基于LSTM-SSA-BDLM的桥梁结构变形性能动态预测与预警

Dynamic Prediction and Early Warning of Bridge Structural Deformation Performance Based on LSTM-SSA-BDLM

摘要:

掌握桥梁结构预期行为可以提早识别潜在的结构问题或失效模式。提出了一种新型的基于长短期记忆网络（LSTM）的桥梁结构性能动态预测框架。研究采用Block Maxima（BM）方法从每小时监测数据中提取挠度极值，作为评估桥梁安全性能的关键指标。该方法通过对具有周期性的挠度极值序列进行滑动窗口LSTM预测，结合奇异谱分析（SSA）和考虑误差更新的贝叶斯动态线性模型（BDLM），有效地提取了由环境因素引起的长期趋势和周期性挠度变化。这一过程有效降低了噪声的影响，同时保留了车辆荷载效应的关键信息。在实际工程案例中的三组监测数据应用表明，该方法在预测精度上相较于滑动窗口LSTM和BDLM方法有显著提升。此外，还提出了一种基于极值理论的动态预警阈值设定方法，有效避免了静态预警指标的局限性，并利用预测结果的置信区间实现了提前预警。

关键词:结构性能;结构预期行为;长短期记忆网络;挠度;预警

Abstract:

Understanding the anticipated behavior of bridge structures is crucial for early identification of potential structural issues or failure modes. This paper proposes a novel predictive framework for bridge structural performance based on Long Short-Term Memory Networks （LSTM）. The block maxima （BM） method is employed to extract hourly deflection extremes from monitoring data， which serve as key indicators for assessing bridge safety. The method involves sliding window LSTM predictions for periodic deflection extreme sequences， integrating Singular Spectrum Analysis （SSA） and error-updating Bayesian Dynamic Linear Models （BDLM） to effectively extract long-term trends and cyclic deflection changes caused by environmental factors. This process effectively reduces the impact of noise while preserving the critical information of vehicle load effects. Applications in three real engineering cases demonstrate that proposed method significantly improves prediction accuracy compared to sliding window LSTM and BDLM approaches. Furthermore， the paper proposes a dynamic warning threshold setting method based on extreme value theory， which effectively avoids the limitations of static warning indicators and utilizes the confidence intervals of predictions for proactive warning.

Key words:structural performance;structural anticipated behavior;long short-term memory;deflection;early warning