Deep learning-based prediction of structural responses of RC slabs subjected to blast loading

Considering the risk of explosion, studying the dynamic structural behavior of reinforced concrete (RC) slabs subjected to blast loads is crucial for enhancing their resistance. Conventional approaches, including costly experimental tests, highly hypothetical analytical methods, and time-consuming numerical simulations, have their limitations. This study presents two deep learning-based models for rapid and accurate prediction of explosion-induced responses in RC slabs. Available literature data and supplemented numerical simulation data are used for training and testing. First, a multi-layer perceptron (MLP) model is established to predict the maximum displacement of RC slabs subjected to explosions. The input features include 10 parameters. The training results show that the MLP model exhibits good prediction performance. Second, a one-dimensional convolutional neural network (1D-CNN) model is constructed to predict the failure modes of RC slabs under explosive loads. The input features of the model are consistent with those of the MLP model. The 1D-CNN model outperforms the other five conventional machine learning models in classification. Furthermore, a permutation feature importance analysis is conducted to determine the effects of the 10 input features on the predicted outcomes. This analysis makes the predictions interpretable, thereby bolstering the credibility of the models. Results show that the proposed deep learning models offer an efficient and reliable method to predict the structural response of RC slabs under explosion loads.