Artificial intelligence-based modeling of extruded aluminum beams subjected to patch loading

Artificial intelligence (AI) has become a reliable tool for the solution of structural engineering problems. This paper aims at developing prediction models for the behavior of extruded aluminum beams under patch loading via symbolic regression (SR) and artificial neural networks (ANN). The models are constructed employing an extensive dataset calculated numerically through nonlinear finite element analysis. This dataset covers a wide range of geometric parameters and considers aluminum alloys with different strain hardening characteristics. Explicit formulations for the resistance of extruded aluminum beams to patch loading are developed by fitting this dataset using SR and ANN, the results are compared with those of the resistance model in the EC9:1-1. Finally, the performance of the proposed AI models is also evaluated. Results confirmed the superior accuracy of the proposed models.