Prediction of progressive collapse resistance of RC frames using deep and cross network model

Compressive membrane/arch action at small deformations and resistance evolution path at large deformations are two key factors in determining whether an RC frame structure can resist progressive collapse. In this study, two deep learning models based on deep and cross network (DCN) are developed to predict the progressive collapse resistance of RC frames. DCN model I is constructed to predict the compressive membrane/arch action resistance, while DCN model II is developed to predict the resistance displacement curve considering the dynamic effect. 464 records regarding structural collapse were collected through extensive literature review and stringent data filtering. They were randomly divided into 80% and 20% for training and testing the two models, to which the column removal scenario, with or without slab, boundary condition, beam net span, beam section, beam reinforcement ratio, and material property were selected as input features. Moreover, Shapley additive explanations (SHAP) method was introduced to interpret the proposed DCN model. The results indicated that the performance of DCN model I in predicting the compressive membrane/arch action resistance was satisfactory with an MAPE value of 10.66% and an R2 value of 0.9799, respectively, while being more accurate than the existing yield line theory and Park's compressive arch calculation model. The proposed DCN model II can further capture the evolution of the dynamic resistance of RC frames accurately. The two DCN models have been deployed online to public for application.