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Recent progress and future outlook of digital twins in structural health monitoring of civil infrastructure

Author(s):
ORCID
Medium: journal article
Language(s): English
Published in: Smart Materials and Structures, , n. 3, v. 33
Page(s): 033001
DOI: 10.1088/1361-665x/ad2bd7
Abstract:

Digital twins (DTs) have witnessed a paramount increase in applications in multidisciplinary engineering systems. With advancements in structural health monitoring (SHM) methods and implementations, DT-based maintenance and operation stages have been implemented significantly during the life cycle of civil infrastructure. Recent literature has started laying the building blocks for incorporating the concept of DTs with SHM of large-scale civil infrastructure. This paper undertakes a systematic literature review of studies on DT-related applications for SHM of civil structures. It classifies the articles based on thematic case studies: transportation infrastructure (i.e. bridges, tunnels, roads, and pavements), buildings, off-shore marine infrastructure and wind turbines, and other civil engineering systems. The proposed review is further uniquely sub-classified using diverse modeling approaches such as building information modeling, finite element modeling, 3D representation, and surrogate and hybrid modeling used in DT implementations. This paper is solely focused on applications relating DTs to SHM practices for various civil engineering infrastructures, hence highlighting ist novelty over previous reviews. Gaps and limitations emerging from the systematic review are presented, followed by articulating future research directions and key conclusions.

Copyright: © 2024 Micheal Sakr, Ayan Sadhu
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10769273
  • Published on:
    29/04/2024
  • Last updated on:
    29/04/2024
 
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