","Construction Research Congress 2022: Computer Applications, Automation, and Data Analytics, CRC 2022","9 March 2022 through 12 March 2022",,177800,,9780784483961,,,"English","Constr. Res. Congr.: Comput. Appl., Autom., Data Anal. - Sel. Pap. Constr. Res. Congr.",Conference Paper,"Final","",Scopus,2-s2.0-85128979097
"Shotz H., Birdsall J.","57225078822;57644239800;","A Glimpse into the Near Future – Digital Twins and the Internet of Things",2022,"Sustainable Civil Infrastructures",,,,"425","438",,,"10.1007/978-3-030-79801-7_31","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128890381&doi=10.1007%2f978-3-030-79801-7_31&partnerID=40&md5=bc548f595c9e14ca5ee5132919d6aad0","Parsons, Centreville, United States; Parsons, Dubai, United Arab Emirates","Shotz, H., Parsons, Centreville, United States; Birdsall, J., Parsons, Dubai, United Arab Emirates","A glimpse into the near future, digital twins are an exciting and innovative technology designed to maximize existing road assets. Digital twins offer organizations the opportunity to deliver leading-edge asset management capabilities and integrated analytics by unlocking the wealth of information stored in existing pavement and bridge management systems. These capabilities offer immense benefit to transportation agencies by ensuring efficient, reliable, and available asset information critical to operations and planning. This paper will offer a new perspective of capital investment strategy and maintenance operations. It will present a model for organizations to move beyond standard maintenance and operations and into a larger framework based on the Internet of Things (IOT). This will provide additional real-time intelligence about assets to enhance operations and maximize investments, while optimizing the total cost of ownership and state of good repair. Topics addressed in this paper include: A discussion of how organizations are preparing to deploy digital twins within an overall asset lifecycle management framework.Use case examples of how organizations are planning to deploy digital twins by integrating source systems of record (SSOR) such as pavement and bridge management systems.An overview of the services, products, and technologies employed.The latest thinking on asset management, including human factors, cyber and physical security, big data analytics, artificial intelligence (AI), and machine learning (ML). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Asset management; Bridge management; Digital twins; Internet of Things; Paving management","Artificial intelligence; Bridges; Data Analytics; Information management; Internet of things; Investments; Life cycle; Pavements; Assets management; Bridge management; Bridge management system; Capital investment; Innovative technology; Management capabilities; Pavement management systems; Paving management; Transportation agencies; Wealth of information; Asset management",,,,,,"The authors acknowledge the foresight of Bruce Irwin at Parsons, for introducing us to the potential of digital twins and authoring our initial Asset Lifecycle Conceptual Model diagram that provides the conceptual framework for the advanced application of technology into asset lifecycle management. A special recognition is also due to the deep thought contributions of David Armstrong at Bentley Systems for sharing his wonderful vision of the digital future, sweetened with just the right amount of wit and cynicism to be believable.",,,,,,,,,,"Armstrong, D., Asset Management, Machine Learning, and the Duality of Oreo Cookies (2021) Unpublished; (2021), https://www.autodesk.com/solutions/digital-twin/architecture-engineering-construction; (2021), https://www.bentley.com/en/products/product-line/digital-twins; Asset Lifecycle Management: 6 Stages and Best Practices, , https://blog.cohesivesolutions.com/asset-lifecycle-management-stages, Cohesive Solutions; (2012), https://www.esri.com/arcgis-blog/products/arcgis/aec/gis-foundation-for-digital-twins/; (2021), https://www.johnsoncontrols.com/openblue/openblue-digital-twin; (2021), https://azure.microsoft.com/en-ca/services/digital-twins/; O’Hanlon, T., (2021) Understanding Asset Management and Lifecycle, , https://www.linkedin.com/pulse/understanding-asset-management-life-cycle-terrence-ohanlon/; (1998) Society of Automotive Engineers. SAE JA1011 Evaluation Criteria for Reliability-Centered Maintenance (RCM) Processes; (2021), https://www.willowinc.com/company/about/","Shotz, H.; ParsonsUnited States; email: howard.shotz@parsons.com","Akhnoukh A.Kaloush K.Elabyad M.Halleman B.Erian N.Enmon II S.Henry C.",,"Springer Science and Business Media B.V.","18th International Road Federation World Meeting and Exhibition, 2021","7 November 2021 through 10 November 2021",,277099,23663405,9783030798000,,,"English","Sustain. Civil Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85128890381
"Akimov L., De Mei K., de Martino di Montegiordano D., Lvov V., Osipov N., Ostrovaia A., Krasnozhen S., Badenko V., Terleev V.","57189364268;57257356300;57256359300;57257209600;57257064200;57189371754;57224906178;6508255564;6506101828;","The Material-Efficient Design of Bridges with the Use of FRP",2022,"Lecture Notes in Networks and Systems","403 LNNS",,,"1101","1111",,,"10.1007/978-3-030-96383-5_123","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127273496&doi=10.1007%2f978-3-030-96383-5_123&partnerID=40&md5=a6b79a8ee667801aff363e27490eae5c","Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29, St. Petersburg, 195251, Russian Federation; Politecnico di Milano, 32 Piazza Leonardo da Vinci, Milan, 20133, Italy; Stahl + Weiß PartGmbB, Basler Straße 55, Freiburg im Breisgau, 79100, Germany; Maffeis Engineering, 26 via Mignano, Solagna, 36020, Italy; CJSC«Bureau of Technics-Project», 25ZH Tsvetochnaya Street, St. Petersburg, 196084, Russian Federation; JSC “LENMORNIIPROEKT”, 3 Mezhevoy Kanal Street, St. Petersburg, 198035, Russian Federation; St. Petersburg State Agrarian University, Peterburgskoe Shosse, 2, Pushkin, St. Petersburg, 196601, Russian Federation","Akimov, L., Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29, St. Petersburg, 195251, Russian Federation, Politecnico di Milano, 32 Piazza Leonardo da Vinci, Milan, 20133, Italy; De Mei, K., Politecnico di Milano, 32 Piazza Leonardo da Vinci, Milan, 20133, Italy, Stahl + Weiß PartGmbB, Basler Straße 55, Freiburg im Breisgau, 79100, Germany; de Martino di Montegiordano, D., Politecnico di Milano, 32 Piazza Leonardo da Vinci, Milan, 20133, Italy, Maffeis Engineering, 26 via Mignano, Solagna, 36020, Italy; Lvov, V., Politecnico di Milano, 32 Piazza Leonardo da Vinci, Milan, 20133, Italy, CJSC«Bureau of Technics-Project», 25ZH Tsvetochnaya Street, St. Petersburg, 196084, Russian Federation; Osipov, N., JSC “LENMORNIIPROEKT”, 3 Mezhevoy Kanal Street, St. Petersburg, 198035, Russian Federation; Ostrovaia, A., Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29, St. Petersburg, 195251, Russian Federation; Krasnozhen, S., Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29, St. Petersburg, 195251, Russian Federation; Badenko, V., Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29, St. Petersburg, 195251, Russian Federation; Terleev, V., Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29, St. Petersburg, 195251, Russian Federation, St. Petersburg State Agrarian University, Peterburgskoe Shosse, 2, Pushkin, St. Petersburg, 196601, Russian Federation","The following paper discusses the design of the pedestrian bridge made of laminar material FRP (fiber reinforced polymer). FRP has a potential to be consistently used in the construction sector, however, to be used as a bearing material in the responsible structures, its behavior has to be properly analyzed. In our article we try to address the question of FRP performance in terms of mechanical behavior for the use in pedestrian bridges. We have created 4 options of the FRP bridge deck. Each option consists of 4 layers of FRP with different angle or rotation of the plies: 1 unidirectional and 3 cross-plied quasi-isotropic. With the use of ABAQUS software we have tested the mechanical behavior of each option comparing them to the reinforced concrete deck. Each FRP showed better mechanical behavior in terms of vertical deflection by 20% on average in comparison to the reinforced concrete deck. Additionally, we have checked the von Mises stress in the material and normal stresses distribution along the deck thickness. The digital monitoring of the structural elements made of innovative construction materials is as well discussed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.","ABAQUS; Bridges design; Digital twin; FRP; Laminar material; Material-efficient design",,,,,,"075-15-2020-934; Ministry of Education and Science of the Russian Federation, Minobrnauka","The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation as part of World-class Research Center program: Advanced Digital Technologies (contract No. 075-15-2020-934 dated 17.11.2020). Additionally, authors express great gratitude to prof. Carlo Poggi and Elisa Bertolesi for the provided support during the course Earthquake Resistant Design and Structural Rehabilitation of Buildings in Politecnico di Milano that gave us the occasion to conduct this research.","Acknowledgements. The research is partially funded by the Ministry of Science and Higher Education of the Russian Federation as part of World-class Research Center program: Advanced Digital Technologies (contract No. 075-15-2020-934 dated 17.11.2020).",,,,,,,,,"Belussi, L., A review of performance of zero energy buildings and energy efficiency solutions (2019) J. Build. Eng., 25; Yoon, Y.-C., Kim, K.-H., Lee, S.-H., Yeo, D., Sustainable design for reinforced concrete columns through embodied energy and CO2 emission optimization (2018) Energy Build, 174, pp. 44-53; Frolov, A., Chumadova, L., Cherkashin, A., Akimov, L., Prospects of use and impact of nanoparticles on the properties of high-strength concrete (2014) Appl. Mech. Mater., 584-586, pp. 1416-1424; Frolov, A., An impact of carbon nanostructured additives on the kinetics of cement hydration (2015) Appl. Mech. Mater., 725-726, pp. 425-430; Proskurovskis, A., The distribution of the main stresses in the section of the ceramsite concrete fixed formwork block with four cells (2020) E3S Web Conf, 164; Zinkevich, I., Innovative methods of concrete dams’ inspection (2019) IOP Conf. Ser. Earth Environ. Sci, 403; Akimov, L., Ilenko, N., Mizharev, R., Cherkashin, A., Vatin, N., Chumadova, L., Composite concrete modifier CM 02–10 and its impact on the strength characteristics of concrete (2016) MATEC Web Conf, 53; Akimov, L., Ilenko, N., Mizharev, R., Cherkashin, A., Vatin, N., Chumadova, L., Influence of plasticizing and siliceous additives on the strength characteristics of concrete (2015) Appl. Mech. Mater., 725-726, pp. 461-468; Siddika, A., Mamun, M.A.A., Alyousef, R., Amran, Y.H.M., Strengthening of reinforced concrete beams by using fiber-reinforced polymer composites: A review (2019) J. Build. Eng., 25; Pellegrino, C., Vasic, M., Assessment of design procedures for the use of externally bonded FRP composites in shear strengthening of reinforced concrete beams (2013) Compos. B Eng., 45, pp. 727-741; Bakis, C.E., Fiber-reinforced polymer composites for construction—state-of-the-art review (2002) J. Compos. Constr., 6, pp. 73-87; Dehghan, A., Peterson, K., Shvarzman, A., Recycled glass fiber reinforced polymer additions to Portland cement concrete (2017) Constr. Build. Mater., 146, pp. 238-250; Hollaway, L.C., A review of the present and future utilisation of FRP composites in the civil infrastructure with reference to their important in-service properties (2010) Constr. Build. Mater., 24, pp. 2419-2445; Charalambidi, B.G., Rousakis, T.C., Karabinis, A.I., Analysis of the fatigue behavior of reinforced concrete beams strengthened in flexure with fiber reinforced polymer laminates (2016) Compos. B Eng., 96, pp. 69-78; Clyne, T.W., Hull, D., (2019) An Introduction to Composite Materials, , Cambridge University Press, London; Liu, C., Shi, Y., An improved analytical solution for process-induced residual stresses and deformations in flat composite laminates considering thermo-viscoelastic effects (2018) Materials, 11, p. 2506; Abaqus/Standard. https://www.3ds.com/products-services/simulia/products/abaqus/abaqus standard/. Accessed 14 Apr 2021; Von Mises Criterion (Maximum Distortion Energy Criterion) Strength (Mechanics) of Materials. https://www.engineersedge.com/material_science/von_mises.htm. Accessed 14 Apr 2021; Poluektov, R.A., Oparina, I.V., Terleev, V.V., Three methods for calculating soil water dynamics (2003) Russ. Meteorol. Hydrol., 11, pp. 61-67; Terleev, V., Modelling the hysteretic water retention capacity of soil for reclamation research as a part of underground development (2016) Proc. Eng., 165, pp. 1776-1783; Terleev, V., Hysteretic water-retention capacity of sandy soil (2017) Magaz. Civil Eng., 70, pp. 84-92; Terleev, V., Improved hydrophysical functions of the soil and their comparison with analogues by the Williams-Kloot test (2019) Adv. Intell. Syst. Comput., 983, pp. 449-461; Terleev, V., Predicting the scanning branches of hysteretic soil water-retention capacity with use of the method of mathematical modeling (2017) IOP Conf. Ser. Earth Environ. Sci., 90; Terleev, V., Hysteresis of the soil water-retention capacity: Estimating the scanning branches (2018) Magaz. Civil Eng., 77, pp. 141-148; Terleev, V., Five models of hysteretic water-retention capacity and their comparison for sandy soil (2018) MATEC Web Conf, 193; Andrianova, M.J., Vorobjev, K.V., Lednova, J.A., Chusov, A.N., A short-term model experiment of organic pollutants treatment with aquatic macrophytes in industrial and municipal waste waters (2014) Appl. Mech. Mater., 587-589, pp. 653-656; Chusov, A., Lednova, J., Shilin, M., Ecological assessment of dredging in the Eastern Gulf of Finland (2012) IEEE/OES Baltic Int. Symp. BALTIC, pp. 1-4. , 2012; Chusov, A.N., Bondarenko, E.A., Andrianova, M.J., Study of electric conductivity of urban stream water polluted with municipal effluents (2014) Appl. Mech. Mater., 641-642, pp. 1172-1175; Pilakoutas, K., Guadagnini, M., Neocleous, K., Matthys, S., Design guidelines for FRP reinforced concrete structures (2011) Proc. Inst. Civil Eng. Struct. Build., 164, pp. 255-263; Rasheed, A., San, O., Kvamsdal, T., Digital twin: Values, challenges and enablers from a modeling perspective (2020) IEEE Access, 8, pp. 21980-22012; Dunaieva, I., GIS services for agriculture monitoring and forecasting: Development concept (2019) Adv. Intell. Syst. Comput., 983, pp. 236-246; Osipov, A., Geoecological evaluation of the territory in the GIS environment in the preparation of information for land management design (2020) E3S Web Conf, 164","Akimov, L.; Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29, Russian Federation; email: Lukas.ak94@gmail.com","Manakov A.Edigarian A.",,"Springer Science and Business Media Deutschland GmbH","International Scientific Siberian Transport Forum, TransSiberia 2021","11 May 2021 through 14 May 2021",,275189,23673370,9783030963828,,,"English","Lect. Notes Networks Syst.",Conference Paper,"Final","",Scopus,2-s2.0-85127273496
"Cao Z., Zhou X., Hu H., Wang Z., Wen Y.","57706266500;57211683348;56121897100;55913248200;57486929900;","Toward a Systematic Survey for Carbon Neutral Data Centers",2022,"IEEE Communications Surveys and Tutorials","24","2",,"895","936",,,"10.1109/COMST.2022.3161275","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127077398&doi=10.1109%2fCOMST.2022.3161275&partnerID=40&md5=77e2810f61f74ffc7c5a6644a7162e90","Nanyang Technological University, School of Computer Science and Engineering, Singapore, 639798, Singapore; Beijing Institute of Technology, School of Information and Electronics, Beijing, 100081, China; Tsinghua University, Tsinghua Shenzhen International Graduate School, Beijing, 100084, China","Cao, Z., Nanyang Technological University, School of Computer Science and Engineering, Singapore, 639798, Singapore; Zhou, X., Nanyang Technological University, School of Computer Science and Engineering, Singapore, 639798, Singapore; Hu, H., Beijing Institute of Technology, School of Information and Electronics, Beijing, 100081, China; Wang, Z., Tsinghua University, Tsinghua Shenzhen International Graduate School, Beijing, 100084, China; Wen, Y., Nanyang Technological University, School of Computer Science and Engineering, Singapore, 639798, Singapore","Data centers are experiencing unprecedented growth as the fourth industrial revolution's supporting pillars and the engine for the future digitalized world. However, data centers are carbon-intensive enterprises due to their massive energy consumption, and it is estimated that data center industry will account for 8% of global carbon emissions by 2030. Meanwhile, both technological and policy instruments for reducing or even neutralizing data center carbon emissions have not been thoroughly investigated, despite the fact that several global cloud providers including Google and Facebook, have pledged to achieve carbon neutrality in their hyperscale data centers. To bridge this gap, this survey paper proposes a roadmap towards carbon-neutral data centers that takes into account both policy instruments and technological methodologies. We begin by presenting the carbon footprint of data centers, as well as some insights into the major sources of carbon emissions. Following that, carbon neutrality plans for major global cloud providers are discussed to summarize current industrial efforts in this direction. In what follows, we introduce the carbon market as a policy instrument to explain how to offset data center carbon emissions in a cost-efficient manner. On the technological front, we propose achieving carbon-neutral data centers by increasing renewable energy penetration, improving energy efficiency, and boosting energy circulation simultaneously. A comprehensive review of existing technologies on these three topics is elaborated subsequently. Based on this, a multi-pronged approach towards carbon neutrality is envisioned and a digital twin-powered industrial artificial intelligence (AI) framework is proposed to make this solution a reality. Furthermore, three key scientific challenges for putting such a framework in place are discussed. Finally, several applications for this framework are presented to demonstrate its enormous potential. © 1998-2012 IEEE.","Artificial intelligence; Carbon neutrality; Data center; Digital twin","Artificial intelligence; Carbon dioxide; Carbon footprint; Energy efficiency; Energy utilization; Green computing; Renewable energy resources; Surveys; Waste heat utilization; Artificial intelligence.; Carbon emissions; Carbon neutralities; Carbon neutrals; Datacenter; Global clouds; Optimisations; Policy instruments; Renewable energy source; Waste heat",,,,,,,,,,,,,,,,"Pore, M., Abbasi, Z., Gupta, S.K., Varsamopoulos, G., Techniques to achieve energy proportionality in data centers: A survey (2015) Handbook on Data Centers, pp. 109-162. , New York, NY, USA: Springer; Zhang, W., Wen, Y., Wong, Y.W., Toh, K.C., Chen, C.-H., Towards joint optimization over ICT and cooling systems in data centre: A survey (2016) IEEE Commun. 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"Steinbuch M., Oomen T., Vermeulen H.","7005859475;16178244600;7006272959;","Motion Control, Mechatronics Design, and Moore's Law",2022,"IEEJ Journal of Industry Applications","11","2",,"245","255",,,"10.1541/ieejjia.21006010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125564978&doi=10.1541%2fieejjia.21006010&partnerID=40&md5=32a9d1cc2c3384255a34f766a1698ca8","Eindhoven University of Technology, Control Systems Technology Group, Department of Mechanical Engineering, Eindhoven, Netherlands; ASML, Veldhoven, Netherlands","Steinbuch, M., Eindhoven University of Technology, Control Systems Technology Group, Department of Mechanical Engineering, Eindhoven, Netherlands; Oomen, T., Eindhoven University of Technology, Control Systems Technology Group, Department of Mechanical Engineering, Eindhoven, Netherlands; Vermeulen, H., Eindhoven University of Technology, Control Systems Technology Group, Department of Mechanical Engineering, Eindhoven, Netherlands, ASML, Veldhoven, Netherlands","Technology in a broad sense is driven by developments in semiconductor technology, particularly with respect to the computational power of devices and systems, as well as sensor technology. The progress of semiconductor technology has demonstrated an exponential curve since the middle of the previous century, representing Moore's Law. Consequently, it is of utmost importance to bridge the gaps between disciplines in the fields of control, automation, and robotics. Moreover, data-driven approaches need to be combined with model-based design. This will lead to new digital twinning and automated design approaches that provide major opportunities. Furthermore, this necessitates the redefinition of our university system. © 2022 The Institute of Electrical Engineers of Japan.",,"Semiconductor devices; Computational power; Control automation; Data-driven approach; Devices and systems; Exponential curves; Mechatronics designs; Model-based design; Moore Law; Semiconductor technology; Sensor technologies; Semiconductor device manufacture",,,,,,,,,,,,,,,,"Gholami, B., Haddad, W.M., Bailey, J.M., AI in the ICU: In the intensive care unit, artificial intelligence can keep watch (2018) IEEE Spectrum, 55 (10), pp. 31-35; van de Weijer, C., Steinbuch, M., (2020) Forward: The future of mobility, , DATO, Eindhoven, The Netherlands; Moore, G.E., Cramming more components onto integrated circuits (1965) Electronics, 38 (8), pp. 114-11; Moore, G.E., Progress in digital integrated circuits (1975) IEEE Int. Electron Devices Meeting, pp. 11-13; Jurvetson, S., Kurzweil, R., (2016) Updated picture of Moore's Law, , https://www.flickr.com/photos/jurvetson/31409423572/; Steinbuch, M., Mechatronics disrupted (2016) Mechatronic Futures, , P. Hehenberger and D. Bradley, editors, Springer; Oomen, T., Steinbuch, M., Model-based control for high-tech mechatronic systems (2020) Mechatronics and Robotics: New Trends and Challenges, , M. Indri and R. Oboe, editors, Taylor & Francis; Vermeulen, J.P.M.B., (2016) Mechatronica 2.0, Balanceren van complexiteit of componentniveau, Inaugural lecture (in Dutch), , Eindhoven University of Technology, Eindhoven, The Netherlands; (2000) International technology roadmap for semiconductors, ITRS, , editor; Bakshi, V., (2009) EUV lithography, , SPIE press; Mack, C., (2007) Fundamental principles of optical lithography, , Wiley-Interscience; Peijnenburg, T., Vermeulen, H., van Eijk, J., Magnetic bearing systems compared to conventional bearing systems (2006) Microelectronic Engineering, 83 (4), pp. 1372-1375; Vermeulen, H., Peijnenburg, T., Norg, M., Bauer, M.G., van Eijk, J., Isolated machine architectures to enable nanometer-level positioning performance in semiconductor applications (2006) Proc. of the Mechatronics Conference MX2006, pp. 3908-3913. , Malvern, PA; van Schoot, J., van Setten, E., Troost, K., Lok, S., Peeters, R., Stoeldraijer, J., Benschop, J., Kaiser, W.M., High-na euv lithography exposure tool program progress (2020) SPIE Advanced Lithography, , San Jose, USA; de Bruyn, B.J.H., (2018) Superconducting linear motors for high-dynamic applications, , PhD Thesis, Eindhoven University of Technology, Eindhoven, The Netherlands; Koolmees, H.B., de