Biljana Šćepanović, Snežana Rutešić, Olga Mijušković, Luisa María Gil-Martín, Enrique Hernández-Montes

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Majority of eccentrically patch loaded girders collapse at lower load level than their geometrical equivalents loaded in the web plane, due to different collapse mechanism. However, some eccentrically patch loaded girders, even with significant load eccentricity, behave as if loaded in the web plane, having the same collapse mode and ultimate load as in the case of centric load. Hence, the ultimate strength of eccentrically patch loaded steel I-girders should be found out in two phases: firstly, girder collapse mode should be estimated; secondly, depending on expected collapse mode, ultimate load should be calculated appropriately. Both tasks are demanding, with plenty of mutually dependent influential parameters. Artificial neural network (ANN) modelling, being suitable for multi-parameter analysis, is quite convenient method for studying eccentrically patch loaded steel I-girders in both mentioned tasks, as confirmed through the examples elaborated in the paper. Not only that it is valuable on its own, as a standalone technique, but also in combination with and as support to other methods (hybrid modelling).

In comparison of five procedures for ultimate load determination (empirical expression, mechanical model, two versions of refined mechanical model, one of them by ANN, and standalone ANN forecast model), ANN modelling, providing high quality results, qualified in top-two procedures for ultimate load, regardless of collapse mode type. It is also proved that certain shortages of mechanical model may be overcome by its coupling with ANN modelling. Such hybrid modelling provided remarkably more accurate results than original mechanical model.

Study confirmed need of revision of mechanical model. As the first step, new, more demanding constraints of mechanical model validity are proposed in this paper.


Eccentric patch load, Collapse mode, Ultimate load, Artificial neural network, Hybrid modelling

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