Machine learning framework for predicting the shear capacity of demountable bolted connectors in composite beams.

ABSTRACT: Steel-concrete composite beams are increasingly adopted in modern construction owing to their high strength, stiffness, and efficiency. Conventional welded shear connectors, while effective, hinder disassembly and recycling, limiting their alignment with sustainable construction practices. To address this, demountable bolted connectors have emerged as a viable alternative, promoting reuse, reduced waste, and compatibility with modular construction. This study presents eight machine learning algorithms including linear, tree-based, and ensemble methods were trained on a hybrid dataset combining experimental that were collected from previous studies and numerical results. Among these ML models, the XGBoost Regressor exhibited the highest accuracy (R² ≈ 0.996) with consistently low error marg

SUBMITTER: Saleh AI 

PROVIDER: S-EPMC12680754 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature