Published January 18, 2023 | Version 1.0
Dataset Open

Bibliographic Data from the Digital Twin Anomaly Detection Decision-Making for Bridge Management Systematic Review

  • 1. Structural Engineering Research Group (SERG), Department of Built Environment (DBE), Faculty of Technology, Art, and Design (TKD), Oslo Metropolitan University (OsloMet)
  • 2. Department of Civil and Architectural Engineering, Qatar University, Doha P.O. Box 2713, Qatar
  • 3. Materials, Mechanics, Management & Design Department, Delft University of Technology, 2628 CN Delft, The Netherlands

Description

This database contains all the bibliographic information about the 8673 records found after applying the Search Strategy used for the Digital Twin Anomaly Detection Decision-Making for Bridge Management Systematic Review. Such strategy consisted on using seven initial keywords and similar terms of interest (namely: bridge and bridges, etc.): 

  • Bridge.
  • Digital twin.
  • Bridge information modelling.
  • Finite elements.
  • Bridge health monitoring.
  • Anomaly detection algorithm.
  • Cultural heritage.

Six initial queries were done combining the first keyword with the rest of them:

  • bridge* AND "digital twin*"
  • bridge* AND (BrIM OR "bridge information model*")
  • bridge* AND (FEM OR FEA OR "finite element method*" OR "finite element analy*")
  • bridge* AND ("bridge health monitoring" OR "structural health monitoring")
  • bridge* AND (ADA OR "anomaly detection algorithm*")
  • bridge* AND ("cultural heritage" OR "monument* bridge*" OR "old bridge*" OR "ancient bridge*" OR "historic* bridge*")

As a first screening step, the combination of these 6 initial searches was done to obtain relevant works containing at least three of the main keywords of interest:

  • #1 AND #2
  • #1 AND #3
  • #1 AND #4
  • #1 AND #5
  • #1 AND #6
  • #2 AND #3
  • #2 AND #4
  • #2 AND #5
  • #2 AND #6
  • #3 AND #4
  • #3 AND #5
  • #3 AND #6
  • #4 AND #5
  • #4 AND #6
  • #5 AND #6

All records found in Scopus where downloaded both in .ris and .csv format and are included in this database. The search was conducted on 10/12/2022.

Note: Searches 10, 14, 17 and 21 did not return any records.

Notes

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101066739.

Files

001_DTADD.csv

Files (159.8 MB)

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Additional details

Funding

DTADD – Digital Twin Anomaly Detection Decision-Making for Bridge Management 101066739
European Commission