Insights into the Feature-Selection Mechanisms for Modeling the Shear Capacity of Stud Connectors in Concrete: A Machine Learning Approach

Shear connections between concrete structural elements play a vital role in defining performance and overall stability. However, limitations in traditional methods for predicting the shear capacity (Vu) of stud connectors in concrete have been highlighted. Developing strategies that precisely describe the performance of stud-headed connectors requires insight into their failure mechanisms and the corresponding shear transmission. Therefore, leveraging advancements in machine learning, this study aims to predict the Vu of the headed stud connector in concrete structures using various input parameters. A database (1121) of the shear strength collected from the literature was trained using six machine learning (ML) algorithms: extreme learning machine (ELM), decision tree (DT), artificial neural network (ANN), multi-linear regression (MLR), support vector machine (SVM), and hybrid ANN–particle swarm optimization (ANN-PSO). Feature selection methods and system identification were applied to explore the optimal or most relevant input parameters. The feature selection techniques indicated that the geometric properties of the stud connector (diameter and cross-sectional area), the concrete modulus of elasticity (Ec), and the height of the weld collar (hw) are the most relevant input variables. The ANN-PSO model outperformed the other classical models in estimating the shear capacity at two modeling stages. The hybrid ANN-PSO achieved R2 = 0.976, MAE = 7.61 kN, RMSE = 10.8 kN, and MAPE = 8.04%, demonstrating the best predictive accuracy among the classical models. On the other hand, DT is the second-best model, with an R2 of 0.958, MAE of 10.27 kN, RMSE of 14.43 kN, and MAPE of 8.53 kN for forecasting the shear capacity of stud connectors in concrete.