摘要
神经网络(ANN)模型作为土木工程领域中一种有效的方法能够用于解决复杂的问题。基于试验数据采用神经网络对钢筋混凝土剪力墙的抗剪承载力进行预测,收集160个钢筋混凝土剪力墙在低周往复荷载下的试验数据,建立数据库,选取140个试验样本对ANN模型进行训练,20个试验样本进行测试验证。ANN1和ANN2有14个输入参数:混凝土抗压强度、剪跨比、轴压比、竖向钢筋强度、横向钢筋强度、墙体竖向分布钢筋配筋率、墙体水平分布钢筋配筋率、边缘构件纵向钢筋配筋率、边缘构件横向钢筋配筋率、边缘构件与截面面积比、截面高厚比、总截面面积、墙高和截面形状,输入数据分别被归一化到区间[0,1]和[0.1,0.9]。两个模型的输出数据均为剪力。对比分析ANN模型预测的钢筋混凝土剪力墙抗剪承载力与采用规范GB 50011和ACI 318-14公式计算的抗剪承载力,结果表明,神经网络模型能够精确地预测钢筋混凝土剪力墙的抗剪承载力,具有较好的预测和泛化能力。
In various areas of civil engineering,the artificial neural network(ANN)model is used to solve complex problems.In this study,ANN models were used to predict the shear bearing capacity of RC shear walls.Based on the results of 160 experiments,a database was constructed that included the performance of RC shear walls under cyclic loading.One hundred and forty samples were chosen to train the ANN models,and 20 were used for validation.There were fourteen inputs parameters:concrete compressive strength,aspect ratio,axial compression ratio,vertical bar yield strength,horizontal bar yield strength,web vertical reinforcement ratio,web horizontal reinforcement ratio,boundary region vertical reinforcement ratio,boundary region horizontal reinforcement ratio,sectional area ratio,sectional height thickness ratio,total section area,wall height,and section shape.ANN1 and ANN2 were normalized in intervals of[0,1]and[0.1,0.9],respectively.The shear force of the RC shear walls was the output data for both models.The predictions by the ANN models and the code methods from GB 50011 and ACI 318 were compared.The results reveal that the developed models exhibit better prediction and generalization capacity for RC shear walls than the code methods.
作者
郭文烨
张健新
GUO Wenye;ZHANG Jianxin(School of Civil and Transportation Engineering,Civil Engineering Technology Research Center of Hebei Province,Hebei University of Technology,Tianjin 300401,P.R.China)
出处
《土木与环境工程学报（中英文）》
CSCD
北大核心
2021年第1期137-144,共8页
Journal of Civil and Environmental Engineering
基金
Natural Science Foundation of Hebei Province(No.E2018202290)。
关键词
神经网络
剪力墙
钢筋混凝土
模型预测
抗剪承载力
artificial neural network
shear wall
reinforced concrete
model prediction
shear bearing capacity
