Kurtoğlu, Ahmet Emin2024-03-182024-03-1820181229-93671598-6233https://hdl.handle.net/11363/7216https://doi.org/Steel girders are the structural members often used for passing long spans. Mostly being subjected to patch loading, or concentrated loading, steel girders are likely to face sudden deformation or damage e.g., web breathing. Horizontal or vertical stiffeners are employed to overcome this phenomenon. This study aims at assessing the feasibility of a machine learning method, namely the support vector machines (SVM) in predicting the patch loading resistance of longitudinally stiffened webs. A database consisting of 162 test data is utilized to develop SVM models and the model with best performance is selected for further inspection. Existing formulations proposed by other researchers are also investigated for comparison. BS5400 and other existing models (model I, model II and model III) appear to yield underestimated predictions with a large scatter; i.e., mean experimental-to-predicted ratios of 1.517, 1.092, 1.155 and 1.256, respectively; whereas the selected SVM model has high prediction accuracy with significantly less scatter. Robust nature and accurate predictions of SVM confirms its feasibility of potential use in solving complex engineering problems.eninfo:eu-repo/semantics/openAccesssteel girderspatch loadinglongitudinal stiffenersupport vector machinesmachine learningArticle29330931810.12989/scs.2018.29.3.3092-s2.0-85056883760Q1WOS:000449622300003Q1