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Predictive Monitoring of Large-Scale Engineering Assets Using Machine Learning Techniques and Reduced-Order Modeling

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Structural Health Monitoring Based on Data Science Techniques

Part of the book series: Structural Integrity ((STIN,volume 21))

Abstract

Structural health monitoring techniques aim at providing an automated solution to the threat of unsurveilled aging of structures that can have tremendous consequences in terms of fatalities, environmental pollution, and economic loss. To assess the state of damage of a complex structure, this paper proposes to fully characterize its behavior under multiple environmental and operational scenarios and compare new sensor measurements with the baseline behavior. However, the repeated simulations of a nonlinear, time-dependent structural model with high-dimensional input parameters represent a severe computational bottleneck for large-scale engineering assets. This chapter presents how to use efficient reduced-order modeling techniques to mitigate the computational effort of many-query simulations without jeopardizing the accuracy. To compare new sensor measurements with the natural behavior of synthetic solutions, the proposed methodology uses hierarchical semi-supervised learning algorithms on a small amount of extracted damage-sensitive features, thus allowing one to assess the state of damage in real time. Using the inexpensive simulations, one can also optimally place sensors to maximize the observability of discriminant features. The all-round methodology is validated on a numerical example.

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Notes

  1. 1.

    Here f denotes a generic regression function and should not be confused with the linear functional \(f(\cdot ;{\boldsymbol{\mu }})\) in Sect. 2.

  2. 2.

    Here r denotes a (scaled) radius and should not be confused with the reduced dimensionality in Sect. 3.

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Author information

Authors and Affiliations

  1. Computational Mathematics and Simulation Science (MCSS), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Caterina Bigoni & Jan S. Hesthaven

  2. Department of Applied Mathematics, University of Twente, Enschede, Netherlands

    Mengwu Guo

Authors
  1. Caterina Bigoni
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  2. Mengwu Guo
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  3. Jan S. Hesthaven
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Corresponding author

Correspondence to Caterina Bigoni .

Editor information

Editors and Affiliations

  1. Department of Applied and Computational Mechanics, Federal University of Juiz de Fora, Juiz de Fora, Brazil

    Alexandre Cury

  2. Department of Civil Engineering, School of Engineering, Polytechnic Institute of Porto, Porto, Portugal

    Diogo Ribeiro

  3. Department of Civil and Environmental Engineering, University of Perugia, Perugia, Italy

    Filippo Ubertini

  4. Structural Engineering, University of California San Diego, La Jolla, CA, USA

    Michael D. Todd

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Bigoni, C., Guo, M., Hesthaven, J.S. (2022). Predictive Monitoring of Large-Scale Engineering Assets Using Machine Learning Techniques and Reduced-Order Modeling. In: Cury, A., Ribeiro, D., Ubertini, F., Todd, M.D. (eds) Structural Health Monitoring Based on Data Science Techniques. Structural Integrity, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-030-81716-9_9

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  • DOI: https://doi.org/10.1007/978-3-030-81716-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-81715-2

  • Online ISBN: 978-3-030-81716-9

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