Prediction of ultimate load capacity of demountable shear stud connectors using machine learning techniques.

ABSTRACT: This study investigates the use of machine learning (ML) models to predict the ultimate load capacity of demountable shear connectors in steel-concrete composite structures. A dataset of 239 experimental and numerical records was assembled, incorporating critical features such as bolt diameter, bolt yield and ultimate strengths, concrete and grout compressive strengths, and multiple interfacial friction coefficients. Eight supervised ML algorithms were evaluated: Linear Regression, Ridge, Lasso, K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Decision Tree, Random Forest, and XGBoost. Model performance was assessed using R², Mean Absolute Error (MAE), and Mean Squared Error (MSE). Among these, XGBoost and Random Forest delivered the best predictive accuracy, with R² values of 0

SUBMITTER: Saleh AI 

PROVIDER: S-EPMC12370999 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature