Exploring failure mechanisms in reinforced concrete slab-column joints: Machine learning and causal analysis

Abstract

Reinforced concrete slab-column construction comprises interconnected slabs and columns that constitute the structural system of a building. While providing architectural flexibility and ease of construction, these types of structures are prone to failure because the structural arrangement beneath the slabs is not always considered thoroughly. This research uses machine learning models to conduct an in-depth study and categorizes failure modes into three main types: flexure, punching, and combined flexure-punching modes. These failure modes are classified with appreciable accuracy by eight machine learning approaches: RAGN-L, Random Forest, Extra Trees, K-Nearest Neighbors, Adaptive Boosting, Support Vector Machine, Logistic Regression, and Gaussian Naive Bayes classifiers, optimized using hyperparameter tuning. The results indicate that the RAGN-L achieves the highest accuracy at 0.99, followed by the Random Forest model with an accuracy of 0.98. The study extends the machine learning analysis by investigating the deep causes that rule the complex interactions among key structural parameters. SHAP analysis revealed the influence of features like slab thickness, reinforcement ratio, and punching shear strength on failure modes. Counterfactual analyses further revealed how changes in these parameters can change failure modes and indicate their sensitivity and robustness. The results imply that reducing or optimizing certain parameter values will change the sample types and thus make them change between failure modes. By combining machine learning, SHAP analysis, causal analysis, and counterfactual methods, this study offers valuable insights into the failure mechanisms of slab-column joints and provides actionable recommendations to enhance structural safety and reliability.