中外公路2025，Vol.45Issue(3)：112-120,9.DOI:10.14048/j.issn.1671-2579.2025.03.014
基于DE-BP神经网络的混凝土箱梁热学参数反分析
Inverse Analysis of Thermal Parameters of Concrete Box Girder Based on DE-BP Neural Network
摘要
Abstract
In view of temperature cracks in concrete box girders easily occurring during construction,an inverse analysis method based on uniform design theory and differential evolution back propagation(DE-BP)neural network was proposed to accurately obtain the thermal parameters of concrete box girders and ensure the reliability of temperature analysis of concrete box girders.This method established the nonlinear relationship between the temperature peak of characteristic points and the thermal parameters through the DE-BP neural network.By using the uniform design method and the Abaqus finite element numerical model,130 sets of sample data were generated.Based on the ratio of 12∶1 for training samples to test samples,the back analysis model was trained.The results show that the mean absolute percentage errors EMAPE of the DE-BP neural network model are all less than 3%,and the relative errors are less than 5%.This indicates that the prediction accuracy of the BP neural network can be effectively improved by the DE algorithm.The maximum error of the temperature peak for the characteristic points based on inversion analysis is 2.05℃,and the calculated temperature histories are in good agreement with the actual ones.In a word,the back analysis method of thermal parameters for the concrete box girder based on the DE-BP neural network and uniform design theory demonstrates high accuracy and a stable inversion process with good reliability,which can provide a theoretical basis for temperature control of other similar projects.关键词
桥梁工程/混凝土箱梁/热学参数/DE-BP神经网络/均匀设计/参数反演/温度控制Key words
bridge engineering/concrete box girder/thermal parameter/DE-BP neural network/uniform design/parameter inversion/temperature control分类
交通工程引用本文复制引用
姚勇,闫宇,孙博文,王岳松,蒋田勇..基于DE-BP神经网络的混凝土箱梁热学参数反分析[J].中外公路,2025,45(3):112-120,9.基金项目
国家自然科学基金资助项目(编号:52078058,52378123) （编号:52078058,52378123）
湖南省自然科学基金创新研究群体项目(编号:2020JJ1006) （编号:2020JJ1006）
湖南省教育厅自然科学研究重点项目(编号:21A0196) （编号:21A0196）
长沙市自然科学基金资助项目(编号:kq2202209) （编号:kq2202209）
