市政技术2026，Vol.44Issue(9)：45-57,65,14.DOI:10.19922/j.1009-7767.2026.09.045
基于自注意力机制的实桥车辆荷载识别方法研究
Research on a Self-attention-based Vehicle Load Identification Method for Solid Bridges
摘要
Abstract
To address the problem of vehicle load identification being affected by temperature effects,measurement noise and complex operational conditions for in-service bridges,a bridge vehicle load identification method integrating digital image correlation(DIC)-based vision monitoring,variational mode decomposition(VMD)and a Transformer self-attention mechnism is proposed.First,vertical dynamic deflection responses of multiple mid-span measurement points are obtained by the DIC system.VMD is employed to separate the low-frequency trend terms from temperature effects of the raw deflection signal to extract the vehicle-induced deflection response;Then,a bridge vehicle-in-duced deflection-vehicle load dataset are constructed through peak screening,fixed-length segmentation and time matching.Finally,a Transformer-based vehicle load identification model is established and compared with CNN,BiLSTM,and BiLSTM-Transformer models.Based on monitoring data from a 30 m prestressed concrete simply sup-ported hollow slab bridge,2 931 valid samples are constructed.The results show that the proposed method can effec-tively extract vehicle-induced deflection and build a reliable dataset.And that deep learning methods overall achieve high identification accuracy.The overall recognition accuracy is relatively higher by deep learning method.Among them,the Transformer model performs best,with an RMSE of 0.609,an R2 of 0.997,and an overall accuracy of 99.66％within 5％relative error range on the test set.This indicates that the self-attention mechanism is more effec-tive in capturing global correlation features in multi-channel vehicle-induced deflection sequences.This research result can provide reference for accurate identification of vehicle loads on solid bridges and engineering applications of bridge health monitoring.关键词
桥梁车辆荷载识别/深度学习/自注意力机制Key words
bridge vehicle load identification/deep learning/self-attention mechanism分类
交通工程引用本文复制引用
叶以挺,应柳祺,张文达,胡洁亮,张一名,应国刚..基于自注意力机制的实桥车辆荷载识别方法研究[J].市政技术,2026,44(9):45-57,65,14.基金项目
"科技甬江2035"重大应用示范项目(宁波市重大科技任务攻关项目)(2024Z016) （宁波市重大科技任务攻关项目）
