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<p>Structures are prone to damage. Identification and localization of damage at its initiation stage are extremely helpful for ensuring safety, economy, and operational benefits. A machine learning (ML) approach is helpful for this purpose, provided that high-quality and sufficient damage signals are available from a variety of locations across the structure. Such signals, generated at damage initiation, are frequently obtained using a non-destructive testing (NDT) technique, such as acoustic emission (AE), which employs the pencil lead break (PLB) method. However, PLB is not possible at inaccessible locations of the structure. Therefore, synthetic experimental signals are required for such locations. Accordingly, the present study aims to generate synthetic experimental signals from numerically simulated AE signals using an artificial neural network (ANN). Here, parameters from numerical signals serve as inputs, and the corresponding parameters of experimental signals are outputs. The most relevant signal parameters are determined using the Pearson correlation coefficient (PCC). The developed model is found to perform very well, achieving an accuracy of around 99%.</p>

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Prediction of damage signals at inaccessible locations using a machine learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月1日

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  • Anoop Sharma,
  • Neetika Saha,
  • Pijush Topdar

摘要

Structures are prone to damage. Identification and localization of damage at its initiation stage are extremely helpful for ensuring safety, economy, and operational benefits. A machine learning (ML) approach is helpful for this purpose, provided that high-quality and sufficient damage signals are available from a variety of locations across the structure. Such signals, generated at damage initiation, are frequently obtained using a non-destructive testing (NDT) technique, such as acoustic emission (AE), which employs the pencil lead break (PLB) method. However, PLB is not possible at inaccessible locations of the structure. Therefore, synthetic experimental signals are required for such locations. Accordingly, the present study aims to generate synthetic experimental signals from numerically simulated AE signals using an artificial neural network (ANN). Here, parameters from numerical signals serve as inputs, and the corresponding parameters of experimental signals are outputs. The most relevant signal parameters are determined using the Pearson correlation coefficient (PCC). The developed model is found to perform very well, achieving an accuracy of around 99%.