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Unsupervised transfer learning for structural health monitoring of urban pedestrian bridges

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Abstract

Bridge authorities have been reticent to integrate structural health monitoring into their bridge management systems, as they do not have the financial and technical resources to collect long-term monitoring data from every bridge. As bridge authorities normally own huge amount of similar bridges, like the pedestrian ones, the ability to transfer knowledge from one or a small group of well-known bridges to help make more effective decisions in new bridges and environments has gained relevance. In that sense, transfer learning, a subfield of machine learning, offers a novel solution to periodically evaluate the structural condition of all pedestrian bridges using long-term monitoring data from one or more pedestrian bridges. In this paper, the applicability of unsupervised transfer learning is firstly shown on data from numerical models and then on data from two similar pedestrian prestressed concrete bridges. Two domain adaptation techniques are used for transfer learning, where a classifier has access to unlabeled training data (source domain) from a reference bridge (or a small set of reference bridges) and unlabeled monitoring test data (target domain) from another bridge, assuming that both domains are from similar but statistically different distributions. This type of mapping is expected to improve the classification accuracy for the target domain compared to a procedure that does not implement domain adaptation, as a result of reducing distributions mismatch between source and target domains.

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Data are available upon reasonable request.

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Acknowledgements

The authors acknowledge Elisabete Portalegre from the Câmara Municipal de Lisboa for providing the information about the number of pedestrian bridges in the municipality of Lisbon.

Funding

This work is part of the research activity carried out at Civil Engineering Research and Innovation for Sustainability (CERIS) and has been funded by Fundação para a Ciência e a Tecnologia (FCT) in the framework of project UIDB/04625/2020.

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Author notes

  1. Ionut Moldovan, Eloi Figueiredo and Bernardino Chiaia have contributed equally to this work.

Authors and Affiliations

  1. Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca Degli Abruzzi 24, Torino, 10128, Italy

    Giulia Marasco & Bernardino Chiaia

  2. Faculty of Engineering, Lusófona University, Campo Grande 376, Lisboa, 1749-024, Portugal

    Ionut Moldovan & Eloi Figueiredo

  3. CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisboa, 1049-001, Portugal

    Ionut Moldovan & Eloi Figueiredo

Authors
  1. Giulia Marasco
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  2. Ionut Moldovan
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  3. Eloi Figueiredo
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  4. Bernardino Chiaia
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Correspondence to Giulia Marasco.

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Marasco, G., Moldovan, I., Figueiredo, E. et al. Unsupervised transfer learning for structural health monitoring of urban pedestrian bridges. J Civil Struct Health Monit (2024). https://doi.org/10.1007/s13349-024-00786-w

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