Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications

Journal article


Fan , W., Chen, Y., Li, J., Sun, Y., Feng, F., Hassanin, H. and Sareh, P. 2021. Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications. Structures. 33, pp. 3954-3963. https://doi.org/10.1016/j.istruc.2021.06.110
AuthorsFan , W., Chen, Y., Li, J., Sun, Y., Feng, F., Hassanin, H. and Sareh, P.
Abstract

Machine learning is one of the key pillars of industry 4.0 that has enabled rapid technological advancement through establishing complex connections among heterogeneous and highly complex engineering data automatically. Once the machine learning model is trained appropriately, it becomes able to effectively predict and make decisions. The technology is rapidly evolving and has found numerous applications in various branches of engineering due to its preponderance. This study is focused on exploring the recent advances of machine learning and its applications in reinforced concrete bridges. It covers a range of different machine learning techniques exploited in structural design, construction quality management, bridge engineering, and the inspection of reinforced concrete bridges. This review demonstrated that machine learning algorithms have established new research directions in bridge engineering, in particular for applications such as the form-finding of innovative long-span structures, structural reinforcement, and structural optimization.

KeywordsMachine learning; Deep learning; Reinforced concrete bridges; Strength prediction; Structural health monitoring
Year2021
JournalStructures
Journal citation33, pp. 3954-3963
PublisherElsevier
ISSN2352-0124
Digital Object Identifier (DOI)https://doi.org/10.1016/j.istruc.2021.06.110
Official URLhttps://doi.org/10.1016/j.istruc.2021.06.110
Publication dates
Online10 Jul 2021
PrintOct 2021
Publication process dates
Accepted30 Jun 2021
Deposited09 Jul 2021
Accepted author manuscript
License
File Access Level
Open
Output statusPublished
References

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