Explainable AI based slip prediction of steel-UHPC interface connected by shear studs

Recently, ultrahigh performance concrete (UHPC)-steel composite structures with shear studs have garnered interest for their potential high ductility and resilience in infrastructure. Ensuring their ductility is crucial for failure prediction and resilient infrastructure development. However, pull-out tests show that studs in UHPC often fail to meet a minimum relative slip of 6 mm, with no established direct methods for predicting this slip. This study proposes a novel approach to evaluate the shear slip at the UHPC-steel interface connected by shear studs, utilizing explainable artificial intelligence. We integrate a Light Gradient Boosting Machine (LightGBM) algorithm with SHapley Additive exPlanations (SHAP) to predict and interpret shear slip. The model development utilizes a dataset of 184 instances from UHPC-steel pullout tests, encompassing 16 features related to UHPC slab characteristics, shear pockets, and stud shear connectors. Engineered features and shear strength are strategically incorporated as predictors under four scenarios to achieve the most optimal model. The best performing model achieves an RMSE of 0.814 mm, an MAE of 0.598 mm, and an R2 of 0.838. The model's superiority is reaffirmed through comparative analysis against five alternative tree-based ensemble models. SHAP analysis demonstrates that features related to the UHPC slab and stud shear connectors contribute 65 % and 35 % respectively to slip prediction. The top influential features are d(st), s(l) and f(c) from the stud, and f(u) from the slab. The engineered features and ultimate load per stud indicate limited efficacy in improving model precision. Higher values of dst and fu positively contribute to shear slip prediction, whereas higher values of sl negatively impact shear slip prediction. Based on several SHAP analyses, a set of parameters for the potential ductile design of stud shear connectors in UHPC slabs is proposed. Overall, the integration of LightGBM and SHAP maintains high generalizability and enhances model transparency, instilling confidence for practical deployment and promising tangible advancements in real-world structural applications.