摘要
针对混凝土箱梁在施工阶段易出现温度裂缝的问题,该文提出一种基于均匀设计理论和DE⁃BP神经网络的反分析方法,旨在准确获得混凝土箱梁的热学参数,确保混凝土箱梁温度分析的可靠性。该方法通过DE⁃BP神经网络建立特征点的温度峰值与热学参数之间的非线性关系,结合均匀设计法和Abaqus有限元数值模型生成130组样本数据,按照12∶1的训练样本与测试样本进行反分析模型的训练。结果表明:DE⁃BP神经网络模型的平均绝对百分比误差EMAPE均小于3%,相对误差均小于5%,表明DE算法能够有效提高BP神经网络的预测精度;基于反演分析的特征点温度峰值的最大误差为2.05℃,计算温度历程与实际温度历程吻合程度较好。综上所述,该文提出的基于DE⁃BP神经网络与均匀设计理论的混凝土箱梁热学参数反分析方法精度较高且反演过程稳定,表现出较好的可靠性,可为同类型工程的温度控制提供理论依据。
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.
作者
姚勇
闫宇
孙博文
王岳松
蒋田勇
YAO Yong;YAN Yu;SUN Bowen;WANG Yuesong;JIANG Tianyong(China National Chemical Construction Investment Co.,Ltd.,Beijing 102308,China;Central Southern Safety&Environment Technology Institute Co.,Ltd.,Wuhan,Hubei 430081,China;School of Civil and Environmental Engineering,Changsha University of Science&Technology,Changsha,Hunan 410114,China)
出处
《中外公路》
2025年第3期112-120,共9页
Journal of China & Foreign Highway
基金
国家自然科学基金资助项目(编号:52078058,52378123)
湖南省自然科学基金创新研究群体项目(编号:2020JJ1006)
湖南省教育厅自然科学研究重点项目(编号:21A0196)
长沙市自然科学基金资助项目(编号:kq2202209)。
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
bridge engineering
concrete box girder
thermal parameter
DE-BP neural network
uniform design
parameter inversion
temperature control
