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BWIM aided damage detection in bridges using machine learning

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Abstract

In this study, a new, model-free damage detection method is proposed and validated on a simple numerical experiment. The proposed algorithm used vibration data (deck accelerations) and bridge weigh-in-motion data (load magnitude and position) to train a two-stage machine learning setup to classify the data into healthy or damaged. The proposed method is composed in its first stage of an artificial neural network and on the second stage of a gaussian process. The proposed method is applicable to railway bridges, since it takes advantage of the fact that vehicles of known axle configuration cross the bridge regularly, that normally only one train is on the bridge at a time and that the lateral positioning of the loads does not change. The novelty of the proposed algorithm is that it makes use of the data on the load’s position, magnitude and speed that can be obtained from a Bridge Weigh-in-Motion system to improve the accuracy of the damage detection algorithm.

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Acknowledgments

The authors are grateful for the funding provided by the Swedish Governmental Agency for Innovation Systems VINNOVA within the project IoT Bridge.

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Authors and Affiliations

  1. KTH Royal Institute of Technology, Brinellvägen 23, 100 44, Stockholm, Sweden

    Ignacio Gonzalez & Raid Karoumi

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  1. Ignacio Gonzalez
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  2. Raid Karoumi
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Correspondence to Ignacio Gonzalez.

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Gonzalez, I., Karoumi, R. BWIM aided damage detection in bridges using machine learning. J Civil Struct Health Monit 5, 715–725 (2015). https://doi.org/10.1007/s13349-015-0137-4

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  • DOI: https://doi.org/10.1007/s13349-015-0137-4

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