The torsional stiffness of I-beams with sinusoidal corrugated web is higher than that of flat web beams and the accuracy of the available hand-calculation methods to determine the elastic critical lateral-torsional buckling moment depends on the geometrical parameters of the beam and the web corrugation. This study proposes different machine learning models to determine the elastic lateral-torsional buckling moments of corrugated web beams. Various machine-learning algorithms such as Decision Tree, Random Forests, Gradient Boosting, Support Vector Regression, Catboost, and Deep Neural Network were employed to develop and train for predicting the elastic-critical lateral-torsional buckling moments of I-beams with corrugated web. An extensive dataset with 2250 pieces was constructed using linear buckling analyses on full-shell finite element models to determine the elastic-critical buckling moment of simply supported beams with sinusoidal web corrugation. Based on the statistical parameters of the predicted and test data, the accuracy and safety assessment of the different machine learning models are examined. The accuracy of the available hand-calculation methods is also investigated. The results of the parametric study showed that the overall performance of the different machine learning models is promising, although, not all are directly suited for the described problem.

I-beams with corrugated webs have higher torsional stiffness than that of flat web beams. Furthermore, the geometrical dimensions of the beam and the web corrugation heavily influence the precision of the currently used traditional pen-andpaper methods for determining the elastic lateral-torsional buckling moment. This study aims to suggest several machine learning models with the intention of predicting the elastic lateral-torsional buckling moment of corrugated web beams. Multiple machine learning models, including Random Forests, Gradient Boosting, Categorical Boosting, and Deep Neural Networks, were deployed to develop and train models to predict the elastic critical lateral-torsional buckling moments of Ibeams with corrugated web. The database used for training the different models was compiled through linear bifurcation analyses conducted on shell finite element models. The study evaluates the precision of the various machine learning models by examining their performance against statistical parameters derived from both predicted and test data. The findings from the parametric evaluation highlight the surprisingly high performance and accuracy of the machine learning models.