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Structural Health Monitoring Using Machine Learning Techniques and Domain Knowledge Based Features

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Human and Machine Learning

Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

Structural Health Monitoring (SHM) is a condition-based maintenance technology using sensing systems. In SHM, the use of domain knowledge is essential: it motivates the use of machine learning approaches; it can be used to extract damage sensitive features and interpret the results by machine learning. This work focuses on two SHM problems: damage identification and substructure clustering. Our solutions to address them are based on machine learning techniques and robust feature extraction using domain knowledge. In the first problem, damage sensitive features were extracted using a frequency domain decomposition, followed by a robust one-class support vector machine for damage detection. In the second problem, a novel clustering technique and spectral moment feature were utilised for substructure grouping and anomaly detection. These methods were evaluated using data from lab-based structures and data collected from the Sydney Harbour Bridge. We obtained high damage detection accuracies and were able to assess damage severity. Furthermore, the clustering technique was able to group substructures of similar behaviour and detect spatial anomalies.

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Acknowledgements

The authors wish to thank the Roads and Maritime Services (RMS) in New South Wales, Australia for provision of the support and testing facilities for this research work. NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program. CSIRO’s Digital Productivity business unit and NICTA have joined forces to create digital powerhouse Data61.

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Authors and Affiliations

  1. DATA61, CSIRO, 13 Garden Street, Eveleigh, NSW, 2015, Australia

    Nguyen Lu Dang Khoa, Thierry Rakotoarivelo & Yang Wang

  2. School of Civil and Environmental Engineering, University of New South Wales, Kensington, NSW, 2052, Australia

    Mehrisadat Makki Alamdari

  3. School of IT, The University of Sydney, Sydney, NSW, 2006, Australia

    Ali Anaissi

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  1. Nguyen Lu Dang Khoa
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  2. Mehrisadat Makki Alamdari
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  3. Thierry Rakotoarivelo
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  5. Yang Wang
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Correspondence to Nguyen Lu Dang Khoa .

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  1. DATA61, CSIRO, Eveleigh, New South Wales, Australia

    Jianlong Zhou

  2. DATA61, CSIRO, Eveleigh, New South Wales, Australia

    Fang Chen

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Khoa, N.L.D., Makki Alamdari, M., Rakotoarivelo, T., Anaissi, A., Wang, Y. (2018). Structural Health Monitoring Using Machine Learning Techniques and Domain Knowledge Based Features. In: Zhou, J., Chen, F. (eds) Human and Machine Learning. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-90403-0_20

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  • DOI: https://doi.org/10.1007/978-3-319-90403-0_20

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