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<p>This paper introduces an efficient machine learning-based structural health monitoring method for railway truss bridges, addressing the time-consuming and error-prone aspects of traditional approaches. By utilizing measured vibration responses under train load, the technique employs wavelets, Fourier transforms, and spectrograms to extract damage-induced changes in signals for training various machine learning models, including decision trees and residual neural networks. Given the slow and impractical data collection from real-world bridges, the paper proposes generating data from a numerical model onto which a moving train load is applied. The acceleration time history responses from nodes are recorded for various damage cases, forming the dataset. The trained models demonstrate accurate classification of damaged members. While these models provide a valuable tool for enhancing efficiency and reducing human errors in structural health monitoring, human interpretation remains necessary for comprehensive assessment.</p>

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Structural health monitoring of railway truss bridge under moving train load using decision tree models and residual neural networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月25日

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  • Sharan Kumar Sunchikala,
  • S. C. Mohan,
  • Sai Dheeraj Gopala,
  • P. Swetha

摘要

This paper introduces an efficient machine learning-based structural health monitoring method for railway truss bridges, addressing the time-consuming and error-prone aspects of traditional approaches. By utilizing measured vibration responses under train load, the technique employs wavelets, Fourier transforms, and spectrograms to extract damage-induced changes in signals for training various machine learning models, including decision trees and residual neural networks. Given the slow and impractical data collection from real-world bridges, the paper proposes generating data from a numerical model onto which a moving train load is applied. The acceleration time history responses from nodes are recorded for various damage cases, forming the dataset. The trained models demonstrate accurate classification of damaged members. While these models provide a valuable tool for enhancing efficiency and reducing human errors in structural health monitoring, human interpretation remains necessary for comprehensive assessment.