摘要
为更准确地预测再生保温混凝土28 d的抗压强度,在Matlab2014a平台上通过遗传算法对BP神经网络的初始权值及阈值进行改进,建立了基于BP神经网络和遗传算法的再生保温混凝土28 d抗压强度预测模型,并采用试验数据作为训练样本和预测样本对GA-BP神经网络及BP神经网络预测模型的性能进行了比较。结果表明:与BP神经网络相比,GA-BP神经网络预测的相对误差较小且相对误差的变化较稳定。
In order to predict the 28 days compressive strength of recycled thermal insulation concrete more accurately,the initial weights and thresholds of BP neural network are improved by genetic algorithm on Matlab 2014 a platform.A prediction model of 28 days compressive strength of recycled thermal insulation concrete based on BP neural network and genetic algorithm is established.The GA-BP neural network and BP neural network are trained and predicted by using experimental data as training samples and prediction samples.The performance of the prediction model is compared.The results show that compared with BP neural network,the relative error of GA-BP neural network prediction is smaller and the change of relative error is more stable.
作者
白浩杰
刘元珍
郭耀东
赵敏
BAI Haojie;LIU Yuanzhen;GUO Yaodong;ZHAO Min(College of Civil Engineering,Taiyuan University of Technology,Taiyuan 030024,China)
出处
《混凝土》
CAS
北大核心
2020年第11期16-19,25,共5页
Concrete
基金
国家自然科学项目(51678384)
山西省留学回国人员科研资助项目(2017-038)。
关键词
再生保温混凝土
BP神经网络
GA-BP神经网络
抗压强度
预测模型
recycled aggregate thermal insulation concrete
BP neural network
GA-BP neural network
compressive strength
prediction model