A machine learning-based framework for predicting of punching shear capacity of RC flat slabs incorporating recycled coarse aggregates

Abstract

The growing emphasis on sustainable construction has spurred interest in utilizing recycled coarse aggregates (RCA) in structural concrete applications. However, the incorporation of RCA can significantly change the mechanical behavior of structural elements, particularly their punching shear resistance, a critical design consideration in flat slabs. Predicting the punching shear capacity (PSC) of reinforced concrete slabs is the goal of traditional analytical models and design guidelines. However, because material qualities are inherently variable and include intricate, nonlinear interactions, these models frequently fall short of producing accurate predictions. In response to this challenge, the present study proposes a robust data-driven framework for PSC prediction using four machine learning (ML) models: Gradient Boosting Machine (GBM), Extreme Learning Machine (ELM), Multiple Linear Regression (MLR), and Support Vector Regression (SVR). A curated dataset comprising 101 experimental observations was employed, encompassing eleven key input variables related to geometry, material properties, and reinforcement. The models were trained and validated using a 70:30 split and evaluated via multiple statistical indices, including R², RMSE, MAE, NSE, and WMAPE. GBM consistently outperformed the other models, achieving the highest prediction accuracy in both training and testing phases. To enhance model interpretability, advanced diagnostic tools such as Taylor diagrams, Regression Error Characteristic (REC) curves, and Cosine Amplitude Method (CAM)-based sensitivity analysis were employed. The results highlighted the dominant influence of concrete compressive strength, reinforcement properties, and cement content on PSC, providing critical insight into design priorities when using RCA.