Correlation model of deflection-temperature-vehicle load monitoring data for bridge structures
JU Hanwen1，DENG Yang1,2，LI Aiqun1,2
1.School of Civil & Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
2.Beijing Energy Conservation & Sustainable Urban and Rural Development Provincial and Ministry Co-construction Collaboration Innovation Center,
Beijing University of Civil Engineering and Architecture, Beijing 100044, China
摘要运营状态下桥梁结构挠度、车载和温度的相关性复杂，高精度的车载、温度与挠度相关性模型对桥梁结构健康监测具有重要意义。为此，提出了基于门控循环单元(gated recurrent unit ,GRU)神经网络的桥梁挠度监测数据建模方法。为解决车辆荷载监测数据在时域内离散分布的问题，提出了基于挠度影响线的车载影响参数计算方法；在此基础上建立了基于GRU神经网络的车载影响参数、环境温度和桥梁挠度相关性模型。以一座悬索桥为例，分别建立了短时段、中长时段的相关性模型，考察了相关性模型对加劲梁挠度的预测能力，并利用相关性模型提出了一种温度和车载挠度分量的分离方法。悬索桥实例研究表明，短时段相关性模型的挠度预测值与实时监测数据基本吻合，而中长时段相关性模型则对一定时间窗口内的挠度极值具有精确的预测能力；采用相关性模型计算得到的温度与车载挠度分量与小波分解结果具有良好的一致性。
Abstract：The correlation between vehicle load, environmental temperature, and deflection of in-service bridge structures is complex. A high-precision correlation model between vehicle load, temperature, and deflection is important to bridge structural health monitoring. This study proposed a modeling method of bridge deflection monitoring data based on gated recurrent unit (GRU) neural network. The calculation method of vehicle load influence parameter was developed based on deflection influence line to solve the problem of time-domain discrete distribution of vehicle load monitoring data. On this basis, the correlation model of vehicle load influence parameter, temperature and bridge deflection based on GRU neural network was established. Taking a suspension as the example, the short-term, medium-term, and long-term correlation models were established. The prediction ability of stiffening girder deflection was investigated. A new separation method of temperature- and vehicle-induce deflection components was presented by using the correlation model. The suspension bridge case-study reveals that the predicted deflection based on the short-term correlation model is basically consistent with real-time deflection monitoring data. The medium- and long-term correlation models can accurately predict the deflection extremes in a certain time window. The temperature- and vehicle-induce deflection components separated by using the correlation model have favorable consistency with wavelet decomposition results.
鞠翰文1，邓扬1,2，李爱群1,2. 桥梁结构挠度-温度-车辆荷载监测数据相关性模型[J]. 振动与冲击, 2023, 42(6): 79-89.
JU Hanwen1，DENG Yang1,2，LI Aiqun1,2. Correlation model of deflection-temperature-vehicle load monitoring data for bridge structures. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(6): 79-89.
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