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Explainable Artificial Intelligence to Advance Structural Health Monitoring

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Structural Health Monitoring Based on Data Science Techniques

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

Abstract

In recent years, structural health monitoring (SHM) applications have significantly been enhanced, driven by advancements in artificial intelligence (AI) and machine learning (ML), a subcategory of AI. Although ML algorithms allow detecting patterns and features in sensor data that would otherwise remain undetected, the generally opaque inner processes and black-box character of ML algorithms are limiting the application of ML to SHM. Incomprehensible decision-making processes often result in doubts and mistrust in ML algorithms, expressed by engineers and stakeholders. In an attempt to increase trust in ML algorithms, explainable artificial intelligence (XAI) aims to provide explanations of decisions made by black-box ML algorithms. However, there is a lack of XAI approaches that meet all requirements of SHM applications. This chapter provides a review of ML and XAI approaches relevant to SHM and proposes a conceptual XAI framework pertinent to SHM applications. First, ML algorithms relevant to SHM are categorized. Next, XAI approaches, such as transparent models and model-specific explanations, are presented and categorized to identify XAI approaches appropriate for being implemented in SHM applications. Finally, based on the categorization of ML algorithms and the presentation of XAI approaches, the conceptual XAI framework is introduced. It is expected that the proposed conceptual XAI framework will provide a basis for improving ML acceptance and transparency and therefore increase trust in ML algorithms implemented in SHM applications.

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Acknowledgements

The authors gratefully acknowledge the support offered by the German Research Foundation (DFG) under grants SM 281/9-1, SM 281/12-1, SM 281/14-1, SM 281/15-1, and LE 3955/4-1. This research is also partially supported by the Carl Zeiss Foundation. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsors.

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Authors and Affiliations

  1. Chair of Computing in Civil Engineering, Bauhaus University Weimar, Weimar, Germany

    Daniel Luckey

  2. Institute of Digital and Autonomous Construction, Hamburg University of Technology, Hamburg, Germany

    Henrieke Fritz, José Joaquín Peralta Abadía & Kay Smarsly

  3. Chair of Applied Mathematics, Bauhaus University Weimar, Weimar, Germany

    Dmitrii Legatiuk

  4. Institute of Structural Mechanics, Bauhaus University Weimar, Weimar, Germany

    Christian Walther

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  1. Daniel Luckey
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  2. Henrieke Fritz
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  4. José Joaquín Peralta Abadía
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  5. Christian Walther
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  6. Kay Smarsly
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Correspondence to Daniel Luckey .

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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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Luckey, D., Fritz, H., Legatiuk, D., Peralta Abadía, J.J., Walther, C., Smarsly, K. (2022). Explainable Artificial Intelligence to Advance Structural Health Monitoring. 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_16

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  • DOI: https://doi.org/10.1007/978-3-030-81716-9_16

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