摘要
针对现有以车辆响应间接识别桥梁损伤方法的不足,基于公路桥梁车桥耦合振动理论,将车桥耦合振动响应与不同类型的深度学习模型相结合,对桥梁损伤间接识别方法的应用进行拓展.以三跨连续梁桥为例,建立汽车和桥梁分析模型,以单元刚度折减的方式模拟桥梁损伤,拟定不同的损伤工况.考虑路面粗糙度的随机性,在多种路面粗糙度下进行车桥耦合振动分析,获取车辆的竖向加速度振动信号.运用以车辆加速度响应作为网络输入的端对端损伤识别方法,分别构建一维卷积神经网络(1D-CNN)和卷积长短期记忆神经网络(CNN-LSTM)两种模型,对两者的识别效果进行对比分析.研究结果表明:两种网络均取得了较好的识别效果,CNN-LSTM在识别准确度和计算效率上要比1D-CNN更有优势.
In response to the shortcomings of existing methods for indirectly identifying bridge damage based on vehicle response,this study addresses the issue by integrating the coupling vibration response of the vehicle-bridge system with various types of deep learning models,employing the theory of highway bridge-vehicle coupling vibration.The application of indirect bridge damage identification methods is expanded using a three-span continuous beam bridge as an example.Carrying out vehicle and bridge analysis models,bridge damage is simulated by reducing the unit stiffness,and different damage scenarios are established.Considering the randomness of road surface roughness,vehicle-bridge coupling vibration analysis is conducted under various road surface roughness conditions to obtain the vertical acceleration vibration signals of the vehicle.Applying an end-to-end damage identification method with vehicle acceleration response as the network input,two models,namely,one-dimensional convolutional neural network(1D-CNN)and convolutional long short-term memory neural network(CNN-LSTM),are constructed.A comparative analysis of the identification performance of both models is carried out.The results indicate that both networks achieve satisfactory identification results,with CNN-LSTM having advantages in terms of identification accuracy and computational efficiency over 1D-CNN.
作者
陈代海
崔华
李整
许世展
张宇
CHEN Daihai;CUI Hua;LI Zheng;XU Shizhan;ZHANG Yu(College of Water Resources and Civil Engineering,Zhengzhou University,Zhengzhou 450001,China;Henan Transportation Investment Group Co.,Ltd.,Zhengzhou 450016,China)
出处
《应用基础与工程科学学报》
北大核心
2026年第1期188-198,共11页
Journal of Basic Science and Engineering
基金
国家自然科学基金项目(51408557)
中国博士后科学基金项目(2013M541995)
河南省交通运输厅计划项目(2020J-2-6)。
关键词
公路桥梁
车桥耦合振动
损伤识别
深度学习
卷积神经网络
车辆响应
highway bridges
vehicle-bridge coupling vibration
damage identification
deep learning
convolutional neural network
vehicle response
