Estimating the strength of bi-axially loaded track and channel cold formed composite column using different AI-based symbolic regression techniques.

ABSTRACT: Steel construction is increasingly using thin-walled profiles to achieve lighter, more cost-effective structures. However, analyzing the behavior of these elements becomes very complex due to the combined effects of local buckling in the thin walls and overall global buckling of the entire column. These factors make traditional analytical methods difficult to apply. Hence, in this research work, the strength of bi-axially loaded track and channel cold formed composite column has been estimated by applying three AI-based symbolic regression techniques namely (GP), (EPR) and (GMDH-NN). These techniques were selected because their output models are closed form equations that could be manually used. The methodology began with collecting a 90 records database from previous researches and conduc

SUBMITTER: Ebid AM 

PROVIDER: S-EPMC11339297 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature