基于BP神经网络的FRP加固混凝土柱承载力预测

为提高纤维增强复合材料(FRP)加固混凝土轴压柱承载力的计算精度,建立了FRP加固混凝土轴压柱承载力的BP神经网络预测模型.利用大量试验数据对神经网络模型进行训练,并用训练成熟的神经网络模型对FRP加固混凝土轴压柱的承载力进行了预测.通过模型预测值与试验结果的比较,证明该模型的预测结果具有一定的可信度,最大误差不超过15%,比其他计算模型的精度高.

BP Neural Network-Based Prediction of Load-Bearing Capacity of Concrete Column Reinforced by FRP

In order to enhance the calculation accuracy of concrete columns reinforced by FRP(fiber reinforced polymer) under axial compression,a BP(back propagation) neural network model was established to predict the load-bearing capacity of a concrete column reinforced by FRP under axial compression.The BP neural network model was trained by volume test data,and using the trained model,the load-bearing capacity of concrete columns reinforced by FRP was predicted.A comparison between the predicted and experimental results shows that the BP neural network model can consider more affecting factors and is reliable.Moreover,its maximum error is less than 15%,and its precision is higher than other models.