Bruyn, B.J.H., Vermeulen, J.P.M.B., Jansen, J.W., Lomonova, E.A., On the performance potential of superconducting linear and planar motors (2017) Proc. of the 17th Euspen International Conference, pp. 3908-3913. , Hannover, Germany; Koolmees, H.B., Vermeulen, J.P.M.B., (2017) High stiffness cryogenic support without thermal conductance, , Patent no. WO 2019072529 A1; Koolmees, H.B., Vermeulen, J.P.M.B., High stiffness fixation and thermal insulation in a superconducting planar motor (2018) Proc. of the 18th Euspen International Conference, pp. 3908-3913. , Venice, Italy; Koolmees, H.B., (2020) A superconducting magnet plate for a planar motor application, , PhD Thesis, Eindhoven University of Technology, Eindhoven, The Netherlands; Steur, M.M.A., (2017) Design of an active wafer clamp for wafer machines, , PhD Thesis, Eindhoven University of Technology, Eindhoven, The Netherlands; Valentin, C.L., Vermeulen, J.P.M.B., van Bree, B.C.T., Munnig-Schmidt, R.H., Experimental validation of a piezoelectrically driven photomask curvature manipulator (2012) Proc. of the 12th Euspen International Conference, , Stockholm, Sweden; Valentin, C.L., (2013) Curvature manipulation of photomasks enhancing the imaging performance of immersion lithography equipment, , PhD Thesis, Delft University of Technology, Delft, The Netherlands; van den Hurk, D.P.M., (2020) Active wafer clamp control of wafer heating in extreme ultraviolet lithography, , PhD Thesis, Eindhoven University of Technology, Eindhoven, The Netherlands; Muganda, J.M., Jansen, B., Homburg, E., van de Burgt, Y., den Toonder, J., Influence function measurement technique using the direct and indirect piezoelectric effect for surface shape control in adaptive systems (2021) IEEE Transactions on Automation Science and Engineering, pp. 1-9; Hermanussen, S.J., Vermeulen, J.P.M.B., Heertjes, M.F., Habets, M.B.I., Conformal wafer loading (2020) Mikroniek, (6), pp. 44-48; Hermanussen, S.J., Vermeulen, J.P.M.B., Heertjes, M.F., Habets, M.B.I., Slip-free wafer chucking using an actuated wafer clamp (2020) Proc. of the ICCMA, Delft, The Netherlands; Gawronski, W.K., (2004) Advanced Structural Dynamics and Active Control of Structures, , Springer, New York, NY, USA; Abramovitch, D.Y., Trying to keep it real: 25 years of trying to get the stuff I learned in grad school to work on mechatronic systems (2015) Proc. 2015 Multi-conf. Syst. Contr, pp. 223-250. , Syndey, Australia; van de Wal, M., van Baars, G., Sperling, F., Bosgra, O., Multivariable H∞/μ feedback control design for high-precision wafer stage motion (2002) Contr. Eng. Prac, 10 (7), pp. 739-755; Oomen, T., Control for precision mechatronics (2020) Encyclopedia of Systems and Control, , J. Baillieul and T. Samad, editors, Springer Nature, 2nd edition; Ohnishi, K., Shibata, M., Murakami, T., Motion control for advanced mechatronics (1996) IEEE/ASME Trans. Mech, 1 (1), pp. 56-67; Lee, H.S., Tomizuka, M., Robust motion controller design for highaccuracy positioning systems (1996) IEEE Trans. Ind. Electr, 43 (1), pp. 48-55; Steinbuch, M., Norg, M.L., Advanced motion control: An industrial perspective (1998) Eur. J. Contr, 4 (4), pp. 278-293; Boerlage, M., Tousain, R., Steinbuch, M., Reference trajectory relevant jerk derivative feedforward control for motion systems (2004) Proc. 2004 Americ. Contr. Conf, pp. 4843-4848. , Boston, MA USA; Oomen, T., Advanced motion control for precision mechatronics: Control, identification, and learning of complex systems (2018) IEEJ Transactions on Industry Applications, 7 (2), pp. 127-140; van Herpen, R., Oomen, T., Kikken, E., van de Wal, M., Aangenent, W., Steinbuch, M., Exploiting additional actuators and sensors for nanopositioning robust motion control (2014) Proc. 2014 Americ. Contr. Conf, pp. 984-990. , Portland, OR, USA; Vermeulen, J.P.M.B., Hendriks, B., Rosielle, N., van Eijk, J., Effective damping in mirror mounts (2005) Proc. of the ASPE 2005 Annual meeting, pp. 242-245. , Norfolk, VA; Verbaan, C.A.M., (2015) Robust mass dampers for bandwidth increase of motion stages, , PhD Thesis, Eindhoven University of Technology, Eindhoven, The Netherlands; Vermeulen, J.P.M.B., van Lieshout, R., Butler, H., van de Wal, M.M.J., Aangenent, W., Heintze, H., Beerens, R., Donders, S., 450mm lithography challenges (2012) Proc. of the 1st DSPE Conference on Precision Mechatronics, pp. 242-245. , Deurne, Netherlands; van deWal, M.M.J., Aangenent, W., Vermeulen, J.P.M.B., 450mm lithography challenges (2014) Proc. of the 2nd DSPE Conference on Precision Mechatronics, pp. 242-245. , St. Michielsgestel, Netherlands; Oomen, T., van Herpen, R., Quist, S., van de Wal, M., Bosgra, O., Steinbuch, M., Connecting system identification and robust control for nextgeneration motion control of a wafer stage (2014) IEEE Trans. Contr. Syst. Techn, 22 (1), pp. 102-118; Voorhoeve, R., de Rozario, R., Aangenent, W., Oomen, T., Identifying position-dependent mechanical systems: A modal approach with applications to wafer stage control (2021) IEEE Trans. Contr. Syst. Techn, 29 (1), pp. 194-206; Doyle, J.C., Glover, K., Khargonekar, P.P., Francis, B.A., State-space solutions to standard H2 and H∞ control problems (1989) IEEE Trans. Automat. Contr, 34 (8), pp. 831-947; Freudenberg, J.S., Hollot, C.V., Middleton, R.H., Toochinda, V., Fundamental design limitations of the general control configuration (2003) IEEE Trans. Automat. Contr, 48 (8), pp. 1355-1370; van Zundert, J., Oomen, T., On inversion-based approaches for feedforward and ILC (2018) Mechatronics, (50), pp. 282-291; Fujimoto, H., Hori, Y., Kawamura, A., Perfect tracking control based on multirate feedforward control with generalized sampling periods (2001) IEEE Trans. Ind. Electr, 48 (3), pp. 636-644; van Zundert, J., Luijten, F., Oomen, T., Exact and causal inversion of nonminimum-phase systems: A squaring-down approach for overactuated systems (2019) IEEE/ASME Trans. Mech, 24 (6), pp. 2953-2963; Evers, E., (2021) Identification and Active Thermomechanical Control in Precision Mechatronics, , PhD thesis, Eindhoven University of Technology; Vermeulen, J.P.M.B., Zwol, P., Koevoets, M., Aangenent, W., Thermal challenges for next generation euv lithography (2016) Euspen SIG conference Thermal Issues, , Prague, Czech Republic; Schoukens, J., Vandersteen, G., Barbé, K., Pintelon, R., Nonparametric preprocessing in system identification: A powerful tool (2009) Eur. J. Contr, (3-4), pp. 260-274; Evers, E., Tuijl, N., Lamers, R., de Jager, B., Oomen, T., Identifying thermal dynamics for precision motion control (2020) Mechatronics, (70), p. 102401; Evers, E., van deWal, M., Oomen, T., Beyond decentralized wafer/reticle stage motion control design: A double-Youla approach for enhancing synchronized motion (2019) Contr. Eng. Prac, (83), pp. 21-32; Tay, T.T., Mareels, I.M.Y., Moore, J.B., (1997) High Performance Control, , Birkhauser, Boston, MA, USA; van Zundert, J., Oomen, T., Aangenent, W., Verhaegh, J., Antunes, D., Heemels, M., Beyond performance/cost tradeoffs in motion control: A multirate feedforward design with application to a dual-stage wafer system (2020) IEEE Trans. Contr. Syst. Techn, 28 (2), pp. 448-461; Oomen, T., Learning in machines (2018) Mikroniek, (6), pp. 5-11; Recht, B., A tour of reinforcement learning: The view from continuous control (2019) Annual Review of Control, Robotics, and Autonomous Systems, (2), pp. 253-279; Rasmussen, C.E., Williams, C.K.I., Gaussian Processes for Machine Learning (2006) Massachusetts Institute of Technology; Classens, K., Oomen, T., Heemels, W., van deWijdeven, J., van deWal, M., Aangenent, W., Digital twins in control: From fault detection to predictive maintenance in precision mechatronics (2020) In First EUSPEN Special Interest Group Meeting on Precision Motions Systems & Control; Gevers, M., Towards a joint design of identification and control? (1993) Essays on Control: Perspectives in the Theory and its Applications, pp. 111-151. , H.L. Trentelman and J.C. Willems, editors, chapter 5, Birkhäuser, Boston, MA, USA; Classens, K., Heemels, M., Oomen, T., A closed-loop perspective on fault detection for precision motion control: With application to an overactuated system (2021) IEEE Int. Conf. Mech, , Tokyo, Japan; Classens, K., Heemels, W., Oomen, T., Closed-loop aspects in mimo fault diagnosis with application to precision mechatronics (2021) Proc. 2021 Americ. Contr. Conf., New Orleans, LA, USA; Pawłowski, K., The 'fourth generation university' as a creator of the local and regional development (2009) Higher Education in Europe, 34 (1), pp. 51-64; Lukovics, M., Zuti, B., Successful Universities towards the Improvement of Regional Competitiveness: 'Fourth Generation' Universities (2017) SSRN, , editors; Oztel, H., Fourth generation university: Co-creating a sustainable future (2020) Quality Education. Encyclopedia of the UN Sustainable Development Goals, , W. Leal Filho, A.A., B.L., O.P., and T. Wall, editors, Springer; Kiemen, M., (2015) An interdisciplinary study on novelty regulation to produce radical change, , PhD Thesis, Free University of Brussels, Brussels, Belgium; Wissema, J., (2009) Towards the third generation university: Managing the university in transition, , Edward Elgar, Cheltenham, UK","Steinbuch, M.; Eindhoven University of Technology, Netherlands; email: m.steinbuch@tue.nl",,,"Institute of Electrical Engineers of Japan",,,,,21871094,,,,"English","IEEJ J. Ind. Appl.",Article,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85125564978
"Koddenbrock M., Heimann J., Herfert D., Pehe J., Wargulski L.","57192205263;57205287274;56707386100;57405570600;57405570700;","WaveImage Bridges the Gap Between Measurement and Simulation. An Application Example of How to Create a Modal Digital Twin Using FE Model Updating",2022,"Conference Proceedings of the Society for Experimental Mechanics Series",,,,"39","48",,,"10.1007/978-3-030-77348-9_6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122520101&doi=10.1007%2f978-3-030-77348-9_6&partnerID=40&md5=ae53d2e394e8ee9095b4a9cd9dd926e5","Society for the Advancement of Applied Computer Science, Berlin, Germany; gfai tech GmbH, Berlin, Germany; GFaI e.V., Berlin, Germany","Koddenbrock, M., Society for the Advancement of Applied Computer Science, Berlin, Germany; Heimann, J., Society for the Advancement of Applied Computer Science, Berlin, Germany; Herfert, D., Society for the Advancement of Applied Computer Science, Berlin, Germany; Pehe, J., gfai tech GmbH, Berlin, Germany; Wargulski, L., GFaI e.V., Berlin, Germany","In this paper, a best-practice example of a digital twin is presented. For this purpose, the authors choose a test model of a machine frame with a rotating motor to simulate a situation from an industrial context. During the manufacturing process, this type of frame undergoes excitation from the imbalance forces of a single-speed drive. In order to avoid a failure of a structure when operating conditions change, it is important to be able to simulate the impact accurately. This accurate simulation requires an adequate digital twin. In this context, a good digital twin is one that reproduces the modal characteristics of the structure properly. In this paper, the modal parameters of the machine frame are determined by experimental modal analysis. Afterward, model updating is performed to approximate the simulated modal parameters to the ones obtained from the real structure experiment. The full process is executed using the software WaveImage, which provides an easy-to-use modular kit for experimental modal analysis, finite element analysis, and finite element model updating. © 2022, The Society for Experimental Mechanics, Inc.","Digital twin; Experimental modal analysis; Finite element simulation; Model updating","Failure (mechanical); Modal analysis; Structural dynamics; Application examples; Best practices; Experimental modal analysis; FE model updating; Finite elements simulation; Measurement and simulation; Modal parameters; Model updating; Rotating motors; Test models; Finite element method",,,,,,,,,,,,,,,,"Leurs, W., Deblauwe, F., Lembregts, F., (1993) Modal Parameter Estimation Based on Complex Mode Indicator Functions; Dong, X., Wang, Y., (2018) Formulation and Optimization Algorithm Comparison for the FE Model Updating of Large-Scale Structures; Gollnick, M., Herfert, D., Heimann, J.: 9. Automatic modal parameter identification with methods of artificial intelligence (2021) Topics in Modal Analysis & Testing, Vol. 8. Springer International Publishing; Allemang, R., Brown, D., (2006) A Complete Review of the Complex Mode Indicator Function (CMIF) with Applications; Ovako Material data sheet S235JR: https://steelnavigator.ovako.com/steel-grades/s235/pdf?variantIDs=701","Herfert, D.; Society for the Advancement of Applied Computer ScienceGermany; email: herfert@gfai.de","Mao Z.",,"Springer","39th IMAC, A Conference and Exposition on Structural Dynamics, 2021","8 February 2021 through 11 February 2021",,264509,21915644,9783030773472,,,"English","Conf. Proc. Soc. Exp. Mech. Ser.",Conference Paper,"Final","",Scopus,2-s2.0-85122520101
"Zhou C., Xiao D., Hu J., Yang Y., Li B., Hu S., Demartino C., Butala M.","57388653500;57388333500;57388333600;57388494800;57077267800;56043235300;56469484100;11241353000;","An Example of Digital Twins for Bridge Monitoring and Maintenance: Preliminary Results",2022,"Lecture Notes in Civil Engineering","200 LNCE",,,"1134","1143",,,"10.1007/978-3-030-91877-4_129","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121926758&doi=10.1007%2f978-3-030-91877-4_129&partnerID=40&md5=d2aaa71452c7fc698d026da0c8bde092","Zhejiang University, University of Illinois at Urbana Champaign Institute, Zhejiang, Haining, China; Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL  61801, United States; Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL  61801, United States","Zhou, C., Zhejiang University, University of Illinois at Urbana Champaign Institute, Zhejiang, Haining, China; Xiao, D., Zhejiang University, University of Illinois at Urbana Champaign Institute, Zhejiang, Haining, China; Hu, J., Zhejiang University, University of Illinois at Urbana Champaign Institute, Zhejiang, Haining, China; Yang, Y., Zhejiang University, University of Illinois at Urbana Champaign Institute, Zhejiang, Haining, China; Li, B., Zhejiang University, University of Illinois at Urbana Champaign Institute, Zhejiang, Haining, China; Hu, S., Zhejiang University, University of Illinois at Urbana Champaign Institute, Zhejiang, Haining, China, Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL  61801, United States; Demartino, C., Zhejiang University, University of Illinois at Urbana Champaign Institute, Zhejiang, Haining, China, Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL  61801, United States; Butala, M., Zhejiang University, University of Illinois at Urbana Champaign Institute, Zhejiang, Haining, China, Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL  61801, United States","In this study, we propose a digital twin pilot study for bridge monitoring and maintenance. In particular, an infrastructure management framework using UAV and surveillance cameras, and accelerometers-based digital twins is proposed to perform long-term and non-interruptive monitoring. Real-world monitoring data are obtained through an experimental test performed on the Juanhu bridge (Haning, Zhejiang, China). Traffic flow and accelerometer data of the tested bridge were measured. The digital twin model of the bridge is created as a real-time Finite Element model in OpenSees. The FE model geometry is produced using a 3D photogrammetric reconstruction, and its dynamic properties are updated based on Bayesian modal identification. The traffic flow information on the bridge is processed through computer vision techniques using the video footage from the UAV and surveillance cameras. The object detection algorithm YOLO and tracking algorithm DeepSORT are used to derive the time-space diagrams. These elements operate in tandem with the accelerometer data and the digital twin FE model to acquire a preliminary vehicle loading estimation. The results are presented in this study and showcase the feasibility of the proposed digital twin framework for bridge monitoring and maintenance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Computer vision; Digital twin; Infrastructure network; Management; Traffic loads","Aircraft detection; Cameras; Maintenance; Monitoring; Network management; Object detection; Security systems; Three dimensional computer graphics; Unmanned aerial vehicles (UAV); Accelerometer data; Bridge monitoring; Bridges maintenance; FE model; Infrastructure managements; Infrastructure networks; Management frameworks; Pilot studies; Surveillance cameras; Traffic loads; Computer vision",,,,,"DREMES202001; Zhejiang University, ZJU","Acknowledgements. This work was supported by ZJU-UIUC Joint Research Center Project No. DREMES202001, funded by Zhejiang University. The work was led by Principal Supervisors Simon Hu, Binbin Li, Mark Butala and Cristoforo Demartino. The authors acknowledge Zihan Liao (PhD student of ZJUI) and Wei Zhu (master student of ZJUI) for the support provided during the dynamic monitoring of the bridge.",,,,,,,,,,"Jones, D., Snider, C., Nassehi, A., Yon, J., Hicks, B., Characterising the digital twin: A systematic literature review (2020) CIRP J Manuf Sci Technol, 29 (A), pp. 36-52; Mordini, A., Savov, K., Wenzel, H., Damage detection on stay cables using an open source-based framework for finite element model updating (2008) Struct Health Monit, 7 (2), pp. 91-102; Kang, J.S., Chung, K., Hong, E.J., Multimedia knowledge-based bridge health monitoring using digital twin (2021) Multimed Tools Appl, pp. 1-16; Dang, N., Shim, C., Bridge assessment for PSC girder bridge using digital twins model (2020) CIGOS 2019. LNCE, 54. , https://doi.org/10.1007/978-981-15-0802-8_199, Ha-Minh C, Dao D, Benboudjema F, Derrible S, Huynh D, Tang A (eds.), vol, Springer, Singapore; Haining government. http://www.haining.gov.cn/; Redmon, J., Divvala, S., Girshick, R., Farhadi, A., You only look once: unified, real-time object detection (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788. , pp; Wojke, N., Bewley, A., Paulus, D., Simple online and realtime tracking with a deep association metric (2017) 2017 IEEE International Conference on Image Processing, pp. 3645-3649. , pp; Mikel Brostrom. https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch. Accessed 30 Mar 2021; Hilal, M.A., Zibdeh, H.S., Vibration analysis of beams with general boundary conditions traversed by a moving force (2000) J Sound Vib, 229 (2), pp. 377-388; Ricciardelli, F., Demartino, C., Design of footbridges against pedestrian-induced vibrations (2016) J Bridg Eng, 21 (8); Demartino, C., (2018) Avossa AM, , Ricciardelli F, Deterministic and probabilistic serviceability assessment of footbridge vibrations due to a single walker crossing. Shock Vib; McKenna, F., OpenSees: A framework for earthquake engineering simulation (2011) Comput Sci Eng, 13 (4), pp. 58-66; Liu, Z., He, Q.B., Li, Z.W., Peng, Z., Vision-based moving mass detection by time-varying structure vibration monitoring (2020) IEEE Sens J, 99, p. 1; Li, B., Au, S.-K., An expectation-maximization algorithm for Bayesian opera-tional modal analysis with multiple (Possibly close) modes (2019) Mech Syst Signal Process, 132, pp. 490-511","Demartino, C.; Department of Civil and Environmental Engineering, United States; email: cristoforodemartino@intl.zju.edu.cn","Pellegrino C.Faleschini F.Zanini M.A.Matos J.C.Casas J.R.Strauss A.",,"Springer Science and Business Media Deutschland GmbH","1st Conference of the European Association on Quality Control of Bridges and Structures, EUROSTRUCT 2021","29 August 2021 through 1 September 2021",,269849,23662557,9783030918767,,,"English","Lect. Notes Civ. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85121926758
"Ye C., Kuok S.-C., Butler L.J., Middleton C.R.","57216481422;36015370900;55795448200;7005340597;","Implementing bridge model updating for operation and maintenance purposes: examination based on UK practitioners’ views",2022,"Structure and Infrastructure Engineering","18","12",,"1638","1657",,,"10.1080/15732479.2021.1914115","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107481456&doi=10.1080%2f15732479.2021.1914115&partnerID=40&md5=ea0c2448c705890bb6cb03482005b338","Civil Engineering Division, Department of Engineering, University of Cambridge, Cambridge, United Kingdom; State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macau; Department of Civil Engineering, Lassonde School of Engineering, York University, Toronto, Canada","Ye, C., Civil Engineering Division, Department of Engineering, University of Cambridge, Cambridge, United Kingdom; Kuok, S.-C., Civil Engineering Division, Department of Engineering, University of Cambridge, Cambridge, United Kingdom, State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macau; Butler, L.J., Department of Civil Engineering, Lassonde School of Engineering, York University, Toronto, Canada; Middleton, C.R., Civil Engineering Division, Department of Engineering, University of Cambridge, Cambridge, United Kingdom","There has been a vision of creating bridge digital twins as virtual simulation models of bridge assets to facilitate remote management. Bridge model updating is one digital twin technology which can enable the continuous updating of the structural model as new monitoring data is collected. This paper examines why there is currently little industry uptake of monitoring, modelling and model updating for the operation and maintenance of bridges despite over two decades of research in these fields. The study analyses the findings from a series of semi-structured industry interviews with expert bridge professionals in the U.K. and from an extensive literature survey of bridge model updating studies to examine the disconnects between research and practice and the practical issues of implementing bridge model updating. In particular, the study found that localised damage resulting in local reduction in structural stiffness, a key assumption made in the majority of research, is subject to question by practitioners as many common types of bridge damage may not induce noticeable change in structural stiffness that existing model updating techniques would identify. Key recommendations for future research are proposed to drive adoption of bridge monitoring, modelling and model updating and thus realise their industrial value. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","Bridge operation and maintenance; digital twin technology; industry practice; structural health monitoring; structural model updating","Bridges; Digital storage; Industrial research; Maintenance; Stiffness; Bridge model; Bridge operation and maintenance; Digital twin technology; Industry practices; Model updating; Monitoring models; Operations and maintenance; Structural model updating; Structural stiffness; Virtual simulation models; Structural health monitoring",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/L016095/1","The authors would like to thank the 19 bridge professionals for participating in the industry facing interviews. The first author would like to thank the EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment (EPSRC grant reference number EP/L016095/1) for providing travel fund for the interviews.",,,,,,,,,,"(2019), https://artbabridgereport.org/, 2019 Bridge Report. Retrieved from; Ashraf, M., Gardner, L., Nethercot, D.A., Finite element modelling of structural stainless steel cross-sections (2006) Thin-Walled Structures, 44 (10), pp. 1048-1062; Baker, H., Moncaster, A., Al-Tabbaa, A., Decision-making for the demolition or adaptation of buildings (2017) Proceedings of the Institution of Civil Engineers - Forensic Engineering, 170 (3), pp. 144-156; Beck, J.L., Katafygiotis, L.S., Updating models and their uncertainties. 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I: Modeling and influence line analysis (2015) Journal of Bridge Engineering, 20 (10), p. 04014112; Živanović, S., Pavic, A., Reynolds, P., Finite element modelling and updating of a lively footbridge: The complete process (2007) Journal of Sound and Vibration, 301 (1-2), pp. 126-145","Ye, C.; Civil Engineering Division, United Kingdom; email: cy273@cam.ac.uk",,,"Taylor and Francis Ltd.",,,,,15732479,,,,"English","Struct. Infrastructure Eng.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85107481456
