基于神经网络的泡沫混凝土强度及导热性能预测

基于神经网络原理,建立预测泡沫混凝土性能的BP神经网络模型,期望通过输入配合比主要参数,得到泡沫混凝土强度及导热性能的预测结果。将实验数据分为训练组和对照组,对训练组进行非线性拟合,若拟合结果满足误差精度则模型建立完毕;通过拟合结果与对照组的比较,可验证模型预测精度。结果表明,BP神经网络模型能够准确拟合实验数据,利用其泛化能力进行预测的结果与对照组的误差小于8%,该模型具有很高的预测精度。

Prediction of Foamed Concrete Compression Strength and Thermal Conductivity Based on BP Neural Network

In this paper, BP neural network model is used to predict the compression strength and thermal conductivity of the foamed concrete. The experimental data were divided into training dataset and control dataset. On the training dataset, the proposed BP?neural?network?model was applied. The fitted model was obtained by tuning the parameters of mixing proportion with error rate controlled at pre-defined level. The prediction accuracy of the model was verified by comparing the results of the fitted model on the control dataset with true values. The results show that the predicted error rate is less than 8%, indicating that BP neural network is capable of predicting the experimental data accurately.