基于卷积神经网络的多车桥梁动态称重算法  

Bridge Weigh-in-motion Algorithm Considering Multi-vehicle Based on Convolutional Neural Network

作　　者：邓露[1] 罗鑫 凌天洋 何维[1] DENG Lu;LUO Xin;LING Tianyang;HE Wei(College of Civil Engineering,Hunan University,Changsha 410082,China)

机构地区：[1]湖南大学土木工程学院,湖南长沙410082

出　　处：《湖南大学学报（自然科学版）》2022年第1期33-41,共9页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金资助项目(751201233)。

摘　　要：为识别多车工况下车辆过桥时的车辆重量,采用卷积神经网络技术开发出可用于多车轴重识别的桥梁动态称重(BWIM)算法.首先,利用车桥耦合系统采集不同车辆过桥时梁底的应变信号;之后,基于深度学习开源框架KERAS搭建了包含9层卷积层、2层全连接层的卷积神经网络(CNN)模型,利用Adam优化器训练CNN模型以拟合所获得的应变信号与车辆轴重在不同工况下的变化规律,并最小化拟合误差;最后,对所开发的算法在单车和多车加载工况下的轴重识别精度进行了对比分析.结果表明:所提出的算法在单车和多车工况下的轴重识别误差均值基本低于5%,并且识别精度对车辆速度和横向位置的变化不敏感,说明算法的轴重识别效果良好且稳定.该多车BWIM算法摆脱了对桥梁影响线的依赖,为不适用于利用影响线方法进行动态称重的桥梁提供了可替代的称重技术.In this study,a new bridge weigh-in-motion(BWIM)algorithm based on the convolutional neural net⁃work technology was developed for identifying the axle weights of multi-vehicles crossing the bridge.First of all,the bridge strains under vehicular loading with variable weight were simulated using the vehicle-bridge coupling simula⁃tion system.Then,a convolutional neural network,consisted of nine convolutional layers and two fully connected lay⁃ers,was developed based on an open source framework for deep learning,i.e.,KERAS.The convolutional neural net⁃work was trained by Adam optimizer to map the relationship between the bridge strain and the vehicle weight under different scenarios,and optimized by minimizing the fitting error.Eventually,the identification accuracy of the pro⁃posed BWIM system was analyzed under the conditions of single-and multi-vehicle loadings.The results show that the mean identifying error of the proposed BWIM was less than 5%for both the single-and multi-vehicle scenarios,and changed slightly with the varying traveling speeds and lateral loading positions,indicating the good and stable performance of the proposed BWIM algorithm in axle weight identification.In addition,the proposed BWIM system doesn’t need a bridge influence line in advance to identify the axle weight,and therefore provides an alternative tech⁃nology for bridges that are not suitable for the influence line method.

关 键 词：桥梁动态称重 卷积神经网络 多车轴重识别 有限元分析 

分 类 号：U446.2[建筑科学—桥梁与隧道工程]