FORECAST OF COLLAPSE MODE IN ECCENTRICALLY PATCH LOADED STEEL I-GIRDERS BY MEANS OF ARTIFICIAL NEURAL NETWORKS

Particularly intriguing problem in structural engineering practice is eccentric patch loading-thin-walled steel I-girder loaded over flange by local load having a certain eccentricity regarding the web plane. Depending on girder geometry and load eccentricity, eccentrically patch loaded girders might have collapse mode and collapse load the same as centrically loaded girders (i.e. as if there is no eccentricity) or completely different collapse mode than in case of centric load, with reduced collapse load due to load eccentricity. It is essential to know collapse mode of eccentrically loaded girder in order to determine its collapse load. Patch loading research projects at the University of Montenegro, among extensive experimental research and different theoretical research ways, included application of artificial neural networks (ANN) for solving simultaneous problems of determination of collapse mode and collapse load in eccentrically locally loaded steel I-girders. Presented example of ANN modelling proves possibility of successful and useful application of this method in engineering practice. Not only that such models might be used to forecast collapse mode of particular girders. They might also help in establishing general criteria for collapse mode identification in eccentrically patch loaded girders