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Bridge health monitoring in environmental variability by new clustering and threshold estimation methods

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Abstract

Environmental variability is a major challenging issue in bridge health monitoring because bridges are more prone to such variability than other civil structures. To deal with this challenge, this article proposes a new machine-learning method for early damage detection under environmental variability by means of the k-medoids clustering, a new damage indicator, and an innovative approach for selecting a proper cluster number. Estimation of a reliable alarming threshold is another important challenge for early damage detection via most of the machine-learning methods. On this basis, a novel probabilistic approach using the theory of extreme value and a goodness-of-fit measure is proposed to estimate an alarming threshold. The major contributions of this article include proposing a new damage indicator suitable for decision making by clustering-based algorithms, an innovative cluster selection algorithm for dealing with the problem of environmental variability and increasing damage detectability, and a novel probabilistic method for threshold estimation. Modal-based features of the well-known Z24 Bridge are considered to verify the accuracy and effectiveness of the proposed approaches along with several comparative studies. Results show that the methods presented here are highly able to detect early damage even under strong environmental variations and estimate a reliable threshold.

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Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, P. Box: 9177948974, Mashhad, Iran

    Hassan Sarmadi & Alireza Entezami

  2. Head of Research and Development, Ideh Pardazan Etebar Sazeh Fanavar Pooya (IPESFP) Knowledge-Based Company, 29th Reza St., Reza Blvd, P. Box: 9176768544, Mashhad, Razavi Khorasan, Iran

    Hassan Sarmadi

  3. Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milan, Italy

    Alireza Entezami, Masoud Salar & Carlo De Michele

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  1. Hassan Sarmadi
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  2. Alireza Entezami
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Correspondence to Hassan Sarmadi.

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Sarmadi, H., Entezami, A., Salar, M. et al. Bridge health monitoring in environmental variability by new clustering and threshold estimation methods. J Civil Struct Health Monit 11, 629–644 (2021). https://doi.org/10.1007/s13349-021-00472-1

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  • DOI: https://doi.org/10.1007/s13349-021-00472-1

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