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Structural Damage Detection in Civil Engineering with Machine Learning: Current State of the Art

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Sensors and Instrumentation, Aircraft/Aerospace, Energy Harvesting & Dynamic Environments Testing, Volume 7

Abstract

This paper presents a brief overview of vibration-based structural damage detection studies that are based on machine learning (ML) in civil engineering structures. The review includes both parametric and nonparametric applications of ML accompanied with analytical and/or experimental studies. While the ML tools help the system learn from the data fed into, the computer enhances the task with the learned information without any programming on how to process the relevant data. As such, the performance level of ML-based damage identification methodologies depends on the feature extraction and classification steps, especially on the classifier choices for which the characteristic nature of the acceleration signals is recorded in a feasible way. Yet, there are several issues to be discussed about the existing ML procedures for both parametric and nonparametric applications, which are presented in this paper.

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Authors and Affiliations

  1. Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA, USA

    Onur Avci

  2. Department of Building Technology, Linnaeus University, Växjö, Sweden

    Osama Abdeljaber

  3. Department of Electrical Engineering, Qatar University, Doha, Qatar

    Serkan Kiranyaz

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  1. Onur Avci
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  2. Osama Abdeljaber
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  3. Serkan Kiranyaz
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Correspondence to Onur Avci .

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Editors and Affiliations

  1. PCB Piezotronics, Inc, Depew, NY, USA

    Chad Walber

  2. Dayton, OH, USA

    Matthew Stefanski

  3. Minneapolis, MN, USA

    Steve Seidlitz

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Avci, O., Abdeljaber, O., Kiranyaz, S. (2022). Structural Damage Detection in Civil Engineering with Machine Learning: Current State of the Art. In: Walber, C., Stefanski, M., Seidlitz, S. (eds) Sensors and Instrumentation, Aircraft/Aerospace, Energy Harvesting & Dynamic Environments Testing, Volume 7. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-030-75988-9_17

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