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基于深度学习的高速铁路简支梁桥震致损伤预测

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基于卷积神经网络,提出了一种快速预测高速铁路无砟轨道-简支梁桥系统震致损伤的方法.为得到更多地震动信息,通过连续小波变换,将一维地震动数据输入变换成三维图像输入.通过对比损伤样本库中的结果,验证了提出方法的可靠性,分析了卷积神经网络的不同超参数对预测结果和训练时长的影响,得到了贝叶斯优化后的卷积神经网络超参数组合,对比了不同抗震分析方法得到高速铁路无砟轨道-简支梁桥系统震致损伤所需时间.利用优化后的卷积神经网络预测了高速铁路无砟轨道-简支梁桥系统中不同关键构件的震致损伤.研究表明:初始学习率是影响网络预测准确度的最主要因素,学习率下降系数、最小批次及训练轮数会对网络预测结果造成一定影响.而训练卷积神经网络所需时长主要由训练轮数及最小批次决定.提出的方法对高速铁路无砟轨道-简支梁桥系统中不同构件的震致损伤均具有较高预测准确度,网络结构具有较高的适用性,优化后的卷积神经网络训练耗时更短且对高速铁路无砟轨道-简支梁桥系统震致损伤预测更准确.研究成果可为震后高速铁路系统震致损伤的快速修复提供参考.
Prediction of seismic-induced damage on high-speed railway simply-supported bridge based on deep learning
A rapid prediction method of seismic-induced damage in high-speed railway ballastless track simply-supported bridge system is proposed based on convolutional neural networks.To obtain more information of seismic motion,one-dimensional seismic motion data is transformed into three-dimensional image through continuous wavelet transform as the input of convolutional neural network.The reliability of the proposed method is validated by comparing with results in damage samples database.The influence of different hyperparameters of convolutional neural networks on prediction results and training duration are analyzed,and a combination of hyperparameters of convolutional neural networks optimized by Bayesian optimization is obtained.The time required for seismic analysis of high-speed railway ballastless track simply-supported bridge system using different seismic analysis methods is compared.The optimized convolutional neural network is utilized to predict seismic-induced damage of different key components in high-speed railway ballastless track simply-supported bridge system.The research indicates that the initial learning rate is the most significant factor affecting the accuracy of network prediction,while the learning rate decay factor,batch size,and number of training epochs have certain effects on the network prediction results.The training duration of convolutional neural network is mainly determined by the number of training epochs and batch size.The proposed method demonstrates high prediction accuracy for seismic-induced damage in various components of high-speed railway ballastless track simply-supported bridge system,and the network structure exhibits high applicability.The optimized convolutional neural network has shorter training time and more accurate prediction for seismic-induced damage in high-speed railway ballastless track simply-supported bridge system.The research findings can provide reference for rapid repair of seismic-induced damage in high-speed railway systems after earthquakes.

high-speed railway ballastless track simply-supported bridge systemseismic-induced damagerapid predictionconvolutional neural networkBayesian optimization

吴凌旭、蒋丽忠、钟天璇、易江、冯玉林、赵坚、周旺保

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中南大学 土木工程学院,湖南 长沙410075

中南大学 工程结构抗震研究中心,湖南 长沙410075

高速铁路建造技术国家工程研究中心 高速铁路工程结构抗震研究所,湖南 长沙410075

贵州电网有限责任公司 电网规划研究中心,贵州 贵阳550000

华东交通大学 土木建筑学院,江西 南昌330013

湖南云景建设有限责任公司,湖南 长沙410075

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高速铁路无砟轨道-简支梁桥系统 震致损伤 快速预测 卷积神经网络 贝叶斯优化

国家自然科学基金项目国家自然科学基金项目国家自然科学基金项目国家重点研发计划课题湖南省科技人才托举工程中南大学前沿交叉研究项目澳门特别行政区科学技术发展基金项目智慧城市物联网国家重点实验室(澳门大学)开放课题中南大学中央高校基本科研业务费专项资金项目中国博士后科学基金面上项目高速铁路建造技术国家工程研究中心开放基金项目

5207848752178180524785172022YFC30043042022TJ-Y102023QYJC006SKL-IOTSCUM-2024-2026SKL-IoTSCUM-2024-2026/ORP/GA08/20232022ZZTS01552022M713544HSR202202

2024

10.13197/j.eeed.2024.0503

地震工程与工程振动
中国力学学会 中国地震局工程力学研究所

地震工程与工程振动

CSTPCD北大核心
影响因子：0.658
ISSN：1000-1301
年,卷(期)：2024.44(5)