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,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
"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. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of EPSRC, Infravation SeeBridge, or Trimble.","FHWA - Federal Highway Administration, Estimated 2012 costs to replace or rehabilitate structurally deficient bridges (2012), https://www.fhwa.dot.gov/bridge/nbi/sd2012.cfm, Available at (Accessed 2 May 2019); ASCE, 2013 Report Card for America's Infrastructure, Bridges (2013), http://2013.infrastructurereportcard.org/bridges/, Available at (Accessed 2 May 2019); ASCE, 2017 report card for America's infrastructure, bridges (2017), https://www.infrastructurereportcard.org/wp-content/uploads/2017/01/Bridges-Final.pdf, Available at (Accessed 2 May 2019); Network Rail, Network rail bridge list (2015), https://www.whatdotheyknow.com/request/list_of_bridges_on_network_rail, Available at (Accessed 2 May 2019); AASHTOWare, AASHTOWare (2018), https://www.aashtoware.org/wp-content/uploads/2018/03/Bridge-Rating-Product-Brochure-FY-2019-11022018.pdf, Available at (Accessed 2 May 2019); Flaig, K.D., Lark, R.J., The development of UK bridge management systems (2000) Proceedings of the Institution of Civil Engineers - Transport, 141 (2), pp. 99-106; (2010), http://www.bridgeforum.org/bof/meetings/bof33/BCI_Study_Report_Final.pdf, Vassou, V. 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Level of accuracy (LOA) specification version 2.0. Available at, accessed 2 May, 2019. U.S. Institute of Building Documentation; Sacks, R., Kedar, A., Borrmann, A., Ma, L., Brilakis, I., Hüthwohl, P., Muhic, S., SeeBridge as next generation bridge inspection: overview, information delivery manual and model view definition (2018) Autom. Constr., 90, pp. 134-145","Lu, R.; School of Architecture, United Kingdom; email: r.lu@lboro.ac.uk",,,"Elsevier B.V.",,,,,09265805,,AUCOE,,"English","Autom Constr",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85065641838
"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
"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
"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. 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"" "" 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. 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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
"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? (2002) Journal of Health Economics, 21 (5), pp. 827-843. , 2-s2.0-0036045177; 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. , 102726; 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, p. 3796; Son, H., Bosché, F., Kim, C., As-built data acquisition and its use in production monitoring and automated layout of civil infrastructure: A survey (2015) Advanced Engineering Informatics, 29 (2), pp. 172-183. , 2-s2.0-84937521132; Pǎtrǎucean, V., Armeni, I., Nahangi, M., Yeung, J., Brilakis, I., Haas, C., State of research in automatic as-built modelling (2015) Advanced Engineering Informatics, 29, pp. 162-171; Lu, Q., Lee, S., Image-based technologies for constructing as-is building information models for existing buildings (2017) Journal of Computing in Civil Engineering, 31. , 04017005 2-s2.0-85018789155; Kim, Q., Kim, M.-K., Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018 (2019) Advanced Engineering Informatics, 39, pp. 306-319. , 2-s2.0-85061601993; Kim, M.-K., Wang, Q., Li, H., Non-contact sensing based geometric quality assessment of buildings and civil structures: A review (2019) Automation in Construction, 100, pp. 163-179. , 2-s2.0-85059964352; Spencer, B.F., Hoskere, V., Narazaki, Y., Advances in computer vision-based civil infrastructure inspection and monitoring (2019) Engineering, 5 (2), pp. 199-222. , 2-s2.0-85063608159; Leite, T., Leite, F., Automated digital modeling of existing buildings: A review of visual object recognition methods (2020) Automation in Construction, 113. , 103131; 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. , 2-s2.0-77956619234; Liu, Z., Liu, S., A review of 3D reconstruction techniques in civil engineering and their applications (2018) Advanced Engineering Informatics, 37, pp. 163-174. , 2-s2.0-85047241482; Adler, J., Adler, D., Emergent trends and passing fads in project management research: A scientometric analysis of changes in the field (2015) International Journal of Project Management, 33 (1), pp. 236-248. , 2-s2.0-84912150660; Chaomei, C., CiteSpace II: Detecting and Visualizing Emerging Trends and Transient Patterns in Scientific Literature (2005) Journal of the American Society for Information Science and Technology, 57 (3), pp. 359-377. , 2-s2.0-33644531603; Cobo, M.J., López-Herrera, A.G., Herrera-Viedma, E., Herrera, F., Science Mapping Software Tools: Review, Analysis, and Cooperative Study among Tools (2011) Journal of the American Society for Information Science and Technology, 62 (7), pp. 1382-1402. , 