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  • Application of machine learning algorithms in structural health monitoring research

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    Hamishebahar483638-Accepted.pdf (524.4Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Hamishebahar, Y
    Li, HZ
    Guan, H
    Griffith University Author(s)
    Guan, Hong
    Li, Huaizhong
    Hamishebahar, Younes
    Year published
    2021
    Metadata
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    Abstract
    Engineering structures, including civil infrastructures, have always been susceptible to various kind of damage during their service life. The goal of structural health monitoring is to provide sufficient when the structure condition deteriorates. It has the obvious benefits of preventing disastrous structural collapses and reducing maintenance costs. By and large, structural health monitoring approaches are divided into two classes, model-based, and data-driven approaches. The main challenge in data-driven approaches lies in a large amount of data to be dealt with. As machine learning algorithms are often used to recognize ...
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    Engineering structures, including civil infrastructures, have always been susceptible to various kind of damage during their service life. The goal of structural health monitoring is to provide sufficient when the structure condition deteriorates. It has the obvious benefits of preventing disastrous structural collapses and reducing maintenance costs. By and large, structural health monitoring approaches are divided into two classes, model-based, and data-driven approaches. The main challenge in data-driven approaches lies in a large amount of data to be dealt with. As machine learning algorithms are often used to recognize the inherent pattern in data, their applications in data-driven approaches have been gained an increasing attention in recent years. Utilizing machine learning algorithms turns the decision-making step of structural health monitoring into an automated process with minimal human intervention. This paper presents a summary of a literature review concerning different data-driven structural health monitoring approaches combined with machine learning algorithms developed in the last several years. Specifically, this report will review existing applications of machine learning algorithms for the purposes of dimensionality reduction and developing a statistical model for structural health monitoring. The primary aim is to categorise the existing studies in the aforementioned areas in terms of the type of machine learning algorithms. This paper also attempts to identify research gaps to facilitate the formulation of a future study in the aforementioned area.
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    Conference Title
    Lecture Notes in Civil Engineering
    Volume
    101
    DOI
    https://doi.org/10.1007/978-981-15-8079-6_21
    Copyright Statement
    © Springer Nature Singapore Pte Ltd. 2021. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
    Subject
    Civil engineering
    Mechanical engineering
    Publication URI
    http://hdl.handle.net/10072/404176
    Collection
    • Conference outputs

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