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This study presents an innovative inverse machine learning methodology for predicting the design parameters of perforated steel beams with elliptically-based openings, focusing on web-post buckling resistance. The approach utilizes five advanced supervised machine learning models to directly map input geometric and resistance parameters to essential beam properties, achieving high predictive accuracy. A user-friendly web tool has been developed to facilitate instant predictions, demonstrating the practical

Author

madhup.mbm

Language

EN