Proposed numerical and machine learning models for fiber-reinforced polymer concrete-steel hollow and solid elliptical columns

Abstract

This study employs a hybrid approach, integrating finite element method (FEM) simulations with machine learning (ML) techniques to investigate the structural performance of double-skin tubular columns (DSTCs) reinforced with glass fiber-reinforced polymer (GFRP). The investigation involves a comprehensive examination of critical parameters, including aspect ratio, concrete strength, number of GFRP confinement layers, and dimensions of steel tubes used in DSTCs, through comparative analyses and parametric studies. To ensure the credibility of the findings, the results are rigorously validated against experimental data, establishing the precision and trustworthiness of the analysis. The present research work examines the use of the columns with elliptical cross-sections and contributes valuable insights into the application of FEM and ML in the design and evaluation of structural systems within the field of structural engineering.