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- Title
基于不同算法的道路混凝土干缩神经网络预测.
- Authors
周胜波; 申爱琴; 张远、; 万晨光; 洪基
- 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.
- Publication
Jianzhu Cailiao Xuebao: Journal of Building Materials, 2014, Vol 17, Issue 3, p414
- ISSN
1007-9629
- Publication type
Article
- DOI
10.3969/j.issn.1007-9629.2014.03.008