• Novel FEM-ML approach strengthens RC deep beams with opening using Ni-Ti SMA. • FS7 SMA configuration increased peak load by 10% and ductility. • GEP method provided interpretable shear capacity equations. • SHAP and PDP identified beam depth, concrete strength, hole diameter are critical. • Study bridges SMA-ML gap offering structural design solutions. The design of deep beams constructed with reinforced concrete (RC) having wide openings presents a significant engineering challenge due to the absence of specific provisions in existing design codes. This study explores the use of Ni-Ti shape memory alloys (SMA) for strengthening RC deep beams, aiming to mitigate brittle shear failure commonly encountered in conventional designs. Finite element modelling (FEM) and machine learning (ML) algorithms were employed to assess the performance of RC deep beams. FEM simulations investigated various SMA configurations, identifying the FS7 layout as optimal, yielding a 10% increase in peak load capacity, enhanced ductile behaviour, improved energy absorption, and prevention of catastrophic structural failure. Additionally, six ML models were systematically evaluated using a dataset of 180 data points generated through Abaqus simulations to analyse the influence of key parameters such hole diameter, concrete compressive strength, and beam depth. The models’ performance was assessed using the coefficient of determination (R²) and other statistical metrics. To improve model’s interpretability, Shapley Additive Explanations (SHAP) and Partial Dependence Plots (PDPs) were utilized, providing insight into critical input-output relationships. Among the evaluated models, the Support Vector Machine (SVM) demonstrated superior performance, achieving R² values of 0. 957 for training and 0. 956 for testing, establishing it as the most effective predictive model for RC deep beam performance. This study emphasizes a data-driven framework for enhancing deep beam behaviour and developing efficient, practical structural solutions.
Ismaeel et al. (Wed,) studied this question.