Deep Learning based method for the prediction of the buckling resistance of SHS and RHS

Abstract

AbstractThis paper proposes a data‐driven and deep learning based model for the prediction of the buckling load of square and rectangular hollow sections (SHS and RHS) based on Finite Element Method (FEM) results of the local buckling effects. The data was elaborated during the EU‐funded research project – HOLLOSSTAB (2016‐2019), where a new design method – termed the “Generalized Slenderness‐based Resistance Method” (GSRM) ‐ was developed, aiming to simplify through a unified analytical approach time consuming computational analyses for the prediction of the local resistance of SHS and RHS. This method further generalizes the concepts of established design methods such as the Direct Strength Method. This paper investigates an alternative approach to the development of design rules for this same mechanical problem: the application of deep learning models for the prediction of the buckling load through simple input parameters, such as geometric quantities, material values and applied load to circumvent the application of the analytical formulae. Thereby, a Deep Neural Network (DNN) model is created and calibrated for the prediction of the buckling loads of SHS and RHS based on the training data given by the extensive FEM parametric study. A statistical evaluation in accordance with EN1990 elaborates a partial safety factor for the proposed DNN. The predictions from the DNN are then compared to the numerical results from FEM analyses and to the predictions of the GSRM and EC3. Since the numerical results were originally validated against large scale experimental tests, this newly developed DNN makes use of a very large set of very representative (numerical) tests. A further comparison assesses the computed partial safety factor and the related quantities of interest for the DNN to the respective counterparts of the GSRM method. This work represents one of the first steps for the development of design methods in the field structural design adopting the most recent advances in computer sciences, and future enhancements of the currently developed models are discussed and proposed as a conclusion.