摘要
建立温度-位移相关模型是开展基于位移响应的大跨桥梁性能评估的关键步骤。该文提出一种基于长短时记忆(LSTM)神经网络的多元温度-位移相关模型建立方法。充分利用LSTM神经网络能够考虑位移时滞效应和适合处理超长数据序列的优势,采用自适应矩估计方法对LSTM神经网络进行优化,并引入丢弃正则化技术提升模型的预测能力。在此基础上,基于一座三跨连续系杆拱桥长期同步监测的温度和位移数据,讨论了影响该桥主梁竖向位移的主要温度变量,并建立了多元温度-位移的LSTM神经网络模型,与基于误差反向传播(BP)神经网络的多元温度-位移相关模型进行了比较。研究结果表明:构件有效温度与主梁竖向位移具有明显的非线性关系,构件间温差和主拱温度梯度与主梁竖向位移呈线性相关性;主拱有效温度和主梁与主拱的温差是引起该桥主梁竖向位移的主要温度变量;相比于BP神经网络模型,该文提出的LSTM神经网络模型能够大幅降低温度位移的重构误差和预测误差。
The establishment of temperature-displacement correlation is essential for the displacement-based performance evaluation of long-span bridges. We propose a new method based on long short-term memory(LSTM) neural network for modeling multiple temperature-displacement correlation. The LSTM neural network,which can describe the time-lag effect and is suitable for processing ultralong data sequences, is adopted as the basic neural network. We use the adaptive moment estimation method to optimize the LSTM neural network, and introduce the dropout regularization technique to improve the generalization ability of the model. The predominant thermal variables that affect the vertical displacement in the main girder of the bridge are extracted using long-term synchronous monitoring data of temperatures and displacements obtained from a three-span continuous tied arch bridge. An LSTM neural network between multiple temperature variables and displacements is established. A back propagation(BP) neural network is also modeled for comparison. The results show that the structural effective temperature has a significant nonlinear relationship with the vertical displacement in the main girder, while the temperature difference among structural components and the temperature gradient in the main arch rib have a linear correlation with the vertical displacement in the main girder. The effective temperature inthe main arch rib and the temperature difference between the main girder and the main arch rib are the predominant thermal variables that result in the vertical displacement in the main girder. Compared with the BP neural network model, the LSTM neural network model proposed in this paper can dramatically reduce the reproduction error and prediction error of the thermal displacement.
作者
郑秋怡
周广东
刘定坤
ZHENG Qiu-yi;ZHOU Guang-dong;LIU Ding-kun(College of Civil and Transportation Engineering,Hohai University,Nanjing 210098,China)
出处
《工程力学》
EI
CSCD
北大核心
2021年第4期68-79,共12页
Engineering Mechanics
基金
国家自然科学基金项目(51678218,51978243)。
关键词
结构健康监测
大跨拱桥
温度
位移
神经网络
structural health monitoring
long-span arch bridge
temperature
displacement
neural network
