Machine Learning-Based Methods for Predicting the Structural Damage and Failure Mode of RC Slabs Under Blast Loading

Abstract

Reinforced concrete (RC) slabs are the main load-bearing member of engineering structures, which may be threatened by blast loading. Predicting and analyzing the damage condition and failure mode of RC slab is a necessary means to ensure structural safety and reduce the potential hazards. In this study, two machine learning (ML) models are proposed using data from the published literature and complementary numerical simulations. By comparing six algorithms, it is determined that Extreme Gradient Boosting (XGBoost) is the optimal structural damage model and Categorical Boosting (CatBoost) is the optimal failure mode classification model. In addition, the Shapley additive explanations (SHAP) method was used to analyze the importance and correlation of features. The results show that the TNT charge mass, explosion distance, and compressive strength are the key features. On this basis, when the TNT charge mass is more than 2.5 kg, the sensitivity of the explosion distance increases, and when the compressive strength is more than 50 MPa, the impact on the structural damage is not significant. The research results can predict the structural damage and failure modes of RC slab under blast loading quickly and accurately, and provide guidance for the explosion-proof design of RC slabs.

Keywords

• machine learning
• blast loading
• reinforced concrete slabs
• damage prediction
• Shapley additive explanation
• failure mode