"Dubey A.C., Subramanian A.V., Jagadeesh Kumar V.","57192594272;57202419355;57200261313;","Steering model identification and control design of autonomous ship: a complete experimental study",2022,"Ships and Offshore Structures","17","5",,"992","1004",,,"10.1080/17445302.2021.1889193","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102454140&doi=10.1080%2f17445302.2021.1889193&partnerID=40&md5=ca61ff479f363a6fe0e8ed3cff068289","Department of Ocean Engineering, IIT Madras, Chennai, India; Department of Electrical Engineering, IIT Madras, Chennai, India","Dubey, A.C., Department of Ocean Engineering, IIT Madras, Chennai, India; Subramanian, A.V., Department of Ocean Engineering, IIT Madras, Chennai, India; Jagadeesh Kumar, V., Department of Electrical Engineering, IIT Madras, Chennai, India","Steering ship models are important for the study of autonomous ship manoeuverability and design of ship motion control system. It is always a difficult task to find the mathematical model by first principle as it needs prior knowledge of hydrodynamic derivatives. The input–output-based system identification theory can be used to establish system mathematical models. A solution is offered by developing a Wi-Fi-based self-propelled, autonomous system for a ship model with Internet of Things (IoT) capabilities to perform manoeuvering and seakeeping tests in indoor environment without any complex mechanical structure, viz. following bridge. The developed autonomous on-board system equipped with main computer, suitable electronics, sensors, data acquisition system and Wi-Fi-based communication system. The developed system offers a cost effective, modular and portable solution to perform hydrodynamic studies of different hull form without incorporating major changes in the system. The use of IoT makes the data accessible to a naval architecture in real-time to analyse the motion response of the ship in different wave conditions and enables to implement the digital twin to simulate the real field scenario. Input–output-based model identification experiments such as turning circle and zig-zag tests are conducted to estimate the first-order steering model parameters and is further extended to design and implementation of a classical proportional–derivative-based steering control. The design is described in this paper with details of implementation on a demonstration oceanographic coastal research vessel. It illustrates the excellent communication between shore station computer and the on-board system on a wire-free model with robust control and exhibiting all the motion behaviour and dynamic effects. Experiments performed in wave basin in different wave conditions validate the efficacy of the proffered method. © 2021 Informa UK Limited, trading as Taylor & Francis Group.","IoT; Model tests; nomoto model; PD control; steering control; system identification","Cost effectiveness; Data acquisition; Digital twin; Environmental testing; Hydrodynamics; Internet of things; Motion control; Naval architecture; Robust control; Ship models; Ship steering equipment; Unmanned surface vehicles; Wireless local area networks (WLAN); Data acquisition system; Design and implementations; Hydrodynamic derivatives; Hydrodynamic studies; Internet of Things (IOT); Mechanical structures; Ship motion controls; System identification theory; Computer control systems",,,,,"Naval Research Board, एनआरबी; National Institute of Ocean Technology, Ministry of Earth Sciences, NIOT","The authors acknowledge the assistance of the Naval Research Board for providing hydrodynamic test facilities and the National Institute of Ocean Technology (NIOT), Chennai, India for their support in carrying out this research development.",,,,,,,,,,"Artyszuk, J., (2017), Performance of the second-order linear Nomoto model terms of zigzag curve parameters. Marine navigation. CRC Press; p. 403–409; Astrom, K.J., Källström, C.G., Identification of ship steering dynamics (1976) Automatica, 12 (1), pp. 9-22. , Jan; Azarsina, F., Williams, C.D., Nomoto indices for constant-depth zigzag manoeuvres of an autonomous underwater vehicle (2013) Int Sch Res Notices, 2013; Brendon, A.J., Gary, C.F., (1995), Development of a horizontal planar motion mechanism for determining hydrodynamic characteristics of underwater vehicles. Australasian Fluid Mechanics Conference, Australia; p. 151–154; Burmeister, H.C., Bruhn, W., Rødseth, Ø.J., Porathe, T., Autonomous unmanned merchant vessel and its contribution towards the e-Navigation implementation: the MUNIN perspective (2014) Int J e-Nav Maritime Econom, 1, pp. 1-13; Clarke, D., (2003), The foundation of steering and maneuvering. Maneuvering and control of marine crafts. Girona, Spain; p. 2–16; Dantas, J.L., Caetano, W.S., Vale, R.T., de Barros, E.A., Analysis of identification methods applied to free model tests of the Pirajuba AUV (2013) IFAC Proc Vol, 46 (33), pp. 185-190; Dubey, A.C., Subramanian, V.A., Kumar, V.J., Bhikkaji, B., (2016), Sept. Development of autonomous system for scaled ship model for seakeeping tests. Oceans 2016 MTS/IEEE Monterey. p. 1–5; Dubey, A.C., Venkat, R.N., Subramanian, A.V., Kumar, J.V., Wi-Fi enabled autonomous ship model tests for ship motion dynamics and seakeeping assessment (2017) IJMRD, 1 (2), pp. 29-45; Fossen, T.I., (2011) Handbook of marine craft hydrodynamics and motion control, , West Sussex, United Kingdom: John Wiley & Sons; Im, N., Seo, J.H., Ship manoeuvring performance experiments using a free running model ship (2010) Int J Marine Navigation Safety Sea Trans, 4 (1), pp. 29-33; Jokioinen, E., Poikonen, J., Jalonen, R., Saarni, J., (2016), Remote and autonomous ships-the next steps. AAWA Position Paper. London: Rolls Royce plc; Lv, C., Yu, H., Chi, J., Xu, T., (2018), Speed and heading control of unmanned surface vehicle based on IDA-PBC and L2 gain disturbance attenuation approach. 2018 Chinese Control And Decision Conference (CCDC). p. 1704–1708; Maurya, P., Desa, E., Pascoal, A., Barros, E., Navelkar, G., Madhan, R., Mascarenhas, A.A.M.Q., (2006), Control of the Maya AUV the vertical and horizontal planes: Theory and practical results. Proceedings of the 7th IFAC Conference on Manoeuvring and Control of Marine Craft. p. 20–22; Moreira, L., Fossen, T.I., Guedes Soares, C., Path following control system for a tanker ship model (2007) Ocean Eng, 34 (14-15), pp. 2074-2085; Moreira, L., Guedes Soares, C., Autonomous ship model to perform manoeuvring tests (2011) J Maritime Res, 8, pp. 29-46. , Jan; Nise, N.S., (2000) Control systems engineering, , 3rd ed, New York (NY): John Wiley & Sons, Inc; Nomoto, K., Taguchi, K., Honda, K., Hirano, S., On the steering qualities of ships (1956) Int Shipbuilding Progress, 1956, pp. 75-82. , Jan; Perera, L., Moreira, L., Santos, F., Ferrari, V., Sutulo, S., Soares, C.G., A navigation and control platform for real-Time manoeuvring of autonomous ship models (2012) IFAC Proc Vol, 45 (27), pp. 465-470. , 9th IFAC Conference on Manoeuvring and Control of Marine Craft; Perera, L.P., Oliveira, P., Soares, C.G., System identification of nonlinear vessel steering (2015) J Offshore Mech Arctic Eng, 137 (3); Qin, Y., Zhang, L., (2014), Parametric identification of ships maneuvering motion based on kalman filter algorithm. Mechatronics and automatic control systems. Springer; p. 107–114; Skjetne, R., Smogeli, Ø.N., Fossen, T.I., (2004), A nonlinear ship manoeuvering model: identification and adaptive control with experiments for a model ship; Tzeng, C.Y., Optimal control of a ship for a course-changing maneuver (1998) J Optim Theory Appl, 97 (2), pp. 281-297. , May; Tzeng, C.Y., Chen, J.F., Fundamental properties of linear ship steering dynamic models (1999) J Marine Sci Technol, 7 (2), pp. 79-88; Velasco, F., Revestido, E., Moyano, E., Lopez, E., (2007), Manoeuvring model parametric identification of an autonomous in-scale fast-ferry model. Oceans 2007-Europe. p. 1–6; Wróbel, K., Montewka, J., Kujala, P., Towards the assessment of potential impact of unmanned vessels on maritime transportation safety (2017) Reliab Eng Syst Safety, 165, pp. 155-169; Xu, H., Hinostroza, M.A., Soares, C.G., Estimation of hydrodynamic coefficients of nonlinear manoeuvring mathematical model with free-running ship model tests (2018) Int J Maritime Eng, 160, pp. 213-225; Zheng, J., Meng, F., Li, Y., Design and experimental testing of a free-running ship motion control platform (2017) IEEE Access, 6, pp. 4690-4696","Dubey, A.C.; Department of Ocean Engineering, India; email: awanish.dubey@gmail.com",,,"Taylor and Francis Ltd.",,,,,17445302,,,,"English","Ships Offshore Struct.",Article,"Final","",Scopus,2-s2.0-85102454140
"Lindner A.J.M., Norrefeldt V.","57196009600;51864093200;","Capabilities of the regional cabin demonstrator as digital twin for a future test mock-up",2021,"IOP Conference Series: Materials Science and Engineering","1024","1","012104","","",,,"10.1088/1757-899X/1024/1/012104","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100826685&doi=10.1088%2f1757-899X%2f1024%2f1%2f012104&partnerID=40&md5=a92095e951f8a3edf1516738f87099cc","Fraunhofer Institute for Building Physics IBP, Fraunhoferstr. 10, Valley, 83626, Germany","Lindner, A.J.M., Fraunhofer Institute for Building Physics IBP, Fraunhoferstr. 10, Valley, 83626, Germany; Norrefeldt, V., Fraunhofer Institute for Building Physics IBP, Fraunhoferstr. 10, Valley, 83626, Germany","Digital twins are essential for Industry 4.0 and the digitalization of research, development and manufacturing. By now, the models contain more and more information and are already often highly complex systems. However, an important step is to keep the model as simple as possible in order to reduce computing resources and the associated simulation time. The Indoor Environment Simulation Suite (IESS) is a tool to generate a zonal model from a CAD geometry. It is designed to simulate the transient indoor climate in a short time, while still considering all important physical processes (heat conduction, radiation, convection). After the zonal model has been generated, the user can parameterize it specifically to the application and couple it with other models when needed. Here, a new approach is to integrate a thermo-physiological model of the human body in order to make statements about the human perception of thermal comfort. This is of particular interest in early development phases in order to assess air conditioning strategies as well as structural or technical adaptions due to thermal bridges or other influences on the cabin climate. In this paper a concept will be presented where the zonal model is coupled with the thermo-physiological model to investigate selected case studies on how thermal comfort in a regional aircraft can be assessed and considered in early design phases. The cabin model is based on a new type of regional aircraft for which a mock-up is currently being built in order to perform subject test at the Fraunhofer Institute in Holzkirchen. The thermal model can thus make statements in advance about how test scenarios should be carried out in order to gain the greatest possible benefit from the subject study tests, where the number of different tests is limited. © 2021 Institute of Physics Publishing. All rights reserved.",,,,,,,,"The work was conducted with financial support from the Clean Sky 2 program under Grant Agreement number: No. 945548 REG GAM 2020. The authors are solely responsible for the content of this publication.",,,,,,,,,,"(2006) Ergonomics of the thermal environment - Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria, , International Organization for Standardization, (DIN EN ISO 7730:2006-05); (2013) Thermal Environmental Conditions for Human Occupancy, , American Society of Heating, Refrigerating and Air-Conditioning Engineers, (ANSI/ASHRAE Standard 55-2013); FANGER, P. O., Thermal comfort. Analysis and applications in environmental engineering (1970) Thermal comfort. Analysis and applications in environmental engineering, , (in not specified); Fiala, D., Lomas, K. J., Stohrer, M., A computer model of human thermoregulation for a wide range of environmental conditions: The passive system (1999) Journal of applied physiology, 87 (5), pp. 1957-1972; Wölki, D., van Treeck, C. A., Hensen, J., (2017) MORPHEUS: Modelica-based implementation of a numerical human model involving individual human aspects: Lehrstuhl für Energieeffizientes Bauen, , Dissertation, RWTH Aachen University; Fiala, D., (1998) Dynamic Simulation of Human Heat Transfer and Thermal Comfort, , Dissertation, Institute of Energy and Suistainable Development, De Monfort University, Leicester; Pathak, A., Norrefeldt, V., Lemouedda, A., Grün, G., (2014) The Modelica Thermal Model Generation Tool for Automated Creation of a Coupled Airflow, Radiation Model and Wall Model in Modelica, pp. 115-124; Atmosphere, U. S., (1976) National Aeronautics and Space Administration, , National oceanic and atmospheric administration, United States Air Force, Washington, DC","Lindner, A.J.M.; Fraunhofer Institute for Building Physics IBP, Fraunhoferstr. 10, Germany; email: Andreas.Lindner@ibp.fraunhofer.de",,,"IOP Publishing Ltd","10th EASN International Conference on Innovation in Aviation and Space to the Satisfaction of the European Citizens, EASN 2020","2 September 2020 through 4 September 2020",,166960,17578981,,,,"English","IOP Conf. Ser. Mater. Sci. Eng.",Conference Paper,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85100826685
"Moradi S., Eftekhar Azam S., Mofid M.","57222984604;57195073631;56458005600;","A Physics Informed Neural Network Integrated Digital Twin for Monitoring of the Bridges",2021,"Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021",,,,"771","778",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139236074&partnerID=40&md5=f29765682c5e02a7b3f38c5633296c4d","Department of Civil Engineering, Sharif University of Technology, Tehran, Iran; College of Engineering and Physical Sciences, University of New Hampshire, Durham, NH, United States","Moradi, S., Department of Civil Engineering, Sharif University of Technology, Tehran, Iran; Eftekhar Azam, S., College of Engineering and Physical Sciences, University of New Hampshire, Durham, NH, United States; Mofid, M., Department of Civil Engineering, Sharif University of Technology, Tehran, Iran","In recent years the Digital Twin (DT) paradigm has been studied as a futuristic tool for the next generation of infrastructures. Due to the interdisciplinary nature of the design, construction, monitoring, and maintenance of the infrastructures and the cooperation of several stakeholders throughout their lifetime, it is indispensable to introduce a comprehensive platform for the digital representation of infrastructures. Although the DT emphasizes the role of digital modeling and data analysis, there is a gap between physical modeling and data-driven tools. The newly introduced Physics Informed Neural Networks (PINNs) are capable of not only filling this gap but also representing a unified real-time platform for different users from various fields. These algorithms suggest an agile environment for users to introduce different criteria from the design stage to the health monitoring period. The PINN integrates both physical modeling and data analysis in a unique algorithm, helping them interact simultaneously and providing real-time, reliable responses. By means of the PINN, the DT can learn and update the model from various data sources with a unique platform, which plays an essential role in the rapid flow of information and transparency of data-based calculations. The dynamic ambiance of the PINN enables the users to interact with the modeling procedure and track the analysis. In this study, the details of the proposed platform for the integration of the PINNs in the DT are addressed for monitoring the bridges. Extensive numerical studies are provided for various scenarios of sensor equipment, including sensor type, data accuracy, and installation pattern. The performance of the proposed platform is evaluated for predicting subsequent responses to ensure the reliability of the responses in future decision makings. © 2021 Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021. All rights reserved.","Bridges; Digital Twin; Health Monitoring; Physics Informed Neural Networks","Cyber Physical System; Data handling; Embedded systems; Information analysis; Structural health monitoring; Construction monitoring; Design construction; Digital datas; Digital modeling; Digital representations; Health monitoring; Neural-networks; Physic informed neural network; Physical data; Physical modelling; Decision making",,,,,,,,,,,,,,,,"Sepasgozar, S. M. E., Differentiating digital twin from digital shadow: Elucidating a paradigm shift to expedite a smart, sustainable built environment (2021) Buildings, 11 (4); Mertala-lindsay, T., Strålman, J., From Early Design to Project Delivery Master’ s thesis in Design and Construction Project Management DIVISION OF CONSTRUCTION MANAGEMENT (2021) CHALMERS UNIVERSITY OF TECHNOLOGY; Chen, Z., Huang, L., Digital Twin in Circular Economy: Remanufacturing in Construction (2020) IOP Conf. Ser. Earth Environ. Sci, 588 (3); Zhu, Y.-C., Wagg, D., Cross, E., Barthorpe, R., (2020) Real-Time Digital Twin Updating Strategy Based on Structural Health Monitoring Systems, 3, pp. 55-64; Jiang, F., Ding, Y., Song, Y., Geng, F., Wang, Z., Digital Twin-driven framework for fatigue life prediction of steel bridges using a probabilistic multiscale model: Application to segmental orthotropic steel deck specimen (2020) Eng. Struct, 241, p. 2021. , December; Jiang, F., Ma, L., Broyd, T., Chen, K., Digital twin and its implementations in the civil engineering sector (2021) Autom. Constr, 130, p. 103838. , July; Shim, C. S., Kang, H. R., Dang, N. S., Digital twin models for maintenance of cable-supported bridges (2019) Int. Conf. Smart Infrastruct. Constr. 2019, ICSIC 2019 Driv. Data-Informed Decis, 2019 (2017), pp. 737-742; Raissi, M., Perdikaris, P., Karniadakis, G. E., (2017) Physics informed deep learning (Part II): Data-driven discovery of nonlinear partial differential equations, pp. 1-19. , arXiv Part II; Raissi, M., Perdikaris, P., Karniadakis, G. E., (2017) Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations, , arXiv, Nov; Mao, Z., Jagtap, A. D., Karniadakis, G. E., Physics-informed neural networks for high-speed flows (2020) Comput. Methods Appl. Mech. Eng, 360, p. 112789. , Mar; Chen, Y., Lu, L., Karniadakis, G. E., Dal Negro, L., Physics-informed neural networks for inverse problems in nano-optics and metamaterials (2020) Opt. Express, 28 (8), p. 11618. , Apr; Misyris, G. S., Venzke, A., Chatzivasileiadis, S., Physics-Informed Neural Networks for Power Systems (2020) 2020 IEEE Power & Energy Society General Meeting (PESGM), pp. 1-5",,"Farhangdoust S.Guemes A.Chang F.-K.","Air Force Office of Scientific Research, United States Air Force;Boeing;et al.;Office of Naval Research Science and Technology;Transportation Research Board;U.S. Department of Transportation","DEStech Publications Inc.","13th International Workshop on Structural Health Monitoring: Enabling Next-Generation SHM for Cyber-Physical Systems, IWSHM 2021","15 March 2022 through 17 March 2022",,182782,,9781605956879,,,"English","Struct. Health Monit.: Enabling Next-Gener. SHM Cyber-Phys. Syst. - Proc. Int. Workshop Struct. Health Monit., SHM",Conference Paper,"Final","",Scopus,2-s2.0-85139236074
