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| 基于CNN-LSTM-Attention的桥梁结构异常预测模型研究 |
| Research on a CNN-LSTM-Attention Based Bridge Structure Anomaly Prediction Model |
| 投稿时间：2025-09-22 修订日期：2025-11-17 |
| DOI： |
| 中文关键词: 结构健康监测 异常预测 CNN-LSTM-Attention 斜拉桥 斜拉索振动信号 |
| 英文关键词: Structural Health Monitoring Anomaly Prediction CNN-LSTM-Attention Cable-Stayed Bridge Stay-Cable Vibration Signals |
| 基金项目: |
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| 摘要点击次数: 215 |
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| 中文摘要: |
| 结构健康监测是确保长大桥梁运营安全的关键技术。针对斜拉桥结构振动信号具有非平稳性、高噪声及强时序依赖特征，传统异常检测方法难以实现早期精准识别的问题，提出一种融合卷积神经网络（CNN）、长短期记忆网络（LSTM）与自注意力机制（Attention）的桥梁结构异常预测模型，即CNN-LSTM-Attention。模型通过CNN提取局部空间特征，LSTM捕捉长时序依赖关系，Attention机制自适应强化关键时间步权重分配，实现对结构动态响应中潜在异常的高效识别。以芜湖长江公路二桥斜拉索加速度监测数据为案例，结果表明该模型在预测精度与鲁棒性方面均优于传统时间序列预测方法，为桥梁健康监测系统的智能预警与运维决策提供了有力的技术支持。 |
| 英文摘要: |
| Structural Health Monitoring (SHM) is a key technology for ensuring the operational safety of long-span bridges. Aiming at the problems that the structural vibration signals of cable-stayed bridges are characterized by non-stationarity, high noise, and strong temporal dependence, making it difficult for traditional anomaly detection methods to achieve early and accurate identification, this study proposes a bridge structure anomaly prediction model integrating Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Self-Attention mechanism, namely CNN-LSTM-Attention. The model extracts local spatial features through CNN, captures long-term temporal dependencies via LSTM, and uses the Attention mechanism to adaptively enhance the weight allocation of key time steps, thereby realizing efficient identification of potential abnormal patterns in the structural dynamic response. Taking the acceleration monitoring data of the stay cables of the Second Wuhu Yangtze River Highway Bridge as a case study, the results show that the proposed model outperforms traditional time-series prediction methods in terms of prediction accuracy and robustness. It provides strong technical support for intelligent early warning and operation-maintenance decision-making of bridge health monitoring systems. |
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