Radial Basis Function Neural Network Models for Peak Stress and Strain in Plain Concrete under Triaxial Stress

Abstract

In the analysis or design process of reinforced concrete structures, the peak stress and strain in plain concrete under triaxial stress are critical. However, the nonlinear behavior of concrete under triaxial stresses is very complicated; modeling its behavior is therefore a complicated task. In the present study, several radial basis function neural network (RBFN) models have been developed for predicting peak stress and strain in plain concrete under triaxial stress. For the purpose of constructing the RBFN models, 56 records including normal- and high-strength concretes under triaxial loads were retrieved from literature for analysis. The K-means clustering algorithm and the pseudoinverse technique were employed to train the network for extracting knowledge from training examples. Besides, the performance of the developed RBFN models was estimated by the method of three-way data splits and K-fold cross-validation. On the other hand, a comparative study between the RBFN models and existing regression models was made. The results demonstrate the versatility of RBFN in constructing relationships among multiple variables of nonlinear behavior of concrete under triaxial stresses. Moreover, the results also show that the RBFN models provided better accuracy than the existing parametric models, both in terms of root-mean-square error and correlation coefficient.