Prediction of concrete compressive strength based on principal component analysis and radial basis function neural network

Abstract

Aiming at the concrete compressive strength data measured in the field laboratory, the data samples are standardized and the missing and abnormal values of the data samples are detected and processed. Then, the principal component factors of several influencing factors are extracted with the idea of “dimensionality reduction” by principal component analysis, and a new data sample set is established. Finally, the radial basis function neural network model is constructed to simulate the concrete compressive strength. The strength is predicted and the predicted value is obtained. The results show that three principal component influencing factors are extracted by principal component analysis, and the RBF network structure with 50 neuron nodes in the hidden layer is established. The error of the predicted value of concrete compressive strength is less than 4%, which meets the control requirements of engineering test accuracy. The predicted model can be used to predict the compressive strength of concrete.