Utilizing ensemble learning in the classifications of ductile and brittle failure modes of UHPC strengthened RC members

Abstract

AbstractThis study aims to achieve the swift and precise classification of ductile and brittle failure modes in flexural reinforced concrete (RC) members, specifically those with tension sides strengthened by ultrahigh performance concrete (UHPC). Employing six ensemble learning techniques—Bagging, Random Forest, AdaBoost, Gradient Boosting, XGBoost, and LightGBM—the authors utilize a comprehensive dataset comprising 14 features, which include manually labeled failure modes obtain from load–deflection curves. The model training spans four scenarios, varying in the inclusion or exclusion of features describing the cross-sectional area of RC members and moment resistance. XGBoost emerges as the most effective classifier, achieving an impressive 84% accuracy with high confidence. Additionally, the study employs the Shapley Additive Explanation (SHAP) technique on the best-performing model to illuminate the significance and impacts of various features in UHPC-strengthened flexural members’ failure modes. Notably, moment resistance and UHPC tensile strength surface as the most influential factors in predicting failure modes. Increased rebar yield strength, UHPC compressive strength, UHPC reinforcement ratio, and steel fiber volume in UHPC contribute to enhanced ductility in flexural members, while heightened moment resistance and UHPC layer thickness, along with a robust RC-UHPC interface, tend to induce brittleness. The introduction of such an effective failure modes classification model, coupled with the model’s explainability, instills trust in its predictions and facilitates seamless integration into real-world applications, particularly in seismic areas. The model’s ability to operate without the need for pre-experimental tests marks a significant advancement in the field.