Prediction and analysis of damage to RC columns under close-in blast loads based on machine learning and Monte Carlo method

Abstract

Rapid prediction and quantitative assessment of the damage of reinforced concrete (RC) columns under blast loads are challenging and crucial issues. The key parameters affecting the anti-blast capacity of RC columns are coupled with failure modes. In this study, machine learning (ML) and Monte Carlo (MC) simulations are employed to investigate the damage of RC columns subjected to blast loads. 257 data collected from existing experimental and numerical studies are utilized to establish a database for model training and testing. The damage indexes of columns are predicted using eight ML models with eight input features. The predictive capacity of each model is characterized by eight evaluation indexes through MC simulations. The CatBoost model is identified as the optimal model based on the Analytic Hierarchy Process (AHP). Additionally, the CatBoost model is explained using the SHapley Additive exPlanations (SHAP) method, and the influence of axial compression ratio on column damage is determined to be intricate. The coupling relationship between the axial compression ratio and the scale distance of the column is analyzed. Finally, a zonal diagram is developed. This diagram can be utilized to assess the damage of the RC column quickly and efficiently.