"Kazemian M., Nikdel S., Mohammadesmaeili M., Nik V., Zandi K.","57914651800;57914651900;57915708400;54978401900;57433878000;","Kalix Bridge Digital Twin—Structural Loads from Future Extreme Climate Events",2021,"Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021",,,,"746","755",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139223801&partnerID=40&md5=5cfa4d988ed1e4de20a57e79a21790ac",,"Kazemian, M.; Nikdel, S.; Mohammadesmaeili, M.; Nik, V.; Zandi, K.","Environmental loads, such as wind and river flow, play an essential role in the structural design and structural assessment of long-span bridges. Climate change and extreme climatic events are threats to the reliability and safety of the transport network. This has led to a growing demand for digital twin models to investigate the resilience of bridges under extreme climate conditions. Kalix bridge, constructed over the Kalix river in Sweden in 1956, is used as a testbed in this context. The bridge structure, made of post-tensioned concrete, consists of five spans, with the longest one being 94 m. In this study, aerodynamic characteristics and extreme values of numerical wind simulation such as surface pressure are obtained by using Spalart-Allmaras Delayed Detached Eddy Simulation (DDES) as a hybrid RANS-LES turbulence approach which is both practical and computationally efficient for near-wall mesh density imposed by the LES method. Surface wind pressure is obtained for three extreme climate scenarios, including extreme windy weather, extremely cold weather, and design value for a 3000-year return period. The result indicates significant differences in surface wind pressure due to time layers coming from transient wind flow simulation. In order to assess the structural performance under the critical wind scenario, the highest value of surface pressure for each scenario is considered. Also, a hydrodynamic study is conducted on the bridge pillars, in which the river flow is simulated using the VOF method, and the water movement process around the pillars is examined transiently and at different times. The surface pressure applied by the river flow with the highest recorded volumetric flow is calculated on each of the pier surfaces. In simulating the river flow, information and weather conditions recorded in the past periods have been used. The results show that the surface pressure at the time when the river flow hit the pillars is much higher than in subsequent times. This amount of pressure can be used as a critical load in fluid-structure interaction (FSI) calculations. Finally, for both sections, the wind surface pressure, the velocity field with respect to auxiliary probe lines, the water circumferential motion contours around the pillars, and the pressure diagram on them are reported in different timesteps. © 2021 Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021. All rights reserved.",,"Climate change; Climate models; Cyber Physical System; Embedded systems; Fluid structure interaction; Rivers; Structural health monitoring; Velocity; Climate event; Environmental loads; Extreme climates; River flow; Structural assessments; Structural design assessments; Surface pressures; Surface winds; Wind flow; Wind pressures; Structural dynamics",,,,,,"The authors greatly appreciate the support of Dlubal Software for providing RWIND Simulation license, as well as Flow Sciences Inc. for providing FLOW-3D license.",,,,,,,,,,"Jančula, M., Jošt, J., Gocál, J., Influence of aggressive environmental actions on bridge structures (2021) Transportation Research Procedia, 55, pp. 1229-1235; Wang, X., (2010) Analysis of climate change impacts on the deterioration of concrete infrastructure–synthesis Report, 643 (10364), p. 1. , Published by CSIRO, Canberra. ISBN978 0; Kemayou, B.T.M., (2016) Analysis of Bridge Deck Sections by Pseudo-compressibility method based on FDM and LES: Improving Performance through Implementation of Parallel Computing, , University of Arkansas; Larsen, A., Walther, J.H., Aeroelastic analysis of bridge girder sections based on discrete vortex simulations (1997) Journal of Wind Engineering and Industrial Aerodynamics, 67, pp. 253-265; Standard, B., (2006) Eurocode 1: Actions on structures; (2013) Minimum design loads for buildings and other structures, , ASCE. American Society of Civil Engineers; Nik, V.M., Making energy simulation easier for future climate–Synthesizing typical and extreme weather data sets out of regional climate models (RCMs) (2016) Applied Energy, 177, pp. 204-226; Perera, A., (2020) Quantifying the impacts of future climate variations and extreme climate events on energy systems; Nik, V.M., Application of typical and extreme weather data sets in the hygrothermal simulation of building components for future climate–A case study for a wooden frame wall (2017) Energy and Buildings, 154, pp. 30-45; Hosseini, M., Javanroodi, K., Nik, V.M., High-resolution impact assessment of climate change on building energy performance considering extreme weather events and microclimate–Investigating variations in indoor thermal comfort and degree-days (2021) Sustainable Cities and Society, p. 103634; Spalart, P.R., Detached-eddy simulation (2009) Annual review of fluid mechanics, 41, pp. 181-202; Spalart, P.R., A new version of detached-eddy simulation, resistant to ambiguous grid densities (2006) Theoretical and computational fluid dynamics, 20 (3), pp. 181-195; Spalart, P.R., Comments on the feasibility of LES for wings, and on a hybrid RANS/LES approach (1997) Proceedings of first AFOSR international conference on DNS/LES, , Greyden Press; Boudreau, M., Dumas, G., Veilleux, J.-C., Assessing the ability of the ddes turbulence modeling approach to simulate the wake of a bluff body (2017) Aerospace, 4 (3), p. 41; Wang, Y.-h., Analysis of water flow pressure on bridge piers considering the impact effect (2015) Mathematical Problems in Engineering, 2015; Qi, H., Zheng, J., Zhang, C., Numerical simulation of velocity field around two columns of tandem piers of the longitudinal bridge (2020) Fluids, 5 (1), p. 32; Herzog, S.D., Spring flood induced shifts in Fe speciation and fate at increased salinity (2019) Applied Geochemistry, 109, p. 104385",,"Farhangdoust S.Guemes A.Chang F.-K.","Air Force Office of Scientific Research, United States Air Force;Boeing;et al.;Office of Naval Research Science and Technology;Transportation Research Board;U.S. Department of Transportation","DEStech Publications Inc.","13th International Workshop on Structural Health Monitoring: Enabling Next-Generation SHM for Cyber-Physical Systems, IWSHM 2021","15 March 2022 through 17 March 2022",,182782,,9781605956879,,,"English","Struct. Health Monit.: Enabling Next-Gener. SHM Cyber-Phys. Syst. - Proc. Int. Workshop Struct. Health Monit., SHM",Conference Paper,"Final","",Scopus,2-s2.0-85139223801
"Ai L., Bayat M., Comert G., Ziehl P.","57208900408;36561127900;24476244900;6602561216;","An Autonomous Bridge Load Rating Framework Using Digital Twin",2021,"Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021",,,,"796","804",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139210530&partnerID=40&md5=4c78a49808288385a0464e05be8b3f81","Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC  29201, United States; Department of Engineering, Benedict College, Columbia, SC, United States","Ai, L., Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC  29201, United States; Bayat, M., Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC  29201, United States; Comert, G., Department of Engineering, Benedict College, Columbia, SC, United States; Ziehl, P., Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC  29201, United States","Load rating of bridges is used to understand the working status and carrying capacity of bridge structures and components and is necessary to the safety of transportation. The current manual load rating procedure is, however, time-consuming. An intelligent and automatic load rating approach can be beneficial to supplement or eventually perhaps replace the current manual procedures. The innovation of this paper lies in developing an autonomous load rating framework by leveraging Digital Twin (DT) techniques. Full-scale laboratory testing of a bridge slab was conducted to verify the efficiency of the proposed framework. The ultimate moment capacity of the slab was obtained by carrying out four-point bending test. The testing procedure was monitored in real-time with multiple strain gauges. A real-scale finite element model of the slab was developed and calibrated with the testing results. The proposed DT framework of the bridge slabs was developed by integrating the numerical modeling and the strain monitoring. The proposed DT framework is intended for field application, and field results will be discussed. © 2021 Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021. All rights reserved.",,"Embedded systems; Structural health monitoring; 'current; Bridge load ratings; Bridge slabs; Bridge structures; Four-point bending test; Laboratory testing; Load ratings; Rating procedures; Twin techniques; Ultimate moment capacity; Cyber Physical System",,,,,,,,,,,,,,,,"(2013) Report card on America’s infrastructure, , ASCE. Reston, VA; Islam, A. A., Jaroo, A.S., Li, F., F. Bridge load rating using dynamic response (2015) Journal of Performance of Constructed Facilities, 29 (4), p. 04014120; (2015) Manual for Bridge Evaluation, 2nd Ed. with 2016 Interim Revisions, , American Association of State and Highway Transportation Officials (AASHTO). American Association of State Highway and Transportation Officials,.""Washington, D.C; Alampalli, S., Frangopol, D.M., Grimson, J., Halling, M.W., Kosnik, D.E., Lantsoght, E.O., Yang, D., Zhou, Y.E., Bridge load testing: state-of-the-practice (2021) Journal of Bridge Engineering, 26 (3), p. 03120002; Zulifqar, A., Cabieses, M., Mikhail, A., Khan, N., (2014) Design of a bridge inspection system (BIS) to reduce time and cost, , George Mason University: Farifax, VA, USA; American Road & Transportation Builders Association (ARTBA); (2011) Manual for bridge evaluation, , AASHTO 2nd Ed., Washington, D.C., 2019; Lantsoght, E. O. L., van der Veen, C., Hordijk, D. A., de Boer, A., State-of-the-art on load testing of concrete bridges (2017) Eng. Struct, 150, pp. 231-241. , https://doi.org/10.1016/j.engstruct.2017.07.050; Fu, G., Pezze, F., Alampalli, S., Diagnostic load testing for bridge load rating (1997) Transp. Res. Rec, 1594, pp. 125-133. , https://doi.org/10.3141/1594-13; Hernandez, E. S., Myers, J. J., Diagnostic test for load rating of a prestressed SCC bridge (2018) ACI Spec. Publ, 323. , 11.1–11.16; Kim, Y. J., Tanovic, R., Wight, R. G., Recent advances in performance evaluation and flexural response of existing bridges (2009) J. Perform. Constr. Facil, 23 (3), pp. 190-200. , https://doi.org/10.1061/(ASCE)CF.1943-5509.0000007; Aguilar, C. V., Jáuregui, D. V., Newtson, C. M., Weldon, B. D., Cortez, T. M., Load rating a prestressed concrete double-tee beam bridge without plans by proof testing (2015) Transp. Res. Rec, (2522), pp. 90-99; Anay, R., Cortez, T. M., Jáuregui, D. V., ElBatanouny, M. K., Ziehl, P., On-site acoustic-emission monitoring for assessment of a prestressed concrete double-tee-beam bridge without plans (2016) J. Perform. Constr. Facil, 30 (4), p. 04015062. , https://doi.org/10.1061/(ASCE)CF.1943-5509.0000810; Casas, J. R., Gómez, J. D., Load rating of highway bridges by proof-loading (2013) KSCE J. Civ. Eng, 17 (3), pp. 556-567. , https://doi.org/10.1007/s12205-013-0007-8",,"Farhangdoust S.Guemes A.Chang F.-K.","Air Force Office of Scientific Research, United States Air Force;Boeing;et al.;Office of Naval Research Science and Technology;Transportation Research Board;U.S. Department of Transportation","DEStech Publications Inc.","13th International Workshop on Structural Health Monitoring: Enabling Next-Generation SHM for Cyber-Physical Systems, IWSHM 2021","15 March 2022 through 17 March 2022",,182782,,9781605956879,,,"English","Struct. Health Monit.: Enabling Next-Gener. SHM Cyber-Phys. Syst. - Proc. Int. Workshop Struct. Health Monit., SHM",Conference Paper,"Final","",Scopus,2-s2.0-85139210530
"Wakabayashi Y., Yasuda K., Tsujii H., Konno K., Hirahara Y.","57222347177;57222348120;57817762000;57818037700;57222347498;","Labor productivity improvement of concrete bridge through utilizing bim and ict",2021,"fib Symposium","2021-June",,,"1914","1921",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134794529&partnerID=40&md5=ee4a3dcac3fcfc9cb121bfb166be43d0","R&D Department, IHI Construction Service Co., Ltd, Tokyo, Japan; OfficeK1 Co., Ltd., Osaka, Japan; International Technology Transfer Corporation Co., Ltd., Hyogo, Japan; Informatix Co., Ltd., Kanagawa, Japan; Chiyoda Sokki Co., Ltd., Tokyo, Japan","Wakabayashi, Y., R&D Department, IHI Construction Service Co., Ltd, Tokyo, Japan; Yasuda, K., OfficeK1 Co., Ltd., Osaka, Japan; Tsujii, H., International Technology Transfer Corporation Co., Ltd., Hyogo, Japan; Konno, K., Informatix Co., Ltd., Kanagawa, Japan; Hirahara, Y., Chiyoda Sokki Co., Ltd., Tokyo, Japan","Dwindling competency on worksites has been a pressing issue looming Japan as the country struggles with a decrease in the number of skilled construction site technicians and an aging population, in addition to the shrinking youth workforce that are creating a shortage of future successors. Given the need to resolve these challenges, BIM has been introduced to improve productivity at construction sites, and a unified model covering processes from planning, preliminary design, design, construction to maintenance is interoperated to improve productivity and quality control. Going forward, the latest technology needs to be incorporated to further enhance efficiency, such as leveraging information and communication technology (ICT) during project implementation. In this project, a digital twin was used to obtain real-time construction data from the construction site of a four-span continuous prestressed concrete box-girder bridge. Construction management through the 4D system in bridge construction equipment, reinforcement inspection using UAV images, and formwork and construction progress inspection using MR technology were tested, and it was confirmed that staff numbers and work time were reduced by 22 to 30% compared to the conventional construction. Furthermore, for quality inspection, the acquired digital data were automatically linked to forms to streamline inspections. This paper reports on the productivity improvements that were made possible on the construction site by combining BIM and ICT such as auto-tracking total station, image analysis and MR technologies. © Fédération Internationale du Béton (fib) – International Federation for Structural Concrete.","Auto-tracking total station; BIM; Image analysis technology; Image recognition technology; MR technology","Box girder bridges; Concrete beams and girders; Concrete buildings; Concrete construction; Construction equipment; Digital twin; Efficiency; Human resource management; Image enhancement; Inspection; Prestressed concrete; Productivity; Project management; Steel bridges; Structural design; Construction management; Construction progress; Conventional constructions; Information and Communication Technologies; Prestressed concrete box girder; Productivity improvements; Project implementation; Real-time construction; Architectural design",,,,,,,,,,,,,,,,"Wakabayashi, Y., Mitani, K., Akamatsu, T., Yasuda, K., [‘BIM/CIM-Based Prestressed Concrete Box Girder Bridge Construction Productivity Improvement’ (2020) Proceedings of the 29Th Symposium on Developments in Prestressed Concrete, pp. 481-484","Wakabayashi, Y.; R&D Department, Japan; email: wakabayashi8366@ihi-g.com","Julio E.Valenca J.Louro A.S.","CIMPOR;dstgroup Group Culture;et al.;Leca;Materials;Megasa","fib. The International Federation for Structural Concrete","2021 fib Symposium of Concrete Structures: New Trends for Eco-Efficiency and Performance","14 June 2021 through 16 June 2021",,169715,26174820,9782940643080,,,"English","fib. Symp.",Conference Paper,"Final","",Scopus,2-s2.0-85134794529
"Adibfar A., Costin A.M.","57202945239;55200193500;","Integrated Management of Bridge Infrastructure through Bridge Digital Twins: A Preliminary Case Study",2021,"Computing in Civil Engineering 2021 - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2021",,,,"358","365",,,"10.1061/9780784483893.045","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132577008&doi=10.1061%2f9780784483893.045&partnerID=40&md5=37e887b659fe6376b9c53b1ddb77ea56","M.E. Rinker, Sr. School of Construction Management, Univ. of Florida, United States","Adibfar, A., M.E. Rinker, Sr. School of Construction Management, Univ. of Florida, United States; Costin, A.M., M.E. Rinker, Sr. School of Construction Management, Univ. of Florida, United States","Data play a significant role in the life cycle management of road infrastructure and bridges, and their integration is a crucial element of smart infrastructure systems. However, the current practice of managing infrastructure involves the use of an abundance of data produced by a variety of non-interoperable information systems. Thus, the lack of interoperability creates major challenges in deployment of a fully integrated and smart management system for the infrastructure. This research focuses on the development of a digital twin that could be used as an umbrella for integrating the live load traffic data on bridges with other bridge life cycle data to mirror the bridge behavior and offer a smart and integrated bridge management system. Intelligent transportation systems (ITS) are remarkable instances of advanced technology that reduced the human role in the operation and management of transportation infrastructure. Weigh-in-motion (WIM) is a type of ITS system provides a stream of valuable data about weight and other dynamic attributes of the fleeting traffic over road network and bridges. In this research, the real-time WIM data are integrated into a BrIM model by visual scripting to form the digital twin of the bridge. Through this approach, weight sensor data could be streamed in the digital twin of bridge, and the level of data utilization by different stakeholders can be improved. The outcomes of this study will help the preservation and sustainability of bridges and helping their resiliency through pro-active planning and enhancing the utilization of the available data. © 2021 Computing in Civil Engineering 2021 - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2021. All rights reserved.",,"Bridges; Data integration; Highway administration; Highway planning; Information management; Intelligent vehicle highway systems; Interoperability; Life cycle; Roads and streets; Bridge infrastructure; Case-studies; Current practices; Infrastructure systems; Integrated management; Intelligent transportation systems; Lifecycle management; Road bridge; Road infrastructures; Smart infrastructures; Intelligent systems",,,,,,,,,,,,,,,,"(2021) Report Card for America's Infrastructure, , https://infrastructurereportcard.org/, ASCE. (Apr. 12, 2021); (2020) Report card for West Virginia's Infrastructure, , https://www.infrastructurereportcard.org/wp-content/uploads/2020/12/WV-2020-Infrastructure-Report-Card-1.pdf, ASCE. (Dec. 10, 2020); Adibfar, A., Costin, A., Next Generation of Transportation Infrastructure Management: Fusion of Intelligent Transportation Systems (ITS) and Bridge Information Modeling (BrIM) (2019) Advances in informatics and computing in civil and construction engineering, pp. 43-50; Adibfar, A., Costin, A., Evaluation of IFC for the Augmentation of Intelligent Transportation Systems (ITS) into Bridge Information Models (BrIM) (2019) Proceedings of ASCE international conference on computing in civil engineering, , June 17-19, Atlanta, Georgia; Costin, A., Adibfar, A., Hu, H., Chen, S., Building Information Modeling (BIM) for Transportation Infrastructure-Literature Review, Applications, and Challenges (2018) Automation in Construction, 94, pp. 257-281; Dygalo, V., Keller, A., Shcherbin, A., Principles of application of virtual and physical simulation technology in production of digital twin of active vehicle safety systems (2020) Transportation Research Procedia, 50, pp. 121-129; (2019) The FAST Act, , https://www.fhwa.dot.gov/fastact/, FHWA. (Dec. 09, 2020); Glaessgen, E.H., Stargel, D.S., The digital twin paradigm for future NASA and U.S. Airforce vehicles (2012) 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference - Special Session on the Digital Twin, , https://doi.org/10.2514/6.2012-1818, April 23-26, Honolulu, Hawaii; Hofmann, W., Branding, F., Implementation of an IoT and Cloud-based Digital Twin for real-time decision support in port operations (2019) IFAC PapersOnline, 53 (13). , Elsevier; Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W., Digital Twin in manufacturing: A categorical literature review and classification (2018) IFAC-PapersOnLine, 51 (11), pp. 1016-1022; Lu, R., Brilakis, I., Digital twining of existing reinforced concrete bridges from labelled point clusters (2019) Automation in Construction, 105, p. 102837. , https://doi.org/10.1016/j.autcon.2019.102837; McLoud, D., Construction groups seek more transportation funding as shutdown looms (2020) Equipment World's Better Roads, , https://tinyurl.com/y3qn4gvh, Dec. 09, 2020; Mi, S., Feng, Y., Zheng, H., Wang, Y., Gao, Y., Tan, J., Prediction maintenance integrated decision-making approach supported by digital twin-driven cooperative awareness and interconnection framework (2021) Journal of Manufacturing Systems, 58, pp. 329-345. , https://doi.org/10.1016/j.jmsy.2020.08.001; Shirowzhan, S., Tan, W., Sepasgozar, S., Digital twin and CyberGIS for improving connectivity and measuring the impact of infrastructure construction planning in smart cities (2020) International Journal of Geo-Information, 9 (4), p. 240. , https://doi.org/10.3390/ijgi9040240; Sofia, H., Anas, E., Faiz, O., Mobile Mapping, Machine Learning and Digital Twin for Road Infrastructure Monitoring and Maintenance: Case Study of Mohammed VI Bridge in Morocco (2020) Proceedings of 2020 IEEE International conference of Moroccan Geomatics (Morgeo), , 11-13 May 2020, Casablanca, Morocco, Morocco; Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Gou, Z., Nee, A., Digital twin-driven product design framework (2019) International Journal of production research, (12), p. 57. , https://doi.org/10.1080/00207543.2018.1443229; Teng, S., Toud, M., Leong, W., How, B., Lam, H., Masa, V., Recent advances on industrial data-driven energy savings: Digital twins and infrastructures (2021) Renewable and Sustainable Energy Reviews, 135, p. 110208",,"Issa R.R.A.","Computing Division of the American Society of Civil Engineers (ASCE)","American Society of Civil Engineers (ASCE)","2021 International Conference on Computing in Civil Engineering, I3CE 2021","12 September 2021 through 14 September 2021",,179585,,9780784483893,,,"English","Comput. Civ. Eng. - Sel. Pap. ASCE Int. Conf. Comput. Civ. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85132577008
"Meixedo A., Santos J., Ribeiro D., Calçada R., Todd M.","56940709200;36810314200;24476782300;7801603531;7202805915;","Data-driven approach for detection of structural changes using train-induced dynamic responses",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"441","448",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130716567&partnerID=40&md5=4fe01c82b6c65f2c94aad2466b276a29","CONSTRUCT-LESE, Department of Civil Eng., Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, Porto, 4200-465, Portugal; Laboratório Nacional de Engenharia Civil, LNEC, Avenidade do Brasil 101, Lisbon, 1700-075, Portugal; CONSTRUCT-LESE, Department of Civil Eng., School of Engineering, Polytechnic of Porto, R. Dr. António Bernardino de Almeida 431, Porto, 4200-072, Portugal; Department of Structural Engineering, University of California, La Jolla, San Diego, CA  92093-0085, United States","Meixedo, A., CONSTRUCT-LESE, Department of Civil Eng., Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, Porto, 4200-465, Portugal; Santos, J., Laboratório Nacional de Engenharia Civil, LNEC, Avenidade do Brasil 101, Lisbon, 1700-075, Portugal; Ribeiro, D., CONSTRUCT-LESE, Department of Civil Eng., School of Engineering, Polytechnic of Porto, R. Dr. António Bernardino de Almeida 431, Porto, 4200-072, Portugal; Calçada, R., CONSTRUCT-LESE, Department of Civil Eng., Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, Porto, 4200-465, Portugal; Todd, M., Department of Structural Engineering, University of California, La Jolla, San Diego, CA  92093-0085, United States","This work considers the detection of structural changes in railway bridge vibration response induced by train traffic using structural health monitoring systems. To achieve this goal, an innovative data-driven unsupervised methodology is proposed, consisting of a combination of time series analysis and advanced multivariate statistical techniques such as autoregressive models, multiple linear regression, and outlier analysis. The efficiency of the proposed methodology is verified on a complex bowstring-arch railway bridge. A digital twin of the bridge is used to simulate baseline and damage conditions by performing finite element time-history analysis using as input measurements of real temperatures, noise effects, and train speeds, and loads. The methodology proven to be highly robust to false detections and sensitive to early damage by automatically detecting small stiffness reductions in the concrete slab, diaphragms, and arches, as well as friction increase in the bearing devices. