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<p>Real-time monitoring of bridge deterioration remains a major challenge in structural health monitoring (SHM), as traditional inspection methods lack sensitivity to early-stage damage and cannot provide real-time evaluation. This study aims to develop a practical approach for damage stage assessment of prestressed hollow-slab bridges using acoustic emission (AE) parameters and a lightweight machine learning model. First, scaled model tests were conducted to collect AE signals under different loading stages, and parameters such as amplitude, energy, duration, ring-down count, and impact velocity were extracted. Second, correlation analyses (Pearson, Spearman, Kendall) were performed to identify the most representative parameters for structural degradation. Third, a K-nearest neighbors (KNN) model was then constructed to classify bridge damage stages in real-time. Finally, comparative analysis with decision tree, support vector machine (SVM), one-dimensional convolutional neural network (1DCNN), and long short-term memory (LSTM) models was conducted. Impact Velocity was determined to have the strongest correlation with damage stages and the KNN model achieved competitive accuracy (0.9103) with superior computational efficiency (1.30 × 10⁻⁶ s per sample), offered the best computational efficiency, making it highly suitable for real-time applications. This research establishes an efficient, data-driven framework for real-time bridge health monitoring, demonstrating the practical viability of combining AE technology with lightweight machine learning for structural damage assessment. The methodology shows particular promise for implementation in real-time monitoring systems for prestressed concrete structures.</p>

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KNN-Based Damage Stage Evaluation for Bridge Using Acoustic Emission Technique

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Qiang Wang,
  • Nan Feng,
  • Shengli Li,
  • Zhuan Zhang,
  • Panjie Li,
  • Xiangni Che,
  • Cuiping Shi

摘要

Real-time monitoring of bridge deterioration remains a major challenge in structural health monitoring (SHM), as traditional inspection methods lack sensitivity to early-stage damage and cannot provide real-time evaluation. This study aims to develop a practical approach for damage stage assessment of prestressed hollow-slab bridges using acoustic emission (AE) parameters and a lightweight machine learning model. First, scaled model tests were conducted to collect AE signals under different loading stages, and parameters such as amplitude, energy, duration, ring-down count, and impact velocity were extracted. Second, correlation analyses (Pearson, Spearman, Kendall) were performed to identify the most representative parameters for structural degradation. Third, a K-nearest neighbors (KNN) model was then constructed to classify bridge damage stages in real-time. Finally, comparative analysis with decision tree, support vector machine (SVM), one-dimensional convolutional neural network (1DCNN), and long short-term memory (LSTM) models was conducted. Impact Velocity was determined to have the strongest correlation with damage stages and the KNN model achieved competitive accuracy (0.9103) with superior computational efficiency (1.30 × 10⁻⁶ s per sample), offered the best computational efficiency, making it highly suitable for real-time applications. This research establishes an efficient, data-driven framework for real-time bridge health monitoring, demonstrating the practical viability of combining AE technology with lightweight machine learning for structural damage assessment. The methodology shows particular promise for implementation in real-time monitoring systems for prestressed concrete structures.