Application of machine learning models in the capacity prediction of RCFST columns.

ABSTRACT: Rectangular concrete-filled steel tubular (RCFST) columns are widely used in structural engineering due to their excellent load-carrying capacity and ductility. However, existing design equations often yield different design results for the same column properties, leading to uncertainty for engineering designers. Furthermore, basic regression analysis fails to precisely forecast the complicated relation between the column properties and its compressive strength. To overcome these challenges, this study suggests two machine learning (ML) models, including the Gaussian process (GPR) and the extreme gradient boosting model (XGBoost). These models employ a range of input variables, such as the geometric and material properties of RCFST columns, to estimate their strength. The models are traine

SUBMITTER: Megahed K 

PROVIDER: S-EPMC10682462 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature