基于应变影响线的桥梁模型修正试验  

Bridge model modification experiment based on strain influence line

作　　者：周宇 甘露一 狄生奎 贺文宇 李宁波 ZHOU Yu;GAN Luyi;DI Shengkui;HE Wenyu;LI Ningbo(College of Civil Engineering,Anhui Jianzhu University,Hefei 230601,China;School of Civil Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China;National-local Joint Engineering Laboratory of Building Health Monitoring and Disaster Prevention Technology,Anhui Jianzhu University,Hefei 230601,China;Operation and Monitoring Center for Hefei Urban Safety and Security,Hefei 230601,China;College of Civil Engineering,Hefei University of Technology,Hefei 230601,China)

机构地区：[1]安徽建筑大学土木工程学院,安徽合肥230601 [2]兰州交通大学土木工程学院,甘肃兰州730070 [3]安徽建筑大学建筑健康监测与灾害预防技术国家地方联合工程实验室,安徽合肥230601 [4]合肥市城市生命线工程安全运行监测中心,安徽合肥230601 [5]合肥工业大学土木与水利工程学院,安徽合肥230601

出　　处：《浙江大学学报（工学版）》2024年第3期537-546,共10页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(51868045);安徽省高校省级自然科学研究资助项目(2022AH050248);建筑健康监测与灾害预防国家地方联合工程实验室开放课题资助项目(GG22KF002);安徽省高校优秀拔尖人才培育资助项目(gxgnfx2022021);甘肃省建设科技资助项目(JK2023-03);企业委托技术开发课题资助项目(HYB20220240,HYB20230001)。

摘　　要：为了验证桥梁应变影响线用于模型修正的有效性,针对某三跨钢板组合连续梁桥在单辆重车移动加载下的应变时程响应进行研究.联合实测应变影响线和计算影响线构建目标函数;以影响线形态控制点处的微应变试验值作为输入层参数,以有限元模型结构几何尺寸信息与材料特征值作为输出层参数,构建反向传播(BP)神经网络进行自我学习;基于训练完毕的BP神经网络,对待修参数进行预测,开展桥梁模型修正研究.结果表明,所提出的有限元模型修正方法能够减小真实结构不确定性带来的建模误差,修正后的优化模型比初始模型更加贴近真实结构,目标函数相对误差降低29%;可以采用基于BP神经网络的模型参数修正方法对有限元模型参数进行预测.The investigation on a three-span steel plate composite continuous girder bridge was conducted to obtain the strain time response under the moving load of a single heavy vehicle,in order to verify the effectiveness of strain influence line for model modification.An objective function was constructed by measured and calculated strain influence lines.The back propagation(BP)neural network was constructed to do self-training,by taking the micro-strain test value at the control point of the influence line form as the input layer parameter,and the structural geometry information and material characteristic value of finite element model as the output layer parameters.The parameters to be modified were predicted and the bridge model modification was carried out,on the basis of the self-trained BP neural network.Results showed that the proposed finite element model modification method can reduce the modelling error caused by the uncertainty of the real structure,the revised optimization model was closer to the real structure than the initial model,and the relative error of the objective function was reduced by 29%.The model parameter modification method based on BP neural network can be used to predict the parameters of the finite element model.

关 键 词：桥梁工程 模型修正 应变影响线 影响线识别 前馈神经网络 

分 类 号：TU317[建筑科学—结构工程]