Intelligent Predictive Analysis of Lateral Torsional Buckling in Pre-Stressed Thin-Walled Steel Beams with Un-Bonded Deviators Under Non-Uniform Bending

This study presents a newly conducted comprehensive investigation into the lateral torsional buckling (LTB) behavior of un-bonded pre-stressed (PS) thin-walled steel I-beams subjected to non-uniform bending moments, combining a numerical study with a machine learning (ML) approach and experimental validation. Despite extensive prior work, no exact analytical solution exists particularly for non-uniform bending or can be extremely complicated, as the resulting differential equations contain variable coefficients particularly under non-uniform bending due to the complexity of the PS system. To overcome this limitation, a numerical study using finite element (FE) analysis is first conducted with emphasis on the key geometric and pre-stressing parameters, including unbraced beam length, tendon eccentricity, deviators configurations, and pre-stressing force, to evaluate the LTB behavior. The FE modeling was then validated against experimental testing to ensure the accuracy and reliability of the FE solutions. Subsequently, a comprehensive dataset is generated using FE simulations to train the ML models aimed at predicting the LTB resistance of the PS system. This study presents three ML approaches, including support vector regression (SVR), random forest (RF) and least-square boosting (LSBoost), and their optimal hyperparameters are determined using Bayesian optimization (BO) to enhance the prediction performance. The results indicate that the LTB capacity predicted by the Bayesian-optimized ML models achieve high predictive accuracy and are in close agreement with numerical FE simulations, thereby highlighting their potential in capturing the complex, underlying non-linear interactions influencing the buckling behavior of the PS structural system. Accordingly, the proposed framework offers a robust and effective predictive tool for evaluating LTB resistance, particularly under non-uniform bending where exact analytical solutions are not available, and for supporting the design and assessment of PS steel structures.