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.","Damage detection; Data-driven approach; Structural health monitoring; Unsupervised learning","Arch bridges; Arches; Damage detection; Linear regression; Multivariant analysis; Railroads; Time series analysis; Unsupervised learning; Autoregressive modelling; Bridge vibration; Data driven; Data-driven approach; Multivariate statistical techniques; Railway bridges; Structural health monitoring systems; Time-series analysis; Train traffic; Vibration response; Structural health monitoring",,,,,"Horizon 2020 Framework Programme, H2020; European Commission, EC; Fundação para a Ciência e a Tecnologia, FCT: SFRH/BD/93201/2013; Ministério da Ciência, Tecnologia e Ensino Superior, MCTES; Institute of Research and Development in Structures and Construction; Laboratório Nacional de Engenharia Civil, LNEC","This work was financially supported by the Portuguese Foundation for Science and Technology (FCT) through the PhD scholarship SFRH/BD/93201/2013. The authors would like to acknowledge the support of the R&D project RISEN - Rail Infrastructure Systems Engineering Network - financed by European Commission through the H2020|ES|MSC - H2020|Excellence Science|Marie Curie programme, the Portuguese Road and Railway Infrastructure Manager (Infraestruturas de Portugal, I.P), the Portuguese National Laboratory for Civil Engineering (LNEC), and the Base Funding - UIDB/04708/2020 of the CONSTRUCT - Instituto de I&D em Estruturas e Construções - financed by national funds through the FCT/MCTES (PIDDAC).",,,,,,,,,,"Malveiro, J, Sousa, C, Ribeiro, D, Calçada, R., Impact of track irregularities and damping on the fatigue damage of a railway bridge deck slab (2018) Structure and Infrastructure Engineering, 14 (9), pp. 1257-1268; Meixedo, A, Ribeiro, D, Calçada, R, Delgado, R., Global and Local Dynamic Effects on a Railway Viaduct with Precast Deck (2014) Proceedings of the Second International Conference on Railway Technology: Research, Development and Maintenance, , Civil-Comp Press, Stirlingshire, UK; Carey, CH, O'Brien, EJ, Keenahan, J., Investigating the Use of Moving Force Identification Theory in Bridge Damage Detection (2013) Key Engineering Materials, 569-570, pp. 215-222. , (January 2016); Cavadas, F, Smith, IFC, Figueiras, J., Damage detection using data-driven methods applied to moving-load responses (2013) Mechanical Systems and Signal Processing, 39 (1-2), pp. 409-425; Nie, Z, Lin, J, Li, J, Hao, H, Ma, H., Bridge condition monitoring under moving loads using two sensor measurements (2019) Structural Health Monitoring, pp. 1-21; Gonzalez, I, Karoumi, R., BWIM aided damage detection in bridges using machine learning (2015) Journal of Civil Structural Health Monitoring, 5 (5), pp. 715-725; Neves, AC, González, I, Leander, J, Karoumi, R., Structural health monitoring of bridges: a model-free ANN-based approach to damage detection (2017) Journal of Civil Structural Health Monitoring, (7), pp. 689-702; Azim, R, Gül, M., Damage detection of steel girder railway bridges utilizing operational vibration response (2019) Structural Control and Health Monitoring, pp. 1-15. , (August); Ribeiro, D, Leite, J, Meixedo, A, Pinto, N, Calçada, R, Todd, M., Statistical methodologies for removing the operational effects from the dynamic responses of a high-rise telecommunications tower (2021) Structural Control and Health Monitoring; Meixedo, A, Ribeiro, D, Santos, J, Calçada, R, Todd, M., Progressive numerical model validation of a bowstring-arch railway bridge based on a structural health monitoring system (2021) Journal of Civil Structural Health Monitoring; Ribeiro, D, Calçada, R, Delgado, R, Brehm, M, Zabel, V., Finite element model updating of a bowstring-arch railway bridge based on experimental modal parameters (2012) Engineering Structures, 40, pp. 413-435; Santos, J., (2014) Smart Structural Health Monitoring Techniques for Novelty Identification in Civil Engineering Structures, , PhD Thesis. Instituto Superior Técnico University of Lisbon; Farrar, CR, Worden, K., (2013) Structural Health Monitoring: a machine learning perspective, , Wiley; Bisgaard, S, Kulahci, M., (2011) Time series analysis and forecasting by example, , Hoboken, NJ: John Wiley & Sons; Johnson, RA, Wichern, DW., (2013) Applied Multivariate Statistical Analysis, , 6th ed. Harlow: Pearson; Santos, JP, Crémona, C, Calado, L, Silveira, P, Orcesi, AD., On-line unsupervised detection of early damage (2015) Structural Control and Health Monitoring",,,,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021","30 June 2021 through 2 July 2021",,179266,25643738,,,,"English","Int. Conf. Struct. Health. Monit. Intelligent Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85130716567
"Westcott B.J., Hag-Elsafi O., Mosaferchi G., Alampalli S.","56928077300;57204982897;57709792600;7003686588;","Lifting load restrictions on the NYS Fort Plain Bridge: A case study in SHM and the internet of things",2021,"International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII","2021-June",,,"1135","1139",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130715606&partnerID=40&md5=26eaa86e741146ec7ebaa5804d66b85c","Intelligent Structures, Inc., Menlo Park, CA, United States; Structures Evaluation Bureau, New York State Department of Transportation, United States","Westcott, B.J., Intelligent Structures, Inc., Menlo Park, CA, United States; Hag-Elsafi, O., Structures Evaluation Bureau, New York State Department of Transportation, United States; Mosaferchi, G., Intelligent Structures, Inc., Menlo Park, CA, United States; Alampalli, S., Structures Evaluation Bureau, New York State Department of Transportation, United States","The global bridge asset inventory is in a state of deterioration and requires new methods of measuring the bridge condition performance state. Budget decisions often require more quantitative information than is provided by visual inspection alone. A Smart Bridge uses innovative digital monitoring, Digital Twin modeling, and analysis using measured performance on a cloud-based Internet of Things Platform (IoT). The Smart Bridge will obtain better information to supplement visual inspections for bridge decision analysis at a lower cost. Despite rehabilitation of the NYS Fort Plain bridge it remains load posted. NYS DOT used a Smart Bridge approach with load testing followed by IoT based performance monitoring and analysis to understand the structural capacity and live load demands on the structure. The results showed that the Ft. Plain bridge performance at or near its original design and lifting of the load restrictions. The structure is now monitored continuously to gain insights on its behavior and to determine the feasibility of the IoT based monitoring system for future structural monitoring. This paper summarizes this case study and the IoT structural monitoring system that was used for improved decision making for bridge management. The economics of Smart bridges and the impact on a fleet of bridges is presented showing a high ROI from performance monitoring. © 2021 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.",,"Bridge approaches; Budget control; Decision making; Deterioration; Life cycle; Load testing; Structural health monitoring; Budget decisions; Case-studies; Condition; Digital monitoring; Load restrictions; Modelling and analysis; Performance States; Quantitative information; Smart bridge; Visual inspection; Internet of things",,,,,,,,,,,,,,,,"Alampalli, S., Frangopol, D.M., Grimson, J., Kosnik, D., Halling, M., Lantsoght, E. O. L., Weidner, J. S., Zhou, Y.E., (2019) Primer on bridge load testing. Transportation Research Circular E-C257, , Washington, DC: Transportation Research Board; Alampalli, S., Frangopol, D.M., Grimson, J., Halling, M.W., Kosnik, D., Lantsoght, E.O.L., Yang, D.Y., Zhou, Y.E., Bridge Load Testing: State-of-the-Practice (2021) Journal of Bridge Engineering, ASCE, 26 (3); New York State Transportation Asset Management Plan June 2019 Marie Therese Dominguez, , https://www.dot.ny.gov/programs/capitalplan/repository/Final%20TAMP%20June%2028%202019.pdf, Commissioner; Heath, D. R., Richard, C., Benefits of Live Load Testing and Finite Element Modeling in Rating Bridges (2015) MassDOT Innovation and Tech Transfer Exchange, , Presented at Worcester, MA March 12; Mufti, A, Bakht, B, Horosko, A, Eden, R, A Case for Adding An Inspection level Related to SHM for Bridge Evaluation by CHBDC (2018) 10th International Conference on Short and Medium Span Bridges, , Quebec City, Quebec, Canada July 31; Hag-Elsafi, Osman, Kunin, Jonathan, (2006) Load testing for Bridge Rating dean's Mill Over Hannacrois Creek, , Special report 147 TRANSPORTATION RESEARCH AND DEVELOPMENT BUREAU New York State Department of Transportation February; Westcott, P., Azhari, F., THE ECONOMICS OF INTEGRATING INNOVATIVE MONITORING TECHNOLOGIES INTO BRIDGE MANAGEMENT POLICY TRB committee AHD35 Standing Committee on Bridge Management TRB 96th Annual Meeting Compendium of Papers Transportation Research Board 96th Annual Meeting Location, , Washington DC, United States Date: 2017-1-8 to 2017-1-12 Report/Paper Numbers: 17-04030",,,,"International Society for Structural Health Monitoring of Intelligent Infrastructure, ISHMII","10th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2021","30 June 2021 through 2 July 2021",,179266,25643738,,,,"English","Int. Conf. Struct. Health. Monit. Intelligent Infrastruct.",Conference Paper,"Final","",Scopus,2-s2.0-85130715606
"Bhouri M.A.","57190835913;","Model-order-reduction approach for structural health monitoring of large deployed structures with localized operational excitations",2021,"Proceedings of the ASME Design Engineering Technical Conference","10",,"V010T10A022","","",,,"10.1115/DETC2021-70375","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119984148&doi=10.1115%2fDETC2021-70375&partnerID=40&md5=8c461fe932b7a2b2cfce4b5690affd54","Massachusetts Institute of Technology, Cambridge, MA, United States","Bhouri, M.A., Massachusetts Institute of Technology, Cambridge, MA, United States","We present a simulation-based classification approach for large deployed structures with localized operational excitations. The method extends the two-level Port-Reduced Reduced-Basis Component (PR-RBC) technique to provide faster solution estimation to the hyperbolic partial differential equation of time-domain elastodynamics with a moving load. Time-domain correlation function-based features are built in order to train classifiers such as Artificial Neural Networks and Support-Vector Machines and perform damage detection. The method is tested on a bridge-shaped structure with a moving vehicle (playing the role of a digital twin) in order to detect cracks' existence. Such problem has 45 parameters and shows the merits of the two-level PR-RBC approach and of the correlation function-based features in the context of operational excitations, other nuisance parameters and added noise. The quality of the classification task is enhanced by the sufficiently large synthetic training dataset and the accuracy of the numerical solutions, reaching test classification errors below 0.1% for disjoint training set of size 7x103 and test set of size 3x103. Effects of the numerical solutions accuracy and of the sensors locations on the classification errors are also studied, showing the robustness of the proposed approach and the importance of constructing a rich and accurate representation of possible healthy and unhealthy states of interest. Copyright © 2021 by ASME","Domain decomposition; Model order reduction; Neural networks; Parametrized partial differential equations; Simulation-based classification; Structural health monitoring","Classification (of information); Damage detection; Domain decomposition methods; Large dataset; Statistical tests; Structural health monitoring; Support vector machines; Time domain analysis; Base components; Correlation function; Domain decompositions; Localised; Model order reduction; Neural-networks; Numerical solution; Parametrized partial differential equations; Reduced basis; Simulation-based classification; Neural networks",,,,,"Office of Naval Research, ONR: N00014-17-1-2077; Army Research Office, ARO: W911NF1910098","This work was supported by the ONR Grant [N00014-17-1-2077] and by the ARO Grant [W911NF1910098]. We would like to thank Professor Anthony T. Patera, Dr. Tommaso Taddei and Professor Masayuki Yano for the helpful comments and software they provided us with.",,,,,,,,,,"Khatir, S., Wahab, M., Fast simulations for solving fracture mechanics inverse problems using pod-rbf xiga and jaya algorithm (2019) Engineering Fracture Mechanics, 205, pp. 285-300; Farrar, C. R., Worden, K., (2013) Structural Health Monitoring: a Machine Learning Perspective, , 1st Edition, John Wiley & Sons, Ltd., Chichester, West Sussex, UK; Peeters, B., Maeck, J., DeRoeck, G., Vibration-based damage detection in civil engineering: excitation sources and temperature effects (2001) Smart Materials and Structures, 10 (3), pp. 518-527; Moughty, J. J., Casas, J. R., A State of the Art Review of Modal-Based Damage Detection in Bridges: Development, Challenges, and Solutions (2017) Applied Sciences, 7 (5), p. 510; Deraemaeker, A., Reynders, E., DeRoeck, G., Kullaa, J., Vibration-based structural health monitoring using output-only measurements under changing environment (2008) Mechanical Systems and Signal Processing, 22 (1), pp. 34-56; Au, S., Zhang, F.-L., Ni, Y.-C., Bayesian operational modal analysis: Theory, computation, practice (2013) Computers & Structures, 126, pp. 3-14; Zhang, L., Brincker, R., Andersen, P., An overview of operational modal analysis: Major development and issues (2005) Proceedings of the International Operational Modal Analysis Conference, pp. 26-27. , Copenhagen, Denmark; Gillich, G.-R., Furdui, H., Wahab, M., Korka, Z.-I., A robust damage detection method based on multi-modal analysis in variable temperature conditions (2019) Mechanical Systems and Signal Processing, 115, pp. 361-379; Taddei, T., Penn, J. D., Yano, M., Patera, A. T., Simulation-Based Classification; a Model-Order-Reduction Approach for Structural Health Monitoring (2018) Archives of Computational Methods in Engineering, 25 (1), pp. 23-45; Cruz, P., Salgado, R., Performance of Vibration-Based Damage Detection Methods in Bridges (2008) Computer-Aided Civil and Infrastructure Engineering, 24, pp. 62-79; Talebinejad, I., Fischer, C., Ansari, F., Numerical Evaluation of Vibration-Based Methods for Damage Assessment of Cable-Stayed Bridges (2011) Computer-Aided Civil and Infrastructure Engineering, 26, pp. 239-251; Fan, W., Qiao, P., Vibration-based Damage Identification Methods: A Review and Comparative Study (2011) Structural Health Monitoring, 10, pp. 83-111; Mei, Q., Gül, M., (2019) A Cost Effective Solution for Road Crack Inspection using Cameras and Deep Neural Networks, , arXiv:1907.06014v2 [cs.CV]; Zhang, X., Rajan, D., Story, B., Concrete crack detection using context-aware deep semantic segmentation network (2019) Computer-Aided Civil and Infrastructure Engineering, 34 (11), pp. 951-971; Nayyeri, F., Hou, L., Zhou, J., Guan, H., Foreground-background separation technique for crack detection (2019) Computer-Aided Civil and Infrastructure Engineering, 34 (6), pp. 457-470; Ni, F., Zhang, J., Chen, Z., Zernike-moment measurement of thin-crack width in images enabled by dual-scale deep learning (2019) Computer-Aided Civil and Infrastructure Engineering, 34 (5), pp. 367-384; Cha, Y., Choi, W., Büyüköztürk, O., Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks (2018) Computer-Aided Civil and Infrastructure Engineering, 32 (5), pp. 361-378; Li, S., Zhao, X., Zhou, G., Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network (2019) Computer-Aided Civil and Infrastructure Engineering, 34 (7), pp. 616-634; Bang, S., Park, S., Kim, H., Kim, H., Encoder-decoder network for pixel-level road crack detection in black-box images (2019) Computer-Aided Civil and Infrastructure Engineering, 34 (8), pp. 713-727; Rafiei, M., Adeli, H., A novel machine learning-based algorithm to detect damage in high-rise building structures (2017) Smart Materials and Structures, 26 (18); Zhang, M. Y., Schmidt, R., Markert, B., Structural damage detection methods-based on the correlation functions (2014) Proceedings of the 9th International Conference on Structural Dynamics, , EURODYN, Porto, Portugal; Yang, Z., Wang, L., Wang, H., Ding, Y., Dang, X., Damage Detection in Composite Structures Using Vibration Response under Stochastic Excitation (2009) Journal of Sound and Vibration, 325 (4), pp. 14-16; Huo, L.-S., Li, X., Yan, Y.-B., Li, H.-N., Damage Detection of Structures for Ambient Loading-Based on Cross-Correlation Function Amplitude and SVM (2016) Shock and Vibration, 2016, pp. 1-12; Yang, Z., Yu, Z., Sun, H., On the Cross-Correlation Function Amplitude Vector and its Application to Structural Damage Detection (2007) Mechanical Systems and Signal Processing, 21, pp. 2918-2932; Kunisch, K., Volkwein, S., Galerkin proper orthogonal decomposition methods for parabolic problem (2001) Numerische Mathematik, 90, pp. 117-148; Grepl, M. A., Maday, Y., Nguyen, N. C., Patera, A. T., Efficient reduced-basis treatment of nonaffine and nonlinear partial differential equations (2007) ESAIM Mathematical Modelling and Numerical Analysis, 41 (3), pp. 575-605; Bhouri, M. A., Patera, A. T., (2020) A two-level parameterized model-order reduction approach for time-domain elastodynamics, , arXiv:2002.11084v2 [math.NA, CS.NA]; Bhouri, M.A., (2020) A two-step port-reduced reduced-basis component method for time domain elastodynamic pde with application to structural health monitoring, , Ph.D. thesis, Massachusetts Institute of Technology, phD Thesis, Published; Huynh, D., Knezevic, D., Patera, A., A static condensation reduced basis element method: Approximation and a posteriori error estimation (2013) ESAIM Mathematical Modelling and Numerical Analysis, 47 (1), pp. 213-251; Huynh, D., Knezevic, D., Patera, A., A static condensation reduced basis element method: Complex problems (2013) Computer Methods in Applied Mechanics and Engineering, 259, pp. 197-216; Eftang, J. L., Patera, A., Port Reduction in Component-Based Static Condensation for Parametrized Problems: Approximation and a Posteriori Error Estimation (2013) International Journal for Numerical Methods in Engineering, 96 (5), pp. 269-302; Eftang, J. L., Patera, A., A port-reduced static condensation reduced basis element method for large component-synthesized structures: approximation and a posteriori error estimation (2014) Advanced Modeling and Simulation in Engineering Sciences, 1 (1), p. 3; Smetana, K., A new certification framework for the port reduced static condensation reduced basis element method (2015) Computer Methods in Applied Mechanics and Engineering, 283, pp. 352-383; Smetana, K., Patera, A. T., Optimal local approximation spaces for component-based static condensation procedures (2016) SIAM Journal on Scientific Computing, 38 (5), pp. A3318-A3356; Antoulas, A. C., Beattie, C. A., Gugercin, S., Interpolatory Model Reduction of Large-Scale Dynamical Systems (2010) Efficient Modeling and Control of Large-Scale Systems, pp. 3-58. , J. Mohammadpour, K. Grigoriadis (Eds), Springer, Boston, MA; Beattie, C., Gugercin, S., (2014) Model Reduction by Rational Interpolation, , arXiv:1409.2140v1[math.NA]; Yu, M., Wu, G., Kong, L., Tang, Y., Tire-Pavement Friction Characteristics with Elastic Properties of Asphalt Pavements (2017) Applied Sciences, 7, p. 1123; Yap, P., Truck tire types and road contact pressures (1989) Proceedings of the 2nd International Symposium on Heavy Vehicle Weights and Dimensions, , The Roads and Transport Association of Canada, Canada; Feng, M. Q., Lee, S. C., (2009) Determining the effective system damping of highway bridges, , Tech. Rep. CA-UCI-2009-001, California Department of Transportation, Sacramento, CA; Musiał, M., Grosel, J., Determining the young's modulus of concrete by measuring the eigenfrequencies of concrete and reinforced concrete beams (2016) Construction and Building Materials, 121, pp. 44-52. , Elsevier; Culmo, M. P., (2009) Connection Details for Prefabricated Bridge Elements and Systems, , Tech. Rep. FHWA-IF-09-010, Federal Highway Administration, Washington, DC; Barth, F., Frosch, J. R., Abou-Zeid, M., Allen, H. J., Barlow, J. P., Brander, M. E., Carlson, K., Zielinski, Z. A., (2001) Control of Cracking of Concrete Structures, , Tech. Rep. ACI 224R-01, American Concrete Institute, Farmington Hills, MI; Balakumaran, S. S. G., Weyers, R. E., Brown, M. C., (2018) Linear Cracking in Bridge Decks, , Tech. Rep. FHWA/VTRC18-R13, Virginia Transportation Research Council, Charlottesville, VA; Logan, A., Choi, W., Mirmiran, A., Rizkalla, S., Zia, P., Short-Term Mechanical Properties of High-Strength Concrete (2009) ACI Materials Journal, 106, pp. 413-418; Gindy, M., Nassif, H. H., Velde, J., Bridge Displacement Estimates from Measured Acceleration Records (2007) Transportation Research Record, 2028 (1), pp. 136-145; Jo, B. W., Lee, Y. S., Jo, J. H., Computer Vision-based Bridge Displacement Measurements using Rotation-Invariant Image Processing Technique (2018) Sustainability, 10 (6), pp. 1-16. , K. R. M. A","Bhouri, M.A.; Massachusetts Institute of TechnologyUnited States",,"Computers and Information in Engineering Division;Design Engineering Division","American Society of Mechanical Engineers (ASME)","33rd Conference on Mechanical Vibration and Sound, VIB 2021, Held as Part of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2021","17 August 2021 through 19 August 2021",,174204,,9780791885475,,,"English","Proc. ASME Des. Eng. Tech. Conf.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85119984148
