Modeling Structural Elements Subjected to Buckling Using Data Mining and the Finite Element Method
Buckling of thin walled welded structures is one of the most common failure modes experienced by these structures in-service. The study of such buckling, to date, has been concentrated on experimental tests, empirical models and the use of numerical methods such as the Finite Element Method (FEM). Some researchers have combined the FEM with Artificial Neural Networks (ANN) to study both open and closed section structures but these studies have not considered imperfections such as holes, weld seams and residual stresses. In this paper, we have used a combination of FEM and ANN to obtain predictive models for the critical buckling load and lateral displacement of the center of the profile under compressive loading. The study was focused on ordinary Rectangular Hollow Sections (RHS) and on the influence of geometric imperfections while taking residual stresses into consideration.
KeywordsFinite Element Method ANN Buckling Geometric Imperfections
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