Estimation of critical force of the buckling composite structures using modelling methods

Abstract

The study reported in this paper employed Artificial Neural Networks (ANN) to predict the critical force of the buckling composite structures. The critical force depends upon various factors such as thickness, stacking sequence, etc. These factors have been identified in earlier studies by means of the Finite Elements Method (FEM). The critical force is affected by the above-mentioned factors. Various approaches have been applied in the course of the presented study. Apart from our FEM simulation, the ANN approach has been applied and the results were compared. The main contribution of these two approaches is the estimation of the critical force. The ANN model is trained to predict the critical force for different configurations of input variables.

Modelling hollow pultruded FRP profiles under axial compression: Local buckling and progressive failure

“…The captured failure mode ranged from buckling bulge for a ratio of 1 to local buckling for a ratio of 5. Moreover, the linear perturbation procedure in Abaqus was used to simulate the local buckling of hollow box and channel-section Fibre-Reinforced Polymer (FRP) short columns and beams through an eigenvalue buckling problem [36][37][38][39][40][41][42]. However, in all these studies, the full load-displacement path and progressive failure were not simulated due to limitations in the analysis method utilised.…”