"de Freitas Bello V.S., Popescu C., Blanksvärd T., Täljsten B., Popescu C.","57338405600;56272949500;20336636900;8703323300;56272949500;","Framework for facility management of bridge structures using digital twins",2021,"IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs",,,,"629","637",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119053364&partnerID=40&md5=2c97d419b5e6fffe2cd8d175aa0698b7","Luleå University of Technology (LTU), Luleå, Sweden; SINTEF Narvik AS, Narvik, 8517, Norway","de Freitas Bello, V.S., Luleå University of Technology (LTU), Luleå, Sweden; Popescu, C., Luleå University of Technology (LTU), Luleå, Sweden, SINTEF Narvik AS, Narvik, 8517, Norway; Blanksvärd, T., Luleå University of Technology (LTU), Luleå, Sweden; Täljsten, B., Luleå University of Technology (LTU), Luleå, Sweden; Popescu, C., Luleå University of Technology (LTU), Luleå, Sweden, SINTEF Narvik AS, Narvik, 8517, Norway","The maturity of Digital Twin (DT) models has evolved in the aerospace and manufacturing industries; however, the construction industry still lags behind. DT technology can be applied to achieve smart management through the entire life cycle of structures. Particularly for bridge structures, which play an essential role in any transportation system and can have high maintenance demands throughout their long life spans. In this study, a literature review on DTs was performed, from the origins of the concept until current best practice focused on bridges. Especially concerning structural analysis and facility management, few studies that employ DT for bridges were encountered. The main challenges identified are related to treatment of the large amount of data involved in the process, mostly gathered from different platforms. Finally, a framework for smart facility management of bridges using DTs was proposed to tackle potential solutions. © 2021 IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs. All rights reserved.","BIM; Bridges; BrIM; Digital twins; Facility management; Review","Architectural design; Bridges; Construction industry; Life cycle; Structural design; BIM; Bridge structures; BrIM; Entire life cycles; Facilities management; Lifespans; Long life; Maintenance demand; Manufacturing industries; Transportation system; Office buildings",,,,,"Energimyndigheten","This work was carried out within the strategic innovation program InfraSweden2030, a joint venture by Vinnova, Formas and The Swedish Energy Agency. The work is also funded by SBUF (construction industry's organisation for research and development in Sweden) and Skanska Sweden.",,,,,,,,,,"Woodward, R., Cullington, D. W., Daly, A. F., Vassie, P. R., Haardt, P., Kashner, R., Astudillo, R., Cremona, C., (2001) Bridge management in Europe (BRIME) - Deliverable D14-Final Report; Hurt, M., Schrock, S., Chapter 1 - Introduction (2016) Highway Bridge Maintenance Planning and Scheduling, pp. 1-30; Lu, Q., Xie, X., Heaton, J., Parlikad, A. K., Schooling, J., From BIM towards digital twin: Strategy and future development for smart asset management (2020) Studies in Computational Intelligence, 853, pp. 392-404; Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W., Digital twin in manufacturing: a categorical literature review and classification (2018) IFAC Papers On Line, 51 (11), pp. 1016-1022; Grieves, M., Vickers, J., (2017) Digital Twin: mitigating unpredictable, undesirable emergent behavior in complex systems, pp. 85-113; Cimino, C., Negri, E., Fumagalli, L., Review of digital twin applications in manufacturing (2019) Computers in Industry, 113. , (103130); Negri, E., Fumagalli, L., Macchi, M., A review of the roles of Digital Twin in CPS-based production systems (2017) Procedia Manufacturing, 11, pp. 939-948; Shafto, M., Conroy, M., Doyle, R., Glaessgen, E., Kemp, C., LeMoigne, J., Wang, L., DRAFT Modeling, Simulation, Information Technology & Processing Roadmap (2010) Technology Area, 11; Shim, C.-S., Dang, N.-S., Lon, S., Jeon, C.-H., Development of a bridge maintenance system for prestressed concrete bridges using 3D digital twin model (2019) Structure and Infrastructure Engineering, 15 (10), pp. 1319-1332; Lu, R., Brilakis, I., Digital twinning of existing reinforced concrete bridges from labelled point clusters (2019) Automation in Construction, 105. , (102837); Huthwohl, P., Brilakis, I., Borrmann, A., Sacks, R., Integrating RC bridge defect information into BIM models (2018) Journal of Computing in Civil Engineering, 32 (3), pp. 1-14. , (04018013); Ye, C., Butler, L., Calka, B., Iangurazov, M., Lu, Q., Gregory, A., Girolami, M., Middleton, C., A digital twin of bridges for structural health monitoring (2019) Proceedings of the 12th International Workshop on Structural Health Monitoring; Andersen, J., Rex, S., Concrete bridge deck condition assessment using IR Thermography and Ground Penetrating Radar technologies (2019) 20th Congress of IABSE, , New York City 2019: The Evolving Metropolis; Lu, Q., Parlikad, A. K., Woodall, P., Don Ranasinghe, G., Xie, X., Liang, Z., Konstantinou, E., Schooling, J., Developing a digital twin at building and city levels: Case study of West Cambridge campus (2020) Journal of Management in Engineering, 36. , (05020004); Khajavi, S. H., Motlagh, N. H., Jaribion, A., Werner, L. C., Holmstrom, J., Digital twin: Vision, benefits, boundaries, and creation for buildings (2019) IEEE Access, 7, pp. 147406-147419; Tao, F., Zhang, M., Liu, Y., Nee, A. Y. C., Digital twin driven prognostics and health management for complex equipment (2018) CIRP Annals, 67, pp. 169-172; Jouan, P., Hallot, P., Digital twin: Research framework to support preventive conservation policies (2020) ISPRS International Journal of Geo-Information, 9 (228); Xu, Y., Sun, Y., Liu, X., Zheng, Y., A digital-twin-assisted fault diagnosis using deep transfer learning (2019) IEEE Access, 7, pp. 19990-19999; Xu, Y., Turkan, Y., BrIM and UAS for bridge inspections and management (2019) Engineering, Construction and Architectural Management, 27 (3), pp. 785-807; Abu Dabous, S., Yaghi, S., Alkass, S., Moselhi, O., Concrete bridge deck condition assessment using IR Thermography and Ground Penetrating Radar technologies (2017) Automation in Construction, 81, pp. 340-354; Delgado, J. M. D., Brilakis, I., Middleton, C., Modelling, management, and visualisation of structural performance monitoring data on BIM (2016) Proceedings of the International Conference on Smart Infrastructure and Construction, ICSIC, pp. 543-549; Delgado, J. M. D., Butler, L. J., Gibbons, N., Brilakis, I., Elshafie, M. Z. E. B., Middleton, C., Management of structural monitoring data of bridges using BIM (2017) Bridge Engineering, 170, pp. 204-218; Chan, B., Guan, H., Hou, L., Jo, J., Blumenstein, M., Wang, J., Defining a conceptual framework for the integration of modelling and advanced imaging for improving the reliability and efficiency of bridge assessments (2016) Journal of Civil Structural Health Monitoring, 6, pp. 703-714; Sacks, R., Kedar, A., Borrmann, A., Ma, L., Brilakis, I., Hüthwohl, P., Daum, S., Muhic, S., SeeBridge as next generation bridge inspection: Overview, information delivery manual and model view definition (2018) Automation in Construction, 90, pp. 134-145; Isailovic, D., Stojanovic, V., Trapp, M., Richter, R., Hajdin, R., Döllner, J., Bridge damage: Detection, IFC-based semantic enrichment and visualization (2020) Automation in Construction, 112. , (103088); Boddupalli, C., Sadhu, A., Azar, E. R., Pattyson, S., Improved visualization of infrastructure monitoring data using building information modeling (2019) Structure and Infrastructure Engineering, 15 (9), pp. 1247-1263; Riveiro, B., Jauregui, D. V., Arias, P., Armesto, J., Jiang, R., An innovative method for remote measurement of minimum vertical underclearance in routine bridge inspection (2012) Automation in Construction, 25, pp. 34-40; Borin, P., Cavazzini, F., Condition assessment of RC bridges integrating machine learning, photogrametry and BIM (2019) International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, pp. 201-208. , XLII-2/W15; Morgenthal, G., Hallermann, N., Kersten, J., Taraben, J., Debus, P., Helmrich, M., Rodehorst, V., Framework for automated UAS-based structural condition assessment of bridges (2019) Automation in Construction, 97, pp. 77-95; McGuire, B., Atadero, R., Clevenger, C., Ozbek, M., Bridge information modeling for inspection and evaluation (2016) Journal of Bridge Engineering, 21 (4), p. 04015076","de Freitas Bello, V.S.; Luleå University of Technology (LTU)Sweden; email: vanessa.saback.de.freitas@ltu.se","Snijder H.H.De Pauw B.De Pauw B.van Alphen S.F.C.Mengeot P.","Allplan;et al.;Greisch;Infrabel;Royal HaskoningDHV;TUC RAIL","International Association for Bridge and Structural Engineering (IABSE)","IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs","22 September 2021 through 24 September 2021",,172892,,,,,"English","IABSE Congr., Ghent: Struct. Eng. Future Soc. Needs",Conference Paper,"Final","",Scopus,2-s2.0-85119053364
"van de Velde M., Maes K., Lombaert G.","57338041900;55445655400;8887034500;","Modelling the non-linear behaviour of the pot bearings of railway bridge KW51",2021,"IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs",,,,"404","411",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119052521&partnerID=40&md5=64e95ce3714740b497ab710a5f554c23","Department of Civil Engineering, KU Leuven, Leuven, Belgium","van de Velde, M., Department of Civil Engineering, KU Leuven, Leuven, Belgium; Maes, K., Department of Civil Engineering, KU Leuven, Leuven, Belgium; Lombaert, G., Department of Civil Engineering, KU Leuven, Leuven, Belgium","Railway bridge KW51 in Leuven, Belgium, has been monitored since October 2018 with the aim of constructing a digital twin, i.e. a virtual representation that mimics the behaviour of the actual structure. A linear finite element model of the bridge was updated using measurements carried out on the bridge. The pot bearings of the bridge, however, are found to behave in a non-linear way. This paper describes a methodology to account for this non-linear behaviour in the model, where friction in the bearings is accounted for by means of non-linear Bouc-Wen elements. The first results are presented, showing that the overall non-linear behaviour of the bearings during a train passage is well captured but that further research is needed to calibrate the model parameters. © 2021 IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs. All rights reserved.","Bouw-Wen friction force; Digital twin; Pot bearings; Railway bridge KW51","Friction; Railroads; Structural design; Belgium; Bouw-wen friction force; Friction force; Linear finite element model; Non linear; Nonlinear behaviours; Pot bearing; Railway bridge kw51; Railway bridges; Virtual representations; Railroad bridges",,,,,"Fonds Wetenschappelijk Onderzoek, FWO: 12Q9218N, 1511719N","Kristof Maes is a postdoctoral fellow of the Research Foundation Flanders (FWO), Belgium (grant number 12Q9218N). FWO also provided additional funding for the measurements by means of research grant 1511719N. The financial support by FWO is gratefully acknowledged.",,,,,,,,,,"Davila Delgado, M, Butler, L, Brilakis, I, Elshafie, M, Middleton, C., Structural Performance Monitoring Using a Dynamic Data-Driven BIM Environment (2018) Journal of Computing in Civil Engineering, 32 (3), p. 04018009; Maes, K, Lombaert, G., Monitoring railway bridge KW51 before, during, and after retrofitting (2021) Journal of Bridge Engineering, 26 (3), p. 04721001; Constantinou, M, Mokha, A, Reinhorn, A., Teflon Bearings in Base Isolation II: Modeling (1990) Journal of Structural Engineering, 116 (2), pp. 455-474; Bouc, R., Modèle mathématique d'hystérésis: application aux systèmes à un degré de liberté (1969) Acustica, 24 (1), pp. 16-25; Wen, YK., Method of random vibration of hysteretic systems (1976) Journal of the Engineering Mechanics Division, 102 (2), pp. 249-263; Mokha, A, Constantinou, M, Reinhorn, A., Teflon Bearings in Base Isolation I: Testing (1990) Journal of Structural Engineering, 116 (2), pp. 438-454; Dolce, M, Cardone, D, Croatto, F., Frictional Behavior of Steel-PTFE Interfaces for Seismic Isolation (2005) Bulletin of Earthquake Engineering, 3, pp. 75-99; Ulker-Kaustell, M, Boschmonar, GF, Isusi, PB, Trillkott, S, Kullberg, C, Karoumi, R., Modelling of Pot Bearings - A Preliminary Study (2018) EXPERIMENTAL VIBRATION ANALYSIS FOR CIVIL STRUCTURES: TESTING, SENSING, MONITORING, AND CONTROL, pp. 343-353. , SPRINGER INTERNATIONAL PUBLISHING AG; Ülker Kaustell, M., (2014) Essential modelling details in dynamic FE-analyses of railway bridges, , [PHD thesis]. KTH School of ABE. Sweden; Dooms, D, De Roeck, G, Degrande, G, Lombaert, G, Schevenels, M, François, S., (2009) StaBIL: A finite element toolbox for MATLAB, , BWM-2009-20. Department of civil engineering. KU Leuven; Maes, K, Lombaert, G., Validation of virtual sensing for the fatigue assessment of steel railway bridges (2020) Proceedings of the 7th International Symposium on Life-Cycle Civil Engineering, IALCCE 2020, , Shanghai, China; Paper submitted; Geudens, J., (2015) An improved static-dynamic model for bridge supports, , [Masters thesis]. KU Leuven. Belgium; Ma, F, Zhang, H, Bockstedte, A, Foliente, G, Paevere, P., Parameter Analysis of the Differential Model of Hysteresis (2004) Journal of Applied Mechanics, 71 (3), pp. 342-349","van de Velde, M.; Department of Civil Engineering, Belgium; email: menno.vandevelde@kuleuven.be","Snijder H.H.De Pauw B.De Pauw B.van Alphen S.F.C.Mengeot P.","Allplan;et al.;Greisch;Infrabel;Royal HaskoningDHV;TUC RAIL","International Association for Bridge and Structural Engineering (IABSE)","IABSE Congress, Ghent 2021: Structural Engineering for Future Societal Needs","22 September 2021 through 24 September 2021",,172892,,,,,"English","IABSE Congr., Ghent: Struct. Eng. Future Soc. Needs",Conference Paper,"Final","",Scopus,2-s2.0-85119052521
"Shu J., Zandi K., Zhao W.","55654267000;57192681171;7403942725;","Automated generation of FE mesh of concrete structures from 3D point cloud using computer vision technology",2021,"Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations - Proceedings of the 10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020",,,,"3300","3303",,,"10.1201/9780429279119-448","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117603784&doi=10.1201%2f9780429279119-448&partnerID=40&md5=af700aaf4f9ffedaace9675f389dcfef","Department of Civil Engineering and Architecture, Zhejiang University, China; Department of Architecture and Civil Engineering, Chalmers University of Technology, Sweden","Shu, J., Department of Civil Engineering and Architecture, Zhejiang University, China; Zandi, K., Department of Architecture and Civil Engineering, Chalmers University of Technology, Sweden; Zhao, W., Department of Civil Engineering and Architecture, Zhejiang University, China","To achieve real-time structural health monitoring (SHM), a concept of digital twin - a digital copy of a structure has been brought up and investigated. It provides an up-to-date virtual model of structures, with the integration of physical as well as data information. The goal of this research is to provide faster and more accurate procedures to capture the spatial information required by a digital twin of a concrete structure using 3D point cloud data. Given that the method is intended for real-scale structures, such as bridges, the work can be divided to 3 steps: (1) to segment and extract geometric information for structural components; (2) to convert the geometry information to FE mesh with consideration of element types; (3) to assign material property as well as boundary conditions based on extracted components type. Linear FE analyses have been carried out to evaluate the structural performance based on the FE model created from the point cloud. The automation of such a process is an essential part of the creation of a digital twin of infrastructures. © 2021 Taylor & Francis Group, London",,"Computer vision; Concrete buildings; Concrete construction; Concretes; Structural health monitoring; 3D point cloud; Automated generation; Computer vision technology; Data informations; Digital copy; Physical information; Point cloud data; Real- time; Spatial informations; Virtual models; Mesh generation",,,,,"Stanford University, SU; H2020 Marie Skłodowska-Curie Actions, MSCA: 754412","The research was conducted during the visiting time in Structures and Composites Laboratory (SACL) at Stanford University. The authors acknowledge the support from Marie Skłodowska-Curie Actions (Grant Agreement Nr. 754412) and also thank Dr. Cruz Carlos in Concrete Lab Manager at The University of California, Berkeley, Prof. Hana Mori Böttger and Mohammad Shooshtarian at The University of San Francisco for providing the cracked specimens to collect point cloud data and experimental data.",,,,,,,,,,"Pauly, M., Point primitives for interactive modeling and processing of 3D geometry (2003) Hartung-Gorre, (15134), pp. 1-168. , https://doi.org/10.3929/ethz-a-004612876; Söderkvist, I., (2009) Using SVD for some fitting problems, (2), pp. 2-5. , https://www.ltu.se/cms_fs/1.51590!/svd-fitting.pdf, Retrieved from; (2019) Diana Finite Element Analysis, , TNO DIANA BV. TNO DIANA BV; Zhou, Q.-Y., Park, J., Koltun, V., (2018) Open3D: A Modern Library for 3D Data Processing, , http://arxiv.org/abs/1801.09847, ArXiv: 1801.09847. Retrieved from",,"Yokota H.Frangopol D.M.",,"CRC Press/Balkema","10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020","11 April 2021 through 15 April 2021",,172353,,9780429279119; 9780367232788,,,"English","Bridge Maint., Saf., Manag., Life-Cycle Sustain. Innov. - Proc. Int. Conf. Bridge Maint., Saf. Manag., IABMAS",Conference Paper,"Final","",Scopus,2-s2.0-85117603784
"Linneberg P.","55315301500;","From condition to performance assessment of bridges - The challenge",2021,"Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations - Proceedings of the 10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020",,,,"3201","3208",,,"10.1201/9780429279119-434","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117573286&doi=10.1201%2f9780429279119-434&partnerID=40&md5=6c75217a383a0461138d82090d158c4e","COWI A/S, Kongens Lyngby, Denmark","Linneberg, P., COWI A/S, Kongens Lyngby, Denmark","Most infrastructure managers use condition rating as one of the most important parameters when prioritising maintenance work. Condition rating is a 'weak' measure loosely correlated to safety and serviceability. Projects, such as COST TU1406, propose a transition from condition to performance assessment when prioritising inspection and maintenance. Key Performance Indicators (KPI) could be Reliability, Availability, Safety, Environment and Economy. Integration of these KPI's may call for a risk-based approach considering the associated consequences. Most existing Bridge Management Systems (BMS) do not work with geometrical models, which are essential when dealing with bridge performance. Current advancement with digitalisation using Building Information Modelling (BIM) may be the answer to overcome this problem. BIM shall among other things include realistic structural systems, load situations and deterioration modelling. Bridge management may utilise a population-based approach or a defect-based approach for deterioration modelling. The population-based approach has some advantages in relation to inspection and maintenance planning but requires spatial homogeneity. The defect-based approach may utilise the benefit of a simple digital twin but may also impose some challenges to bridge management. © 2021 Taylor & Francis Group, London",,"Architectural design; Bridges; Defects; Deterioration; Life cycle; Maintenance; Safety engineering; Bridge management; Building Information Modelling; Condition; Deterioration modeling; Infrastructure managers; Inspection and maintenance; Key performance indicators; Maintenance work; Performance assessment; Risk-based approach; Benchmarking",,,,,,,,,,,,,,,,"Hajdin, R., Kusar, M., Masovic, S., Linneberg, P., Amado, J., Tanasic, N., (2018) WG3 Technical report, Establishment of a Quality Control Plan, , COST TU1406, ISBN: 978-86-7518-200-9; Dann, M. R., Maes, M. A., Value of information-based inspection planning using a population approach (2019) IABSE Guimaraes, , Dann et al, 2019; Faber, M.H., Sorensen, J.D., Indicators for Assessment and Inspection Planning of concrete structures (2002) Journal of Structural Safety, 24, pp. 377-396; Faber, M.H., Sorensen, J.D., Indicators for assessment and inspection planning (2000) Workshop on Risk and Reliability Based Inspection Planning, , ETH Zürich; , 1. , Fib Model Code, fib Model Code 2010 and 2, fib bulletin 65 and 66, ISBN 978-2-88394-105-2 and ISBN 978-2-88394-106-9; Hajdin, Hajdin, R., Casas, J. R., Matos, J., Inspection of Existing Bridges - Moving on from condition rating (2019) IABSE Guimaraes, , 2019; Hergenröder, M., (1992) For statistical maintenance planning of existing concrete structures affected by carbonation and reinforcement corrosion, , Technical University of Munich, (in German); Horvath, A., We need more accurate and more useful environmental assessment of infrastructure (2019) IABSE Guimaraes 2019; (2016) Digital Europe: Pushing the frontier, capturing the benefits, , www.mckinsey.com/mgi, McKinsey Global Institute; McKenna, T., Minehane, M., O'Keeffe, B., O'Sullivan, G., Ruane, K., Bridge information modelling (BrIM) for a listed viaduct (2016) Bridge Engineering, 170 (BE3). , McKenna et al, 2016","Linneberg, P.; COWI A/SDenmark","Yokota H.Frangopol D.M.",,"CRC Press/Balkema","10th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2020","11 April 2021 through 15 April 2021",,172353,,9780429279119; 9780367232788,,,"English","Bridge Maint., Saf., Manag., Life-Cycle Sustain. Innov. - Proc. Int. Conf. Bridge Maint., Saf. Manag., IABMAS",Conference Paper,"Final","",Scopus,2-s2.0-85117573286
"Shao S., Deng G., Zhou Z.","57215084079;57201408119;15830628600;","Structural Geometric Morphology Monitoring for Bridges Using Holographic Visual Sensor",2021,"Lecture Notes in Civil Engineering","128",,,"3","13",,,"10.1007/978-3-030-64908-1_1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102276416&doi=10.1007%2f978-3-030-64908-1_1&partnerID=40&md5=68acbcfe0f191dcc52bd95831ef40ec8","College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China; College of Civil Engineering, Chongqing Jiaotong University, Chongqing, China; State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing, China","Shao, S., College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China, College of Civil Engineering, Chongqing Jiaotong University, Chongqing, China, State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing, China; Deng, G., College of Civil Engineering, Chongqing Jiaotong University, Chongqing, China, State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing, China; Zhou, Z., College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China","Full-field noncontact structural geometry morphology monitoring can be used to achieve a breakthrough in the fields of structural safety monitoring and digital twins owing to its advantages of economy, credibility, high frequency, and holography. Moreover, such type of monitoring can improve the precision and efficiency of the structural health monitoring technology and theory of large-scale structures. This study validated the performance of a proposed holographic visual sensor and algorithms in computer vision-based, full-field, noncontact displacement and vibration measurement. On the basis of the temporal and spatial characteristics of the measured series data, denoising, and the disturbance-rejection algorithm, the microscopy algorithm of subpixel motion and the extracting algorithm of motion information were respectively constructed for weak high-order displacement components and the holographic measurement of high-quality geometric morphology. Moreover, an intelligent perception method optimized for holographic-geometric and operational-modal shapes were used to extract morphological features from a series of holographic transient responses under excitation. Experimental results showed that the holographic visual sensor and the proposed algorithms can extract an accurate holographic displacement signal and factually and sensitively accomplish vibration measurement while accurately reflecting the actual change in structural properties under various damage/action conditions. The accuracy and efficiency of the system in the structural geometry monitoring for dense full-field displacement measurement and smooth operational modal shape photogrammetry were confirmed in the experiments. The proposed method could serve as a foundation for further research on digital twins for large-scale structures, structural condition assessment, and intelligent damage identification methods. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Dense full-field displacement measurement; Digital twins; Holographic visual sensor; Operational modal shapes photogrammetry; Structural geometry morphology monitoring; System identification","Damage detection; Digital twin; Displacement measurement; Disturbance rejection; Efficiency; Geometry; Holography; Monitoring; Morphology; Transient analysis; Vibration measurement; Displacement components; Disturbance rejection algorithm; Holographic measurement; Intelligent perception; Large scale structures; Morphological features; Structural health monitoring technology; Structural safety monitoring; Structural health monitoring",,,,,,,,,,,,,,,,"Feng, D.M., Feng, M.Q., Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection-a review (2018) Eng. Struct., 156, pp. 105-117; Review on China’s bridge engineering research: 2014. China (2014) J. Highw. Transp, 27 (5), pp. 1-96; Shao, S., Zhou, Z.X., Deng, G.J., Wang, S.R., Experiment of structural morphology monitoring for bridges based on non-contact remote intelligent perception method (2019) China J. Highw. Transp., 32 (11), pp. 91-102; Sun, L.M., Shang, Z.Q., Xia, Y., Development and prospect of bridge structural health monitoring in the context of big data (2019) China J. Highw. Transp., 32 (11), pp. 1-20; Ye, X.W., Dong, C.Z., Review of computer vision-based structural displacement monitoring (2019) China J. Highw. Transp., 32 (11), pp. 20-39; Bao, Y.Q., Li, H., Ou, J.P., Emerging data technology in structural health monitoring: Compressive sensing technology (2012) J. Civ. Struct. Health Monit., 4 (2), pp. 77-90; Bao, Y.Q., Yu, Y., Li, H., Mao, X.Q., Jiao, W.F., Zou, Z.L., Ou, J.P., Compressive sensing based lost data recovery of fast-moving wireless sensing for structural health monitoring (2015) Struct. Control Health Monit., 22 (3), pp. 433-448; Javh, J., Slavič, J., Boltežar, M., The subpixel resolution of optical-flow-based modal analysis (2017) Mech. Syst. Signal Process., 88, pp. 89-99; Guo, J., Zhu, C.A., Dynamic displacement measurement of large-scale structures based on the Lucas-Kanade template tracking algorithm (2016) Mech. Syst. Signal Process., 66, pp. 425-436; Yang, Y.C., Dorn, C., Mancini, T., Talken, Z., Kenyon, G., Farrar, C., Mascareñas, D., Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification (2017) Mech. Syst. Signal Process., 85, pp. 567-590; Xu, Y., Brownjohn, J., Kong, D.L., A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge (2018) Struct. Control Health Monit., 25 (5); Cha, Y.J., Chen, J.G., Büyüköztürk, O., Output-only computer vision based damage detection using phase-based optical flow and unscented Kalman filters (2017) Eng. Struct., 132, pp. 300-313; Feng, D.M., Feng, M.Q., Model updating of railway bridge using in situ dynamic displacement measurement under trainloads (2015) J. Bridge Eng., 20; Cha, Y.J., Trocha, P., Büyüköztürk, O., Field measurement-based system identification and dynamic response prediction of a unique MIT building (2016) Sensors, 16, p. 1016; Mei, Q.P., Gül, M., Boay, M., Indirect health monitoring of bridges using Mel-frequency cepstral coefficients and principal component analysis (2019) Mech. Syst. Signal Process., 119, pp. 523-546; Shao, S., Zhou, Z.X., Deng, G.J., Du, P., Jian, C.Y., Yu, Z.Y., Experiment of structural geometric morphology monitoring for bridges using holographic visual sensor (2020) Sensors, 20, p. 1187","Zhou, Z.; College of Civil and Transportation Engineering, China; email: zhixiangzhou@szu.edu.cn","Rizzo P.Milazzo A.",,"Springer Science and Business Media Deutschland GmbH","10th European Workshop on Structural Health Monitoring, EWSHM 2020","1 July 2022 through 1 July 2022",,255199,23662557,9783030649074,,,"English","Lect. Notes Civ. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85102276416
