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<p>Accurate identification of structural states during the hoisting of high-speed railway (HSR) precast box girders is essential for ensuring construction safety. However, efficient identification of girder status during hoisting remains a major challenge due to the complexity of mechanical responses at lifting points. To explore the feasibility and effectiveness of acoustic emission (AE) monitoring in identifying hoisting states, this study proposes a status recognition method that integrates AE sensing at lifting points with an optimized classification model. Specifically, (1) AE signals are collected during various hoisting scenarios of standard 32.6&#xa0;m HSR box girders to capture characteristic changes in signal features such as amplitude, energy, ring counts, etc. (2) Based on a comprehensive feature set extracted from the AE signals, a light gradient boosting machine (LGBM) classification model optimized via Bayesian algorithm is developed for hoisting state recognition. Experimental tests conducted in a prefabrication yard demonstrate that the proposed method effectively distinguishes different hoisting conditions, particularly capturing potential anomalies caused by lifting asynchrony or stress concentration. The results validate the applicability of AE technology for non-invasive, efficient status identification during girder hoisting, providing a technical foundation for the intelligent monitoring of construction safety.</p>

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Lifting State Identification of High-Speed Railway Prefabricated Box Girders Using Acoustic Emission Monitoring and an Optimized Classification Model

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Ying Yang,
  • Yanqi Wu,
  • Taigang Wang,
  • Shengli Li,
  • Zhuan Zhang,
  • Nan Jiang,
  • Qiang Wang

摘要

Accurate identification of structural states during the hoisting of high-speed railway (HSR) precast box girders is essential for ensuring construction safety. However, efficient identification of girder status during hoisting remains a major challenge due to the complexity of mechanical responses at lifting points. To explore the feasibility and effectiveness of acoustic emission (AE) monitoring in identifying hoisting states, this study proposes a status recognition method that integrates AE sensing at lifting points with an optimized classification model. Specifically, (1) AE signals are collected during various hoisting scenarios of standard 32.6 m HSR box girders to capture characteristic changes in signal features such as amplitude, energy, ring counts, etc. (2) Based on a comprehensive feature set extracted from the AE signals, a light gradient boosting machine (LGBM) classification model optimized via Bayesian algorithm is developed for hoisting state recognition. Experimental tests conducted in a prefabrication yard demonstrate that the proposed method effectively distinguishes different hoisting conditions, particularly capturing potential anomalies caused by lifting asynchrony or stress concentration. The results validate the applicability of AE technology for non-invasive, efficient status identification during girder hoisting, providing a technical foundation for the intelligent monitoring of construction safety.