Plastic damage prediction of concrete under compression based on deep learning

Diverse loads frequently damage concrete when it is in use. However, it might be challenging to immediately identify the stress and damage of concrete in actual engineering situations. In order to predict the stress and damage of concrete, a deep learning (DL) model based on the convolutional neural network (CNN) is proposed in this paper. To provide the training and validation data, a finite element (FE) model of uniaxial compression of concrete specimens based on the concrete damage-plasticity (CDP) model is constructed. The DL model is trained with the strain contours with a specified range provided by the FE model as the inputs and the stress and damage assessment of concrete as the outputs. The prediction of the stress and damage of concrete materials was effectively realized by the trained DL model, and it was verified in a larger range of working conditions distinct from the training and verification sets. The results show that the DL algorithm has good accuracy and reliability. By efficiently and correctly recreating the FE prediction results, the DL model offers a method for promptly evaluating the stress and damage of concrete structures under complicated stress circumstances in actual engineering.