Thermo-Mechanical Stress Prediction in Steel IPE Profiles under Asymmetric Thermal Loading: A Finite Element and XGBoost-Based Approach

• A combined FEM–XGBoost framework predicts stress parameters in steel IPE profiles subjected to thermal conditions. • Integrates FEM data with ML-based XGBoost method to minimize redundant simulations. • Generalizes well across various beam dimensions and thermal loads with high prediction precision. • Provides rapid, scalable stress evaluation, especially in enhancing structural dependability for the energy and infrastructure domains. Accurate prediction of thermally induced stresses in structural members remains a significant challenge in engineering, especially under complex real-world conditions. Traditional analytical and numerical methods, while robust, often struggle to capture the complicated relationship between uneven thermal loads and structural responses without significant computational effort. This study investigates the effect of asymmetric thermal loading on standard steel IPE profiles, which are widely employed in buildings and structures. These members, often exposed partially to outdoor conditions, experience uneven temperature distributions across their cross-sections, resulting in complex internal stress patterns. To simulate such scenarios, a range of thermal conditions is applied to beams and columns with varying geometries using the Finite Element Method (FEM) numerical analysis. The resulting stress components, including von Mises, axial, and shear stresses, are analyzed in detail. This study introduces a mixed approach that integrates FEM with eXtreme Gradient Boosting (XGBoost) to forecast thermal stresses in steel IPE profiles subjected to asymmetrical temperature gradients. The suggested technique, in contrast to traditional assessments that emphasize uniform heating, accounts for the interrelated impacts of irregular thermal exposures and geometric variations among IPE sections. The FEM database enabled the training of an improved XGBoost model that achieved exceptional accuracy (R² > 0.98) in predicting multiple stress components. The results highlight the critical role of cross-sectional geometry in stress development under thermal gradients and underscore the effectiveness of machine learning techniques in forecasting structural responses. This integration offers a quick, adaptable method for assessing thermal impacts in steel IPE structures, with considerable promise for design and real-time structural evaluation in industrial settings. This approach offers substantial benefits to the petroleum and broader oil and gas sectors, particularly in enhancing structural dependability under thermal and mechanical stresses.