Lateral torsional capacity of steel beams in different loading conditions by neural network

Abstract

Lateral torsional buckling (LTB) is a common mode of failure in steel structures due to instability. However, current standard recommendations have limitations in accurately determining the ultimate capacity of members subjected to LTB. To address this issue, an in-depth parametric study using finite-element analysis (FEA) was conducted to investigate the effects of major parameters, including various types of loading, on the strength of steel I-beams. Additionally, the artificial neural network (ANN) technique was used to find a reliable procedure for assessing the LTB strength of steel I-beams using a generated database. To demonstrate the efficacy of the developed formulation, it was compared against predictions using existing equations. The presented formula demonstrated strong accuracy, making it an effective tool for engineers designing I-beams to resist LTB. This research makes significant contributions to the structural engineering field and has important implications for the creation and evaluation of steel structures.