钢-混组合梁挠度增大系数的神经网络计算方法  

Neural network-based calculation of deflection increasing coefficient of steel-concrete composite beam

作　　者：魏海斌[1] 张仰鹏 焦峪波[1] 刘寒冰[1] 

机构地区：[1]吉林大学交通学院,长春130022

出　　处：《吉林大学学报（工学版）》2014年第4期963-967,共5页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(51378236);吉林省科技发展计划项目(20130101039JC);吉林大学'985工程'项目

摘　　要：以简支组合梁承受均布和集中荷载为例,考虑了界面滑移效应,引入挠度增大系数的概念,以ANSYS模拟得到的组合梁有限元模型产生样本数据,将BP神经网络用于挠度增大系数的计算,推导得出了增大系数的闭合解公式。A neural network-based calculation method of deflection increasing coefficient of steel- concrete composite beam is proposed. This method takes the complexity and limitation of existing methods into consideration. The simply supported composite beam under concentrated load and uniformly distributed load is taken as example. Considering the effect of interface slip, the concept of deflection increasing coefficient is put forward. Sample data of the finite element model of composite beam are generated from simulation on ANSYS. BP neural network is trained and texted first using the sample data; then, it is applied to obtain the analytical formula of the deflection increasing coefficient.

关 键 词：道路工程 组合梁 挠度增大系数 神经网络 滑移效应 

分 类 号：U448[建筑科学—桥梁与隧道工程]