基于不同算法的道路混凝土干缩神经网络预测

Shrinkage Prediction of Pavement Cement Concrete Based onDifferent Algorithms Neural Network

关键词:  道路混凝土  干缩预测  神经网络  原料配合比

Key words:  pavement cement concrete  dry shrinkage prediction  neural network  material mix proportion

摘要:

针对多种因素下道路混凝土干缩预测模型难以建立的难题，基于BP神经网络理论建立了干缩预测模型.结果表明：BP神经网络预测道路混凝土干缩可获得较高准确度，且具有良好的泛化能力，在5种算法中，Trainlm训练速度快，但误差大，Traingda函数训练速度居中，误差最小，用其训练的神经网络可很好映射道路混凝土配合比与干缩率之间的非线性关系.

Abstract:

The mathematical prediction model for shrinkage of pavement cement concrete under multi factors is difficult to establish. Therefore, the BP neural network model was developed to predict shrinkage of concrete. Results show that BP neural network can accurately predict the shrinkage of concrete and the model has good ability to generalize. By comparing five different algorithms, the Trainlm algorithm is quick to be trained but has big error, whereas the Traingda algorithm can be trained not as quick as Trainlm but has the minimum error. Hence, the neural network model by applying Traingda algorithm can well reflect the nonlinear relationship between materials mix proportion and the dry shrinkage ratio of pavement cement concrete.