基于神经网络的粉煤灰混凝土强度与干缩性能模型  

Model of Strength and Dry Shrinkage Performance of Fly Ash Concrete Based on Neural Network

作　　者：刘颖[1] LIU Ying(Shaanxi College of Communication Technology,Xi'an 710018,China)

机构地区：[1]陕西交通职业技术学院

出　　处：《粉煤灰综合利用》2019年第4期102-104,共3页Fly Ash Comprehensive Utilization

基　　金：陕西省教育科学“十三五”规划2018年度课题，课题名称：多元信息化教学与高职应用数学课程相融合的实践研究（SGH18V028）

摘　　要：粉煤灰混凝土的强度、干缩性是影响其承载性能与裂缝率的关键要素,为准确测度两指标性能,提出一种神经网络模型,通过实验对比,选择最优学习函数,用L-M、trainlm两种学习算法构建两类指标质量评估预测模型。结果显示,神经网络模型可以准确预测粉煤灰混凝土强度及干缩性能,预测结果与实测值之间的差距较小,应用价值较高。The strength and shrinkage of fly ash concrete are the key factors affecting the bearing capacity and crack rate. To accurately measure the performance of two indexes, a neural network model is proposed. Through experimental comparison, the optimal learning function is selected. And trainlm two learning algorithms to construct two types of indicators quality assessment prediction model. The results show that the neural network model can accurately predict the strength and dry shrinkage performance of fly ash concrete. The difference between the predicted results and the measured values is small, but the application value is high.

关 键 词：神经网络 粉煤灰混凝土 强度与干缩性能 

分 类 号：TU502[建筑科学—建筑技术科学]