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Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review

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Abstract

Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have become of great interest in recent years owing to their superior ability to detect damage and deficiencies in civil engineering structures. With the advent of the Internet of Things, big data and the colossal and complex backlog of aging civil infrastructure assets, such applications will increase very rapidly. ML can efficiently perform several analyses of clustering, regression and classification of damage in diverse structures, including bridges, buildings, dams, tunnels, wind turbines, etc. In this systematic review, the diverse ML algorithms used in this domain have been classified into two major subfields: vibration-based SHM and image-based SHM. The efficacy of deploying ML algorithms in SHM has been discussed and detailed critical analysis of ML applications in SHM has been provided. Accordingly, practical recommendations have been made and current knowledge gaps and future research needs have been outlined.

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Acknowledgements

The work presented in this paper was supported financially by the Canadian Mitacs Graduate Fellowship award (Intern ID #GLF580) and the graduate scholarship offered by the laboratory of Professor Moncef Nehdi, Department of Civil and Environmental Engineering, Western University, London ON, Canada.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Western University, London, ON, N6A 5B9, Canada

    Majdi Flah, Itzel Nunez, Wassim Ben Chaabene & Moncef L. Nehdi

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  1. Majdi Flah
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  2. Itzel Nunez
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  4. Moncef L. Nehdi
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Correspondence to Moncef L. Nehdi.

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Flah, M., Nunez, I., Ben Chaabene, W. et al. Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review. Arch Computat Methods Eng 28, 2621–2643 (2021). https://doi.org/10.1007/s11831-020-09471-9

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