2-s2.0-79959372931; Chen, C., Ibekwe-Sanjuan, F., Hou, J., The Structure and Dynamics of Cocitation Clusters: A Multiple-Perspective Cocitation Analysis (2010) Journal of the American Society for Information Science and Technology, 61 (7), pp. 1386-1409. , 2-s2.0-77954068456; Software Survey: VOSviewer, A Computer Program for Bibliometric Mapping, , SpringerLink; Perianes-Rodriguez, A., Waltman, L., Van Eck, N.J., Constructing bibliometric networks: A comparison between full and fractional counting (2016) Journal of Informetrics, 10 (4), pp. 1178-1195. , 2-s2.0-84993967401; Oraee, M., Hosseini, M.R., Papadonikolaki, E., Palliyaguru, R., Arashpour, M., Collaboration in BIM-based construction networks: A bibliometric-qualitative literature review (2017) International Journal of Project Management, 35 (7), pp. 1288-1301. , 2-s2.0-85024115642; Shabani, A., Hosamo, H., Plevris, V., Kioumarsi, M., A Preliminary Structural Survey of Heritage Timber Log Houses in Tonsberg, Norway, , Proceedings of the12th International Conference On Structural Analysis Of Historical Constructions (SAHC) November 2021 Barcelona; Han, D., Rolfsen, C.N., Engeland, B.D., Hosamo, H., Hjelmbrekke, H., Hu, K., Guo, T., Ying, C., Research on BIM and virtual pre-assembly technology in construction management (2021) ECPPM 2021 - EWork and EBusiness in Architecture, Engineering and Construction, p. 6. , Boca Raton, Florida CRC Press; Han, D., Rolfsen, C.N., Bui, N., Hosamo, H., Dong, Y., Zhou, Y., Guo, T., Ying, C., Automatic detection method for verticality of bridge pier based on BIM and point cloud (2021) ECPPM 2021 - EWork and EBusiness in Architecture, Engineering and Construction, p. 6. , Boca Raton, Florida CRC Press; Rolfsen, C.N., Lassen, A.K., Han, D., Hosamo, H., Ying, C., The use of the BIM-model and scanning in quality assurance of bridge constructions (2021) ECPPM 2021 - EWork and EBusiness in Architecture, Engineering and Construction, p. 4. , Boca Raton, Florida CRC Press; Han, D., Rolfsen, C.N., Erduran, E., Hempel, E.E., Hosamo, H., Guo, J., Chen, F., Ying, C., Application of phase three dimensional laser scanner in high altitude large volume irregular structure (2021) ECPPM 2021 - EWork and EBusiness in Architecture, Engineering and Construction, p. 6. , Boca Raton, Florida CRC Press; Hosamo, M., Singh, S.P.P., Mohan, A., Analytical modeling of RRM-ATM switch for linear increment and exponential decay of source data rate (2017) Journal of Computer Networks, 4 (1), pp. 56-64; 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Proceedings of the International Symposium on Automation and Robotics in Construction, 29; Laefer, D.F., Truong-Hong, L., Carr, H., Singh, M., Crack detection limits in unit based masonry with terrestrial laser scanning (2014) NDT & e International, 62, pp. 66-76. , 2-s2.0-84890779641; Cabaleiro, M., Lindenbergh, R., Gard, W.F., Arias, P., Van De Kuilen, J.W.G., Algorithm for automatic detection and analysis of cracks in timber beams from LiDAR data (2017) Construction and Building Materials, 130, pp. 41-53. , 2-s2.0-84997079941; Barazzetti, L., Parametric as-built model generation of complex shapes from point clouds (2016) Advanced Engineering Informatics, 30 (3), pp. 298-311. , 2-s2.0-84966430627; Cheng, L., Chen, S., Liu, X., Xu, H., Wu, Y., Li, M., Chen, Y., Registration of laser scanning point clouds: A review (2018) Sensors, 18, p. 1641. , 2-s2.0-85047327614; Gómez, G.B., Jaime, E.Z., Raúl, F., Automated Registration of 3D Scans Using Geometric Features and Normalized Color Data (2013) Computer-Aided Civil and Infrastructure Engineering, 28, pp. 98-111; Wu, Q., Xu, K., Wang, J., Constructing 3D CSG Models from 3D Raw Point Clouds (2018) Computer Graphics Forum, 37, pp. 221-232; 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. , 2-s2.0-84873283130; Son, H., Kim, C., Kim, C., 3D reconstruction of as-built industrial instrumentation models from laser-scan data and a 3D CAD database based on prior knowledge (2015) Automation in Construction, 49, pp. 193-200. , 2-s2.0-85027921229; Shugen, W., Qiuyuan, G., Mingwei, S., Simple Building Reconstruction from Lidar Data and Aerial Imagery, , Proceedings of the 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering June 2012 Nanjing, China; Laefer, D.F., Harnessing remote sensing for civil engineering: Then, now, and tomorrow (2020) Lecture Notes in Civil Engineering, Lecture Notes in Civil Engineering, 33; Yan, Y., Guldur, B., Hajjar, J.F., (2017) Automated Structural Modelling of Bridges from Laser Scanning, , Colorado, USA Structures Congress","Hosamo, H.H.; University of Agder, Jon Lilletuns vei 9, Norway; email: haidar.hosamo@uia.no",,,"Hindawi Limited",,,,,16878086,,,,"English","Adv. Civ. Eng.",Review,"Final","All Open Access, Gold",Scopus,2-s2.0-85135059036
"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
"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
"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
"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