考虑时空相关性的桥梁监测数据多通道联合恢复方法  

Joint recovery method for multi-channel bridge monitoring data considering spatiotemporal correlation

作　　者：辛景舟 杨伟彤 周建庭[1,2] 唐启智 周朝营 张洪[1,2] XIN Jingzhou;YANG Weitong;ZHOU Jianting;TANG Qizhi;ZHOU Chaoying;ZHANG Hong(State Key Laboratory of Mountain Bridge and Tunnel Engineering,Chongqing Jiaotong University,Chongqing 400074,China;School of Civil Engineering,Chongqing Jiaotong University,Chongqing 400074,China)

机构地区：[1]重庆交通大学省部共建山区桥梁及隧道工程国家重点实验室,重庆400074 [2]重庆交通大学土木工程学院,重庆400074

出　　处：《振动工程学报》2025年第3期558-566,共9页Journal of Vibration Engineering

基　　金：国家自然科学基金资助项目(52278292,52208264);重庆市杰出青年科学基金资助项目(CSTB2023NSCQ-JQX0029);贵州省交通运输厅科技项目(2023-122-001)。

摘　　要：桥梁健康监测数据由于受到传感器故障等因素的影响,易发生数据缺失的情况。然而,现有监测数据恢复方法尚未有效利用数据的时间与空间相关性。为此,本文提出了一种考虑时空相关性的桥梁监测数据多通道联合恢复方法。采用卡尔曼滤波归一化处理原始数据,消除随机误差的影响;将预处理后的数据划分为训练集和测试集,通过滑窗方式构造训练样本,并对样本进行掩码处理,将数据恢复问题转化为时间序列预测问题;利用传感器自身历史数据的时空相关性,构建端到端的长短期记忆神经网络进行训练,从而实现缺失数据的恢复;基于某悬索桥主梁挠度和吊索索力监测数据验证所提方法的有效性,讨论该方法恢复单通道及多通道数据的性能。结果表明,与传统循环神经网络相比,在数据缺失率为60%时,所提方法可以实现22%的精度提升;此外,该方法能充分利用数据通道间的时空相关性,实现多个通道数据的同时恢复。Bridge health monitoring data often encounter missing values due to sensor failures and other factors.Existing data recovery methods have not effectively utilized the temporal and spatial correlations in the data.In this paper,a multi-channel recovery method for bridge monitoring data based on temporal and spatial correlations is proposed.The original data is preprocessed using a Kalman filter to eliminate random errors.The preprocessed data is divided into training and testing sets,and training samples are constructed using a sliding window approach with masking.The data recovery issue is formulated as a time series prediction issue.Besides,an end-to-end LSTM network architecture is trained to leverage the temporal and spatial correlations in the historical data of the sensors which enables the recovery of missing data.The proposed method is validated using the measured deflection and cable force data from a suspension bridge,and the performance of single-channel and multi-channel data recovery is discussed.Compared to the traditional RNN models,results show that the proposed method achieves a 22% improvement in accuracy when the data missing rate is 60%.Moreover,the method effectively utilizes the temporal and spatial correlations among different channels,enabling simultaneous recovery of data from multiple channels.

关 键 词：桥梁健康监测 桥梁工程 数据恢复 长短期记忆神经网络 时空相关性 

分 类 号：U446[建筑科学—桥梁与隧道工程]