The investigation into the failure criteria of concrete based on the BP neural network

Abstract

This paper proposes a method for modelling the failure criteria of concrete through the back propagation (BP) neural network. The mappings between the inputs and outputs are constructed through the mechanical analyses based on two types of failure mechanisms of concrete. The influences of the number of neurons and the activation functions in the hidden layer on the modelling results are studied by tuning the hyperparameters of the neural network. The BP neural network is trained systematically with the hidden layer of different numbers of neurons to construct the failure criteria based on the octahedral shear stress and various failure modes of concrete, respectively. The models are examined by comparing the mean square errors (MSEs) and coefficients of determination (R2). And the geometrical characteristics of the failure surfaces, the tensile and compressive meridians and the envelope features on the deviatoric plane are analyzed to evaluate the failure criteria models. The optimal configuration of the BP neural network is selected through the comparative analyses including the number of neuronal nodes, the activation functions in the hidden layer, and the input and output variables. Compared with the Ottosen failure criterion, the failure surfaces obtained through the BP neural network achieve higher accuracy, being consistent with the morphological characteristics of the failure surfaces described by previous experiments and the classical hypotheses.