"Liu X., Pan Y., Zhao X.","55717456400;42561644400;57201784667;","Research on key technology of operation and maintenance management of long span railway steel bridge based on BIM",2021,"IABSE Conference, Seoul 2020: Risk Intelligence of Infrastructures - Report",,,,"222","229",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101694807&partnerID=40&md5=7d84a0554c115690534f766c2d92df6b","China Academy of Railway Sciences Corporation Limited, Beijing, China","Liu, X., China Academy of Railway Sciences Corporation Limited, Beijing, China; Pan, Y., China Academy of Railway Sciences Corporation Limited, Beijing, China; Zhao, X., China Academy of Railway Sciences Corporation Limited, Beijing, China","In order to adapt to the development trend of informatization and intelligence of railway bridge operation and maintenance management, the integration of BIM Technology and large-span railway steel bridge operation and maintenance business becomes more and more urgent. Taking one special railway steel bridge as an example, the division levels of bridge structural parts, structural elements and specific components were defined, and the refined BIM model of bridge was established based on the demand of operation and maintenance. The knowledge base systems of component, defect, inspection and maintenance in bridge was formed. The three terminal BIM management system was developed, and the closed-loop management process of bridge inspection, maintenance and repair based on the BIM model was established. At the same time, the monitoring information could be integrated, which can provide the basis for the formation of bridge digital twins. The research results provide a firmly support for data-driven comprehensive evaluation and conditional maintenance of railway steel bridges. © 2021 IABSE Conference, Seoul 2020: Risk Intelligence of Infrastructures - Report. All rights reserved.","BIM; Maintenance data; Management system; Operation; Railway steel bridge","Digital twin; Knowledge based systems; Maintenance; Railroad bridges; Railroad transportation; Railroads; Steel research; Comprehensive evaluation; Development trends; Inspection and maintenance; Knowledge base system; Monitoring information; Operation and maintenance; Railway steel bridge; Structural elements; Steel bridges",,,,,,,,,,,,,,,,"YANG, Huaizhi, Elementary Discussion on Application of PHM System for Maintenance and Repair in Large Bridges of High Speed Railway [J] (2017) Railway Engineering, (6), pp. 12-16; Xiaoxin, Jiangxin, xinxin, Zhao, (2017) Research on Application Technology of Fault Prediction and Prognostics and Health Management (PHM) System for Long Span Railway Bridges [R], , China Academy of Railway Sciences Corporation Limited; Classification and Coding Standard of Railway Engineering Information Model [J] (2015) Railway Technology Innovation, (1), pp. 8-111. , Railway BIM Alliance; suhua, Zhu, Fracture Analysis and Maintenance and Repair Suggestions for High Strength Bolts in Steel Truss Girder of Super Large High Speed Railway Bridge [J] (2016) Shanghai Railway Science and Technology Co., Ltd, (4), pp. 79-80; (2011) TG / GW 114-2011 Repair Rules of High Speed Railway Bridge and Tunnel Structures (Trial) [S], , Ministry of Railways of the people's Republic of China. Beijing: China Railway Press; PAN, Yongjie, Xinxin, ZHAO, LIU, Xiaoguang, Research and Application on key Technologies of PHM System for Long Span Bridge Based on BIM (2018) Railway Engineering, 58 (1), pp. 5-9. , etc; ZHAO, Xinxin, QIAN, Shengsheng, LIU, Xiaoguang, Image Identification Method for High-Strength Bolt Missing on Railway Bridge Based on Convolution Neural Network (2018) China Railway Science, 39 (4), pp. 56-62","Liu, X.; China Academy of Railway Sciences Corporation LimitedChina; email: 18810622516@163.com",,"Cable Bridge;et al.;Hoban E and C;Hyundai Engineering and Construction;SK E and C;Yooshim Engineering Corporation","International Association for Bridge and Structural Engineering (IABSE)","IABSE Conference Seoul 2020: Risk Intelligence of Infrastructures","9 November 2020 through 10 November 2020",,166862,,9783857481758,,,"English","IABSE Conf., Seoul: Risk Intell. Infrastructures - Rep.",Conference Paper,"Final","",Scopus,2-s2.0-85101694807
"Konikov A., Roitman V.","57192663171;16220339600;","Integrated use of IT - technology in the construction industry",2020,"IOP Conference Series: Materials Science and Engineering","1001","1","012145","","",,,"10.1088/1757-899X/1001/1/012145","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100044638&doi=10.1088%2f1757-899X%2f1001%2f1%2f012145&partnerID=40&md5=5f6f1c1ccb5b29ce1243081fd1e5efa5","Moscow State University of Civil Engineering, 26, Yaroslavskoye shosse, Moscow, 129337, Russian Federation","Konikov, A., Moscow State University of Civil Engineering, 26, Yaroslavskoye shosse, Moscow, 129337, Russian Federation; Roitman, V., Moscow State University of Civil Engineering, 26, Yaroslavskoye shosse, Moscow, 129337, Russian Federation","A number of new IT technologies are now developing successfully: Cloud Computing, Border Computing, Internet of Things, Digital Twins, Machine Vision, Big Data, Deep Learning. The list is constantly updated, as this direction is developing very dynamically. Experience shows that the best effect is achieved when several new IT technologies are used in one technical solution. Often, new solution in the IT area is an incentive to use other modern solutions: the introduction of the Internet of Things required the transition to a new Internet protocol IPv6, the use of wireless networks in the Smart House caused the need to use the technology of ZigBee. The way the combination is very much dependent on the subject area. In some cases, it is effective to combine IT technologies with advanced advances in other technics areas. For example, ""Machine Vision"" can be used in unmanned aerial vehicles (drones) in the construction and operation of high-rise buildings, bridges, etc. The work explores the complex use of IT - technology in the construction industry. © 2020 IOP Conference Series: Materials Science and Engineering.",,,,,,,,,,,,,,,,,,"Hersent, Olivier, Boswarthick, David, Elloumi, Omar, (2012) The Internet of Things: Key Applications and Protocols, p. 370. , Willey; Chernyak, L., IoT platform. Open systems (2012) DBMS, (7); Urban Sensor Data Streams: London 2013 (2013) IEEE Internet Computing, 17 (6), p. 1; Konikov, Alexandr, Kulikova, Ekaterina, Stifeeva, Olga, Research of the possibilities of application of the Data Warehouse in the construction area (2018) MATEC Web of Conferences, 251, p. 03062; Konikov, A., Konikov, G., Big Data is a powerful tool for improving the environment in the construction business (2017) IOP Conference Series: Earth and Environmental Science, 90, p. 012184; Konikov, A.I., Study of a number of aspects of using Big Data technology in construction (2019) BST Journal, (2), pp. 28-29; Ivanov, Nikolay, Gnevanov, Maxim, Big data: perspectives of using in urban planning and management (2018) MATEC Web of Conferences, 170, p. 01107; Valpeters, M., Kireev, I., Ivanov, N., Application of machine learning methods in big data analytics at management of contracts in the construction industry (2018) MATEC Web of Conferences, 170, p. 01106; Gnevanov, М. V., Ivanov, N. A., Big Data technology - using in urban planning (2018) Industrial and Civil Engineering, (4), pp. 83-87; Maximov, K.V., The effectiveness of the use of cloud computing: methods and models of evaluation (2016) Applied computer science, 1 (81), pp. 106-113; Peripheral calculations (Edge computing) (2019), (11 - 7). , https://www.tadviser.com, TADVISER. Government.Bisiness.IT; Reid, Jack, Rhodes, Donna, Digital system models: An investigation of the non-technical challenges and research needs (2016) Conference on Systems Engineering Research, Systems Engineering Advancement Research Initiative, , Massachusetts Institute of Technology; Computer vision: technologies, market, perspectives (2019) TADVISER. Government.Bisiness.IT, (6 - 26). , https://www.tadviser.com; Lukyanica, A.A, Shishkin, A.G., (2009) AY-ES-ES-PRESS”, p. 518. , A.G. Digital video processing, M; Konikov, A.I., Situational Control Center buildings (2018) Industrial and Civil Engineering, (7), pp. 84-87","Konikov, A.; Moscow State University of Civil Engineering, 26, Yaroslavskoye shosse, Russian Federation; email: KonikovAI@mgsu.ru",,,"IOP Publishing Ltd","2020 International Scientific and Practical Conference on Environmental Risks and Safety in Mechanical Engineering, ERSME 2020","20 October 2020 through 22 October 2020",,166643,17578981,,,,"English","IOP Conf. Ser. Mater. Sci. Eng.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85100044638
"Cardno C.A.","25958626200;","Smart Bridges, Evolved",2020,"Civil Engineering Magazine Archive","90","6",,"42","49",,,"10.1061/ciegag.0001502","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098915378&doi=10.1061%2fciegag.0001502&partnerID=40&md5=3ffbfb11aaf7c9bfaa787619f435d2a5","ASCE, Reston, VA, United States","Cardno, C.A., ASCE, Reston, VA, United States","Bridge instrumentation, digital twins, and automated design - among other tech-focused trends - are helping create ""smart""bridges that will better serve the public for years to come. © 2020 American Society of Civil Engineers.",,"Digital twin; Automated design; Bridge instrumentation; Smart bridge; Bridges; automation; bridge; cellular automaton; design method; digitization; hydrotechnical engineering; trend analysis",,,,,,,,,,,,,,,,,"Cardno, C.A.; ASCEUnited States",,,"American Society of Civil Engineers (ASCE)",,,,,08857024,,CIEGA,,"English","Civ. Eng. Magazine Arch.",Article,"Final","",Scopus,2-s2.0-85098915378
"Cervenka J., Jendele L., Zalsky J., Pukl R., Novak D.","7103036677;6507138049;57222347260;6507525719;7103231214;","Digital twin approach for durability and reliability assessment of bridges",2020,"fib Symposium",,,,"1840","1848",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134807069&partnerID=40&md5=26db7e81805c9080dfb8a42232b3581f","Cervenka Consulting s r o, Prague, Czech Republic; Klokner’s Institute, Czech Technical University, Prague, Czech Republic; Dep of Civil Engineering, Technical University of Brno, Brno, Czech Republic","Cervenka, J., Cervenka Consulting s r o, Prague, Czech Republic; Jendele, L., Cervenka Consulting s r o, Prague, Czech Republic; Zalsky, J., Klokner’s Institute, Czech Technical University, Prague, Czech Republic; Pukl, R., Cervenka Consulting s r o, Prague, Czech Republic; Novak, D., Dep of Civil Engineering, Technical University of Brno, Brno, Czech Republic","Digital twin is a modern concept, in which a digital replica of a real product and structure is developed, and a simulation is performed to test the product behaviour under service conditions. In the presented paper the digital twin method is used for making assessments of safety, durability and reliability of bridge structures. Although some numerical modelling is often done when an existing bridge is evaluated, it usually does not involve the simulation of real behaviour under service and environmental loads including chloride ingress, reinforcement corrosion and assessment of ultimate load carrying capacity. The digital twin concept in addition includes an important aspect of the digital twin calibration and validation using the real monitoring data. The paper presents a chemo-mechanical model covering initiation and propagation of chlorides or carbonation. This model is combined with the nonlinear modelling of cracking, bond failure and reinforcement yielding (Cervenka and Papanikolaou (2008). The paper extents the previously developed model by the authors Hájková et al. (2019), Jendele, Šmilauer and Červenka (2014). The models were implemented in ATENA software and are validated on experimental data. The developed models can be efficiently used in large scale analysis of real engineering problems as demonstrated on applications to an existing bridge structures in Germany. The example simulation using the digital twin concept show time development of reinforcement corrosion due to chloride ingress, and their impact on the evolution of structural safety and reliability. © fédération internationale du béton (fib).","Chloride ingress; Concrete bridges; Corrosion; Durability; Finite element analysis","Chlorine compounds; Concrete buildings; Concrete construction; Durability; Electrochemical corrosion; Load limits; Reinforcement; Reliability; Calibration and validations; Chemo-mechanical model; Chlorides or carbonations; Initiation and propagation; Non-linear modelling; Reinforcement corrosion; Reliability assessments; Ultimate load-carrying capacity; Digital twin",,,,,,,,,,,,,,,,"Crisfield, M.A., An Arc-Length Method Including Line Search and Accelerations (1983) International Journal for Numerical Methods in Engineering, 19, pp. 1269-1289; Červenka, J., Červenka, V., Eligehausen, R., Fracture-Plastic Material Model for Concrete, Application to Analysis of Powder Actuated Anchors (1998) Proc. FRAMCOS, 3 (1998), pp. 1107-1116; Červenka, V., Jendele, L., Červenka, J., (2020) Atenaprogram Documentation - Part1 - Theory, , www.cervenka.cz, Praha: Cervenka Consulting; Červenka, J., Papanikolaou, V.K., (2008), Three dimensional combined fracture-plastic material model for concrete (2008) Int. J. Plast, 24, pp. 2192-2220; Darmawan, M.S., Stewart, M.G., Effect of Pitting Corrosion on Capacity of Prestress-ing Wires (2007) Magazine of Concrete Research, 59 (2), pp. 131-139; (2006) Model Code for Service Life Design, , Fédération Internationale du Béton (fib), Lausanne, Switzerland; Gonzales, J.A., Rade, C., Alonso, C., Feliu, S., Comparison of Rates of General Corrosion and Maximum Pitting Penetration on Concrete Embedded Steel Reinforcement (1995) Cement and Concrete Research, 25 (2), pp. 257-264; Hájková, K., Šmilauer, V., Jendele, L., Červenka, J., Prediction of reinforcement corrosion due to chloride ingress and its effects on serviceability (2019) Engineering Structures, 174, pp. 768-777; Jendele, L., Šmilauer, V., Červenka, J., Multiscale hydro-thermo-mechanical model for early-age and mature concrete structures (2014) Adv. Eng. Software, p. 2014; Kwon, S.J., Na, U.J., Park, S.S., Jung, S.H., Service life prediction of concrete wharves with early-aged crack: Probabilistic approach for chloride diffusion (2009) Struct Safety, 31 (1), pp. 75-83; Liu, Y., (1996) Modelling the Time-To-Corrosion Cracking of the Cover Concrete in Chloride Contaminated Reinforced Concrete Structures, , Virginia: Polytechnic Institute; Liu, T., Weyers, R.W., Modelling the dynamic corrosion process in chloride contaminated concrete structures (1998) Cem Concr Res, 28 (3), pp. 365-367","Cervenka, J.; Cervenka Consulting s r oCzech Republic; email: jan.cervenka@cervenka.cz","Zhao B.Lu X.",,"fib. The International Federation for Structural Concrete","International fib Symposium on Concrete structures for resilient society, 2020","22 November 2020 through 24 November 2020",,267619,26174820,9782940643042,,,"English","fib. Symp.",Conference Paper,"Final","",Scopus,2-s2.0-85134807069
"Ye C., Middleton C., Kuok S.-C., Butler L.","57216481422;7005340597;36015370900;55795448200;","Challenges of implementing bridge model updating in industry practice",2020,"IABSE Congress, Christchurch 2020: Resilient Technologies for Sustainable Infrastructure - Proceedings",,,,"464","470",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104791751&partnerID=40&md5=b803a6eb674a8c376bfdcc3006494a81","University of Cambridge, Cambridge, United Kingdom; University of Macau, Macau; York University, Toronto, Canada","Ye, C., University of Cambridge, Cambridge, United Kingdom; Middleton, C., University of Cambridge, Cambridge, United Kingdom; Kuok, S.-C., University of Macau, Macau; Butler, L., York University, Toronto, Canada","Model updating aims to update an analysis model (e.g. a finite element model) of an engineering structure in order to closely represent the true condition and performance of the physical structure. Model updating of bridges has been an active research field for more than two decades, yet the confidence and practical usefulness of bridge model updating results may be subject to questioning. While model updating may have worked well for many other engineering applications, it has found to be challenging and problematic to implement such practice on bridge structures. More recently, there has been a vision of developing bridge digital twins which can automatically update the model in near real time as new monitoring data become available. This paper aims to elaborate on the critical issues that have not been addressed properly to enable real-world implementation of bridge model updating. A series of industry facing semi-structured interviews have been conducted with 19 bridge professionals (owners, operators and consultants) to aid in investigating the technical and practical challenges of implementing bridge model updating in practice. It is envisioned that the outcomes of this paper will inform future research regarding model updating and digital twin development for bridge applications. © 2020 IABSE Congress, Christchurch 2020: Resilient Technologies for Sustainable Infrastructure - Proceedings. All rights reserved.","Bridge; Implementation; Model updating; Structural health monitoring","Digital twin; Bridge applications; Bridge structures; Engineering applications; Engineering structures; Industry practices; Physical structures; Real-world implementation; Semi structured interviews; Bridges",,,,,"Engineering and Physical Sciences Research Council, EPSRC","The authors would like to thank the 19 bridge professionals for participating in the industry facing interviews. The first author would also like to thank the EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment for providing travel fund for the interviews.",,,,,,,,,,"Glisic, B., (2015) Branko Glisic Home Page, , http://glisicstructuralhealthmonitoring.princeton.edu/, [Internet]. [cited 2020 Jan 2]; Webb, G.T., Vardanega, P.J., Middleton, C.R., Categories of SHM Deployments: Technologies and Capabilities (2014) ASCE Journal of Bridge Engineering, 20 (11), p. 04014118; Webb, G.T., (2014) Structural Health Monitoring of Bridges, , University of Cambridge; Aghagholizadeh, M., Catbas, F.N., A Review of Model Updating Methods for Civil Infrastructure Systems (2015) Computational Techniques for Civil and Structural Engineering, pp. 83-99. , Stirlingshire, UK: Saxe-Coburg Publications; Simoen, E., De Roeck, G., Lombaert, G., Dealing with uncertainty in model updating for damage assessment: A review (2015) Mechanical Systems and Signal Processing, 56-57, pp. 123-149; Theofanous, M., Gardner, L., Testing and numerical modelling of lean duplex stainless steel hollow section columns (2009) Engineering Structures, 31 (12), pp. 3047-3058; Xu, J., Butler, L.J., Elshafie, M.Z.E.B., Experimental and numerical investigation of the performance of self-sensing concrete sleepers (2020) Structural Health Monitoring, 19 (1), pp. 66-85; Bentz, E.C., Hoult, N.A., Bridge model updating using distributed sensor data (2017) Proceedings of the Institution of Civil Engineers - Bridge Engineering, 170 (1), pp. 74-86; Daniell, W.E., Macdonald, J.H.G., Improved finite element modelling of a cable-stayed bridge through systematic manual tuning (2007) Engineering Structures, 29 (3), pp. 358-371; Brownjohn, J.M.W., Xia, P.-Q., Hao, H., Xia, Y., Civil structure condition assessment by FE model updating: methodology and case studies (2001) Finite Elements in Analysis and Design, 37 (10), pp. 761-775; Lea, F.C., Middleton, C.R., (2002) Reliability of Visual Inspection of Highway Bridges, , University of Cambridge; Moore, M., Phares, B., Graybeal, B., Rolander, D., Washer, G., (2001) Reliability of Visual Inspection for Highway Bridges, Volume I: Final Report, , Federal Highway Administration; (2019) CS 454 Assessment of Highway Bridges and Structures, , Highways England; (2018) UK Network Rail Standards, , Network Rail","Ye, C.; University of CambridgeUnited Kingdom; email: cy273@cam.ac.uk","Abu A.","Arup;Aurecon;Granor Rubber and Engineering;Sika;TJAD;WSP","International Association for Bridge and Structural Engineering (IABSE)","IABSE Congress Christchurch 2020: Resilient Technologies for Sustainable Infrastructure","3 February 2021 through 5 February 2021",,168364,,9783857481703,,,"English","IABSE Congress, Christchurch: Resilient Technol. Sustain. Infrastr. - Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85104791751
