modeling the patch load resistance of plate girders using a support vector machine

the support vector machine (svm) is a relatively new machine learning method which is increasingly being applied to engineering problems and have yielded encouraging results. because of complex behavior of elastoplastic of web panels of plate girders under patch loading, almost none of the proposed methods provides consistent and accurate predictions of patch load capacity. consequently, alternative solutions are required to overcome these limitations. in this paper svm models are developed for predicting the ultimate resistance of plate girders subjected to patch loading. the training and testing patterns of the proposed svm models are based on well established experimental results taken from literature. finally a comparison is made between predictions obtained from the svm models and a traditional method for determining patch loading resistance. the comparison confirms that the svm models developed in this paper, outperform the traditional method.