To design a more efficient energy absorber, it is critical to evaluate how changing the design parameters affects its performance, and also determine each one’s order of significance. In this paper, using a new approach, the behavior and response of straight, double-tapered, and triple-tapered thin-walled tubes with rectangular cross sections under axial and dynamic loading are investigated by performing a sensitivity analysis on a support vector machine (SVM) as a surrogate machine learning model. First, a finite element model of the energy absorber is constructed and validated with available experimental and theoretical studies. Next, a design of experiments was developed using the Sobol series sampling method and an appropriate dataset was created. This information is then used to develop an SVM model to predict the initial peak load and mean load of tubes. The accuracy of the machine learning created in this study is then assessed, and it is demonstrated that the developed model can precisely predict the performance of the absorber. The machine learning model is then subjected to a Sobol sensitivity analysis, and the outcomes are compared to those of the parametric study. The results suggest that the thickness of the tube has a stronger effect on the absorber performance than other geometric parameters. Comparing the effects of different material parameters on the behavior of tubes, the results show that yield strength has the greatest impact on the response of the energy absorber. It is also observed that the tapered tubes have a much lower initial peak load compared to straight ones.

This paper studies the behavior and response of triple thin-walled tubes with rectangular cross-sections under axial and dynamic loading. First, a finite element model of the energy absorber is prepared, and the results are validated with available theoretical and experimental studies. Then the effect of different input parameters such as tube-thickness, cross-sectional ratio, slope angle, and material parameters on the performance of thin-walled energy absorbers is studied.The simulation results show that the thickness of the tube has a more significant effect on the absorber’s performance than other geometric parameters. The results also show that changing the cross-sectional ratio and the inclination angle of the tube changes the initial peak load more than the average load of the absorber. Comparing the effects of different materials on the performance of absorbers, the results show that steel alloys record the highest average loads and initial peak loads, followed by titanium alloys and then aluminum alloys. This information is then used to develop a machine learning model to predict the performance of the absorbers. Then, the performance of the machine learning developed in this study is evaluated, and it is shown that the developed machine learning can accurately predict the absorber’s performance. Finally, a Sobol sensitivity analysis is performed on the machine-learned model and the results are compared with those of parametric study.