RSM and ANN modeling of the energy absorption behavior of steel thin-walled columns: a multi-objective optimization using the genetic algorithm

Abstract

In many industries, it is necessary to use structures that exhibit a proper stability against the design loads and depreciate the energy in a controlled manner. In this study, the energy absorption characteristics of thin-walled structures with rectangular cross sections are investigated under the quasi-static loading. The section of structures has a different aspect ratio, and in all of them, an elliptical cutout with a different diameter ratio exists on the larger side. In all instances, the area of the cross section and cutout is constant. Hence, an experimental design with two design parameters consisting of the shell aspect ratio and the diameter ratio of the cutout was conducted by applying the central composite design method. Energy absorption parameters were modeled using the artificial neural network and the response surface method. A systematic crashworthiness study was carried out with a multi-objective optimization design using the genetic algorithm. The results showed that the optimal amount of the specific energy absorption was 14.48 kJ/kg and the optimal amount of the peak crushing load was 37.77 kN which was obtained in the aspect ratio of 1 and the diameter ratio of 0.7. The validity of the results was confirmed by empirical experiments.

Multiobjective optimization of dimension and position of elliptical crush initiator on crashworthiness performance of square tube using response surface methodology

“…This study confirms the above findings regarding this statement. However, contrasting findings were obtained by Dadrasi et al (2020), in which the height of the elliptical cutout had a more significant influence than its width. This discrepancy may be attributed to the fact that they only placed a single cutout on one side of the tube, whereas in this study, the cutout was positioned on all four sides of the square tube.…”

“…frontiersin.org 04 model is significant. The key aspect of the RSM is the derivation of various mathematical formulas that express the relationship between the response and input variables (Dadrasi et al, 2020). This relationship is expressed as a quadratic equation (Eq.…”