"Cervenka J., Jendele L., Zalsky J., Pukl R., Novak D.","7103036677;6507138049;57222347260;6507525719;7103231214;","Digital twin approach for durability and reliability assessment of bridges",2020,"Proceedings of the fib Symposium 2020: Concrete Structures for Resilient Society",,,,"1840","1848",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102411016&partnerID=40&md5=701f028050f7d458648fc048f6069e47","Prague, Czech Republic; Klokner's Institute, Czech Technical University, Prague, Czech Republic; Dep. of Civil Engineering, Technical University of Brno, Brno, Czech Republic","Cervenka, J., Prague, Czech Republic; Jendele, L., Prague, Czech Republic; Zalsky, J., Klokner's Institute, Czech Technical University, Prague, Czech Republic; Pukl, R., Prague, Czech Republic; Novak, D., Dep. of Civil Engineering, Technical University of Brno, Brno, Czech Republic","Digital twin is a modern concept, in which a digital replica of a real product and structure is developed, and a simulation is performed to test the product behaviour under service conditions. In the presented paper the digital twin method is used for making assessments of safety, durability and reliability of bridge structures. Although some numerical modelling is often done when an existing bridge is evaluated, it usually does not involve the simulation of real behaviour under service and environmental loads including chloride ingress, reinforcement corrosion and assessment of ultimate load carrying capacity. The digital twin concept in addition includes an important aspect of the digital twin calibration and validation using the real monitoring data. The paper presents a chemo-mechanical model covering initiation and propagation of chlorides or carbonation. This model is combined with the nonlinear modelling of cracking, bond failure and reinforcement yielding (Cervenka and Papanikolaou (2008). The paper extents the previously developed model by the authors Hájková et al. (2019), Jendele, Šmilauer and Cervenka (2014). The models were implemented in ATENA software and are validated on experimental data. The developed models can be efficiently used in large scale analysis of real engineering problems as demonstrated on applications to an existing bridge structures in Germany. The example simulation using the digital twin concept show time development of reinforcement corrosion due to chloride ingress, and their impact on the evolution of structural safety and reliability. © Proceedings of the fib Symposium 2020: Concrete Structures for Resilient Society. All rights reserved.","Chloride ingress; Concrete bridges; Corrosion; Durability; Finite element analysis","Chlorine compounds; Concrete buildings; Concrete construction; Durability; Electrochemical corrosion; Load limits; Reinforcement; Reliability; Calibration and validations; Chemo-mechanical model; Chlorides or carbonations; Initiation and propagation; Non-linear modelling; Reinforcement corrosion; Reliability assessments; Ultimate load-carrying capacity; Digital twin",,,,,"Grantová Agentura České Republiky, GA ČR; Technologická Agentura České Republiky: TF06000016","The monitoring program was part of an international Eurostars-2 project E! 10925 “cyberBridge”. The application of global safety formats was supported by the project from Czech Grant Agency 20-01781S Uncertainty modelling in safety formats of concrete structures. The optimization of material parameters was using the tools and methods developed under the project supported by Technological Agency of Czech Republic - TF06000016, Advanced system for monitoring, diagnosis and reliability assessment of large-scale concrete infrastructures.",,,,,,,,,,"Crisfield, M.A., An Arc-Length Method Including Line Search and Accelerations (1983) International Journal for Numerical Methods in Engineering, 19, pp. 1269-1289; Cervenka, J., Cervenka, V., Eligehausen, R., Fracture-Plastic Material Model for Concrete, Application to Analysis of Powder Actuated Anchors (1998) Proc. FRAMCOS, 3, pp. 1107-1116. , 1998; Cervenka, V, Jendele, L, Cervenka, J., (2020) ATENAProgram documentation - Part1 - Theory, , www.cervenka.cz, Praha: Cervenka Consulting; Cervenka, J., Papanikolaou, V.K., Three dimensional combined fracture-plastic material model for concrete (2008) Int. J. Plast, 24, pp. 2192-2220. , 2008; Darmawan, M.S., Stewart, M.G., Effect of Pitting Corrosion on Capacity of Prestress-ing Wires (2007) Magazine of Concrete Research, 59 (2), pp. 131-139; (2006) Model code for service life design, , fib Bulletin 34 Fédération Internationale du Béton (fib), Lausanne, Switzerland; Gonzales, J. A., Andrade, C., Alonso, C., Feliu, S., Comparison of Rates of General Corrosion and Maximum Pitting Penetration on Concrete Embedded Steel Reinforcement (1995) Cement and Concrete Research, 25 (2), pp. 257-264; Hájková, K., Šmilauer, V., Jendele, L., Cervenka, J., Prediction of reinforcement corrosion due to chloride ingress and its effects on serviceability (2019) Engineering Structures, 174, pp. 768-777; Jendele, L., Šmilauer, V., Cervenka, J., Multiscale hydro-thermo-mechanical model for early-age and mature concrete structures (2014) Adv. Eng. Software 2014; Kwon, SJ, Na, UJ, Park, SS, Jung, SH., Service life prediction of concrete wharves with early-aged crack: probabilistic approach for chloride diffusion (2009) Struct Safety, 31 (1), pp. 75-83; Liu, Y., (1996) Modelling the Time-to-corrosion Cracking of the Cover Concrete in Chloride Contaminated Reinforced Concrete Structures, , Virginia: Polytechnic Institute; Liu, T, Weyers, RW., Modelling the dynamic corrosion process in chloride contaminated concrete structures (1998) Cem Concr Res, 28 (3), pp. 365-367; (2011), Model Code 2010 fib Lausanne. Ernst & Sohn: Switzerland, ISBN 978-3-433-03061-5; Muthena, A, Andrade, C, Nilsson, L-O, Edvardsen, C, (2000) DuraCrete, , Final technical report. Tech. rep.2000; Petschacher, M., (2010) Bridge-Weigh-in-Motion, , ISSN 0379-1491. FSV, Wien; Tang, L, Utgenannt, P, Boubitsas, D., Durability and service life prediction of reinforced concrete structures (2015) J Chin Ceram Soc, 43 (10), pp. 1408-1419","Cervenka, J.Czech Republic; email: jan.cervenka@cervenka.cz","Zhao B.Lu X.","ALLPLAN;Liuzhou OVM Machinery Co., Ltd.","International Federation for Structural Concrete","2020 fib Symposium: Concrete Structures for Resilient Society","22 November 2020 through 24 November 2020",,167100,,9782940643042,,,"English","Proc. fib Symp.: Concrete Struct. Resilient Soc.",Conference Paper,"Final","",Scopus,2-s2.0-85102411016
"Kasireddy V., Wei Y., Akinci B.","57194726207;57202716257;6603543201;","Use cases for owners and maintainers",2019,"Infrastructure Computer Vision",,,,"169","201",,,"10.1016/B978-0-12-815503-5.00004-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093476692&doi=10.1016%2fB978-0-12-815503-5.00004-8&partnerID=40&md5=8fe4f9cc93ef6f06094aacea778b986d","Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, United States","Kasireddy, V., Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, United States; Wei, Y., Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, United States; Akinci, B., Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, United States","The as-is modeling workflow comprises methods that are needed to convert a set of laser scans or images into a 3D model with geometric information with basic semantics, such as labels of structural components. An as-built model with geometric information is sufficient for measurement only. However, an important group of users of as-is models is owners and maintainers, who often care about more than geometric information of facilities and infrastructure systems. For example, owners and maintainers will ask the following questions: Is a bridge still safe to use? How is the energy performance of a building? When do we need to conduct maintenance of a specific infrastructure? Is there a need for retrofitting? Questions like these cannot be answered directly with the general as-is modeling workflow mainly focusing on the low-level details such as geometric information. It is necessary to define a workflow that addresses the requirements from owners’ and maintainers’ perspectives. In Section 4.2, we will present a high-level description of the need for computer vision in infrastructure projects and potential benefits from the perspective of owners and maintainers. In Section 4.3, we will discuss some issues a general workflow encounters when it is used to model a single asset in an infrastructure management setting. In Section 4.4, we define the concept of portfolio modeling-which considers the needs of owners and maintainers by extending the general as-is modeling workflow in terms of space, time, and auxiliary information. Starting from Section 4.5, we will introduce some typical applications from owners and maintainers’ perspectives and discuss what extensions (space, time, and auxiliary information) are required and how they are achieved. We will conclude this chapter by outlining guidelines for owners and maintainers, along with potential challenges they may encounter when adopting infrastructure computer vision in their workflows. © 2020 Elsevier Inc. All rights reserved.","As-is modeling; Computer vision; Digital twin; Energy performance; Imaging; Infrastructure; Laser scan; Maintenance; Managers; Owners",,,,,,,,,,,,,,,,,"Abdel-Qader, I., Pashaie-Rad, S., Abudayyeh, O., Yehia, S., PCA-Based algorithm for unsupervised bridge crack detection (2006) Adv. Eng. Software, 37, pp. 771-778; Adhikari, R.S., Moselhi, O., Bagchi, A., Image-based retrieval of concrete crack properties for bridge inspection (2014) Autom. Construct., 39, pp. 180-194; Akinci, B., Boukamp, F., Gordon, C., Huber, D., Lyons, C., Park, K., A formalism for utilization of sensor systems and integrated project models for active construction quality control (2006) Autom. Construct., 15, pp. 124-138; Alomari, K., Gambatese, J., Olsen, M.J., Role of BIM and 3D laser scanning on job sites from the perspective of construction project management personnel (2016) Constr. Res. 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Struct., 19, p. 015019; Zhang, Z., (2008) A Flexible New Technique For Camera Calibration, 13. , A Typo in Appendix B) (Last Updated on Aug A Flexible New Technique for Camera Calibration; Zhu, Z., (2011) Column Recogniton And Defects/Damage Properties Retrieval For Rapid Infrastructure Assessment and Rehabilitation Using Machine Vision Column Recogniton and Defects/Damage Properties Retrieval For Rapid Infrastructure Assessment and, , Georgia Institute of Technology; Zhu, Z., Brilakis, I., Parra-Montesinos, G., (2009) Real-Time Concrete Damage Visual Assessment for First Responders, pp. 1204-1213. , researchgate.net; Zhu, Z., German, S., Brilakis, I., Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation (2011) Autom. Construct., 20, pp. 874-883",,,,"Elsevier",,,,,,9780128155035,,,"English","Infrastructure Computer Vision",Book Chapter,"Final","",Scopus,2-s2.0-85093476692
"Campos E., Smith N.A.","57211800301;57211807379;","Stakeholder engagement utilising data rich visualisations",2019,"Australasian Coasts and Ports 2019 Conference",,,,"161","166",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075068271&partnerID=40&md5=8fbdf87f1c79354c5ab769244c9a92d9","Cardno, Perth, WA, Australia","Campos, E., Cardno, Perth, WA, Australia; Smith, N.A., Cardno, Perth, WA, Australia","The consultancy engineering and design industries are facing the ongoing challenge of effectively communicating design intent with various project stakeholders. With the constant, fast-moving advancements in technology, innovative methods for delivering projects are being used more frequently in the consulting world. Web portals and story maps are a powerful way to engage with stakeholders and share information such as design reports, construction drawings and technical reports as well as 3D visualisations and imagery. Following positive client response from key pilot projects, these innovative project delivery methods are regularly being used to convey project findings or present solutions in the coast and ports environment. Our experience has shown clients appreciate the visual aspect of this type of delivery rather than solely relying on report-based deliverables. Visualisations offered by this delivery process also help to bridge the gaps between technical and non-technical stakeholders as they provide clearer representation of the project outcomes and how it may affect them. This enables clients to better estimate buy in from stakeholders on projects - especially in the feasibility stage. Projects on the coastline typically have a large variety of interested stakeholders and it is found that by implementing a visual design process, it helps them understand high risk and low risks aspects of the project and in turn, the approvals and the delivery stage of the projects are streamlined. The range of possible outputs is diverse and these can be tailored, based on the client and project needs. Options include, but are not limited to: • Online web portal for internal collaboration within the project team • Online web portal with interactive models • ArcGIS story maps for visual delivery of project reports • Conceptual Models • Conceptual model flythrough footage • Renders • 360 Panoramic renders. • Immersive video footage and photographs • Drone survey and aerial imagery. © Australasian Coasts and Ports 2019 Conference. All rights reserved.","Collaboration; Data; Digital twin; Innovative; Stakeholders","Aerial photography; Antennas; Project management; Rendering (computer graphics); Visualization; Collaboration; Data; Digital twin; Innovative; Stakeholders; Portals",,,,,,,,,,,,,,,,"https://bimforum.org/; https://bim.natspec.org/; https://en.wikipedia.org/wiki/Virtual_reality",,,"BMT;et al.;J Steel;Tasports;The Australian Maritime College (AMC);Wallbridge Gilbert Aztec (WGA)","Australian Coasts and Ports","Australasian Coasts and Ports 2019 Conference","10 September 2019 through 13 September 2019",,152960,,,,,"English","Australas. Coasts Ports Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85075068271
"Lu R.D., Brilakis I.","57194640091;8837673400;","Digital twinning of existing bridges from labelled point clusters",2019,"Proceedings of the 36th International Symposium on Automation and Robotics in Construction, ISARC 2019",,,,"616","623",,,"10.22260/isarc2019/0082","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071493000&doi=10.22260%2fisarc2019%2f0082&partnerID=40&md5=568e9edaf7887dc96a7fd460df0bba38","Department of Engineering, University of Cambridge, United Kingdom","Lu, R.D., Department of Engineering, University of Cambridge, United Kingdom; Brilakis, I., Department of Engineering, University of Cambridge, United Kingdom","The automation of digital twinning for existing bridges from point clouds has yet been solved. Whilst current methods can automatically detect bridge objects in points clouds in the form of labelled point clusters, the fitting of accurate 3D shapes to detected point clusters remains human dependent to a great extent. 95% of the total manual modelling time is spent on customizing shapes and fitting them to right locations. The challenges exhibited in the fitting step are due to the irregular geometries of existing bridges. Existing methods can fit geometric primitives such as cuboids and cylinders to point clusters, assuming bridges are made up of generic shapes. However, the produced geometric digital twins are too ideal to depict the real geometry of bridges. In addition, none of existing methods have evaluated the resulting models in terms of spatial accuracy with quantitative measurements. We tackle these challenges by delivering a slicing-based object fitting method that can generate the geometric digital twin of an existing reinforced concrete bridge from labelled point clusters. The accuracy of the generated models is gauged using distance-based metrics. Experiments on ten bridge point clouds indicate that the method achieves an average modelling distance smaller than that of the manual one (7.05 cm vs. 7.69 cm) (value included all challenging cases), and an average twinning time of 37.8 seconds. Compared to the laborious manual practice, this is much faster to twin bridge concrete elements. © 2019 International Association for Automation and Robotics in Construction I.A.A.R.C. All rights reserved.","BIM; BrIM; Digital twin; IFC; Point cloud data","Architectural design; Object detection; Reinforced concrete; Robotics; BrIM; Concrete elements; Digital twin; Existing reinforced concrete; Geometric primitives; Irregular geometries; Point cloud data; Quantitative measurement; Geometry",,,,,"Engineering and Physical Sciences Research Council, EPSRC","This research is funded by EPSRC, EU Infravation SeeBridge project and Trimble Research Fund. We thank their support.",,,,,,,,,,"(2017) 2017 Report Card for America’s Infrastructure, Bridges, , ASCE ASCE; (2015) Network Rail Bridge List, , Network Rail; Buckley, B., Logan, K., The business value of BIM for infrastructure 2017 (2017) Dodge Data & Analytics, pp. 1-68; Lu, R., Brilakis, I., (2017) Recursive Segmentation for as - Is Bridge Information Modelling, , LC3; Valero, E., Semantic 3d reconstruction of furnished interiors using laser scanning and RFID technology (2016) Jr of Comp in Civil Eng, , https://doi.org/10.1061/(ASCE)CP.19435487.0000525; Valero, E., Adán, A., Cerrada, C., Automatic method for building indoor boundary models from dense point clouds collected by laser scanners (2012) Sensors, , https://doi.org/10.3390/s121216099; Oesau, S., Lafarge, F., Alliez, P., Indoor scene reconstruction using feature sensitive primitive extraction and graph-cut (2014) ISPRS, , https://doi.org/10.1016/j.isprsjprs.2014.02.004\; Deng, Mapping between BIM and 3d GIS in different levels of detail using schema mediation and instance comparison (2016) Aut in Constr, , https://doi.org/10.1016/j.autcon.2016.03.006; Xiao, J., Furukawa, Y., Reconstructing the world’s museums (2014) International Jr of Computer Vision, , https://doi.org/10.1007/s11263-014-0711-y; Zhang, Automatic generation of as-built geometric civil infrastructure models from point cloud data (2014) Comput in Civil and Building Eng, pp. 406-413; Ochmann, Automatic reconstruction of parametric building models from indoor point clouds (2016) Computers & Graphics, , https://doi.org/10.1016/j.cag.2015.07.008; Laefer, D.F., Truong-Hong, L., Toward automatic generation of 3d steel structures for building information modelling (2017) Auto in Constr, , https://doi.org/10.1016/j.autcon.2016.11.011; Ji, Exchange of parametric bridge models using a neutral data format (2013) Journal of Computing in Civil Engineering, , https://doi.org/10.1061/(ASCE)CP.19435487.0000286; Amann, Extension of the upcoming IFC alignment standard with cross sections for road design (2015) ICCBEI; Borrmann, (2018) Industry Foundation Classes: A Standardized Data Model for the Vendor-Neutral Exchange of Digital Building Models, , https://doi.org/10.1007/978-3-319-92862-35; Sacks, SeeBridge as next generation bridge inspection: Overview, information delivery manual and model view definition (2018) Aut in Constr, , https://doi.org/10.1016/j.autcon.2018.02.033; Lu, R., Brilakis, I., Middleton, C., Detection of structural components in point clouds of existing RC bridges (2018) CACAIE, , https://doi.org/10.1111/mice.12407; Kobryń, A., Transition curves for highway geometric design (2017) Springer Tracts on Transportations and Traffic, 14. , Cham: Springer International Publishing",,"Al-Hussein M.",,"International Association for Automation and Robotics in Construction I.A.A.R.C)","36th International Symposium on Automation and Robotics in Construction, ISARC 2019","21 May 2019 through 24 May 2019",,150272,,,,,"English","Proc. Int. Symp. Autom. Robot. Constr., ISARC",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85071493000
"Zitzelsberger J., Vrinceanu L.","8615051000;57195627693;","Electric drives - Enabler for intelligent mechanics how decentralised drive units create an added value in machine building",2017,"17th IEEE International Conference on Smart Technologies, EUROCON 2017 - Conference Proceedings",,,"8011196","674","679",,,"10.1109/EUROCON.2017.8011196","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029369403&doi=10.1109%2fEUROCON.2017.8011196&partnerID=40&md5=3e1753e0f1da1f15db14cefc9baf3c75","Department for Innovation Management, Sys-o-tec Innovation Consulting E.K., Hamburg, Germany; University of Applied Sciences, Hamburg, Germany","Zitzelsberger, J., Department for Innovation Management, Sys-o-tec Innovation Consulting E.K., Hamburg, Germany; Vrinceanu, L., University of Applied Sciences, Hamburg, Germany","Modern mechanical engineering systems are primarily designed to perform a specific system process in an optimal manner. For this purpose, they use control systems that monitor and influence the process with the help of sensors and actuators. Changing control values or (physical) disturbances are recorded and considered by the control systems. Problems arise whenever the disturbances are not physical, but organizational. In these cases, the existing control systems can not react and there are delays and failures in the system process. New trends in automation and information and communication technology can help to bridge this gap in process control by providing intelligent and data-based services in the immediate process environment. Distributed drive systems can assume a key role as they already have the necessary technical requirements. However, these are to be made more flexible. For this purpose, it is necessary to subject both hardware and software production to a systemic approach and to provide the mechanical part of the system process with a digital image. The fact that this increases the complexity in the system production is undisputed. Therefore, in addition to the technical possibilities for solving the problem, the value in use must also be determined and evaluated from the outset in system development. © 2017 IEEE.","added value; administration shell; automation; cloud; control; digital twin; distributed systems; electric drives; information and communication technology; internet of things; process; reference architecture model; system; systems engineering","Automation; Clouds; Control; Control systems; Digital storage; Electric drives; Internet of things; Processing; Systems engineering; Added values; digital twin; Distributed systems; Information and Communication Technologies; Reference architecture; system; Process control",,,,,,,,,,,,,,,,"Leonhard, W., (2001) Control of Electrical Drives, , Springer Verlag Berlin Heidelberg, 3rd edition; Tveter, D.R., The pattern recognition basis of artificial intelligence (1998) IEEE Computer Society; Albright, S.C., Winston, W., Zappe, C., (2010) Data Analysis and Decision Making, , Cengage Learning, 4th edition; The Reference Architectural Model RAMI 4.0, , https://www.zvei.org/en/subjects/industry-4-0/fhe-reference-architectural-model-rami-40-and-the-industrie-40-component, ZVEI; ftp://ftp.heise.de/pub/ix/ix_listings/projektmanagement/vmodell/V-Modell-XT-Gesamt-Englisch-VE3.pdf, The V-Model XT Weit e.V. Munich, 2nd release, 2015; Levitt, J., (2011) Complete Guide to Preventive and Predictive Maintenance, , Industrial Press Inc., 2nd edition; Sage, A.P., Rouse, W.B., (2009) Handbook of Systems Engineering and Management, , Johan Wiley and Sons Inc., 2nd edition; Xu, J.-X., Panda, S.K., Lee, T.H., (2009) Real-time Iterative Learning Control, , Springer Verlag London, 1st edition",,"Latkoski P.Cvetkovski G.Karadzinov L.","Faculty of Computer Science and Engineering;Faculty of Electrical Engineering and IT;Neotel;Netcetera;Saints Cyril and Methodius University","Institute of Electrical and Electronics Engineers Inc.","17th IEEE International Conference on Smart Technologies, EUROCON 2017","6 July 2017 through 8 July 2017",,130057,,9781509038435,,,"English","IEEE Int. Conf. Smart Technol., EUROCON - Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85029369403