Life Sciences Commons^™

Application Of Machine Learning Models In The Capacity Prediction Of Ccfst Columns, Khaled Megahed, Nabil Said Mahmoud, Saad Elden Mostafa Abd-Rabou

Mansoura Engineering Journal

Circular concrete-filled steel tubular (CCFST) columns are widely utilized in structural engineering due to their impressive load-bearing capabilities and ductility. Existing design standards often yield disparate outcomes when applied to structural columns with identical properties, introducing uncertainty for engineering designers. This study introduces an innovative technique to address these challenges using two machine learning (ML) models: Gaussian process regression (GPR) and extreme gradient boosting (XGBoost). These models consider various input variables, including the geometric and material properties of CCFST columns, to estimate the compressive strength. The models undergo training and evaluation using two datasets comprising 1004 axially loaded CCFST columns …