Prediction of the axial compression capacity of stub CFST columns using machine learning techniques.

ABSTRACT: Concrete-filled steel tubular (CFST) columns have extensive applications in structural engineering due to their exceptional load-bearing capability and ductility. However, existing design code standards often yield different design capacities for the same column properties, introducing uncertainty for engineering designers. Moreover, conventional regression analysis fails to accurately predict the intricate relationship between column properties and compressive strength. To address these issues, this study proposes the use of two machine learning (ML) models-Gaussian process regression (GPR) and symbolic regression (SR). These models accept a variety of input variables, encompassing geometric and material properties of stub CFST columns, to estimate their strength. An experimental database

SUBMITTER: Megahed K 

PROVIDER: S-EPMC10838919 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature