New formulas for predicting the lateral–torsional buckling strength of steel I-beams with sinusoidal web openings

Abstract

Lateral torsional buckling (LTB) of steel I-beams is characterized by simultaneous lateral deflection and twist. LTB is an instability failure mode that has been intensively investigated. However, existing standard procedures and formulations still have limitations in determining the LTB ultimate moment, especially when considering the use of perforated beams. Consequently, in the current paper, by conducting an extensive parametric study, it was tried to investigate the effect of all main parameters as well as the effect of different loading conditions on the ultimate LTB resistance of steel I-beams with sinusoidal web openings. Then, based on the provided database, the artificial neural network (ANN) method was employed, and based on it, a high-precision formulation was proposed to predict the ultimate LTB strength of steel I-beams. In addition to the ANN method, a regression-based formula was also developed as a classical method to examine the differences between the two methods. Finally, the proposed formulas were compared with other existing formulas for estimating the LTB strength. The results showed that the proposed formula based on ANN not only present a reasonable accuracy compared to the existing formulations but also can be used by engineers as practical equations in the design of I-beams with sinusoidal web openings.