Predicting punching shear in RC interior flat slabs with steel and FRP reinforcements using Box-Cox and Yeo-Johnson transformations

Abstract

This study presents a new approach for predicting the punching shear strength of reinforced concrete flat plates reinforced with steel and fiber-reinforced polymer (FRP) bars, using four machine learning regression models. A dataset of 505 interior flat plates from previous literature was used to develop the models. Input variables were transformed using Box-Cox and Yeo-Johnson transformations to improve predictive power by reducing white noise. The SHapley Additive exPlanations (SHAP) framework was utilized as the Explainable Artificial Intelligence (XAI) method to decipher model execution and feature the main info factors for anticipating the punching shear capacity. Additionally, design codes and equations from the literature are compared to the efficiency and accuracy of the power transformation featured XGBoost model. The findings demonstrate that the suggested prediction model performed very well and is appropriate for estimating the punching shear capacity of slab-column connections reinforced with steel and FRP bars. Moreover, the power transferred XGBoost model performed better than other numerical equations because it had the highest mean R2 (0.93) and lowest MAPE (0.20) for testing, which suggests that machine learning models may be able to offer an elective strategy to currently used mechanics-based models for configuration practice.