Experimental Data-Based Machine Learning Prediction of Crashworthiness Parameters and Energy Absorption

Nejati Ajbisheh, Iman; Rezapour, Javad; Gohari Rad, Sina

doi:10.22068/ase.2026.745

Volume 16, Issue 1 (3-2026) ASE 2026, 16(1): 4980-4999 | Back to browse issues page

‎ 10.22068/ase.2026.745

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Nejati Ajbisheh I, Rezapour J, Gohari Rad S. Experimental Data-Based Machine Learning Prediction of Crashworthiness Parameters and Energy Absorption. ASE 2026; 16 (1) :4980-4999
URL: http://ase.iust.ac.ir/article-1-745-en.html

Experimental Data-Based Machine Learning Prediction of Crashworthiness Parameters and Energy Absorption

Iman Nejati Ajbisheh

, Javad Rezapour

, Sina Gohari Rad

Department of Mechanical Engineering, La.C, Islamic Azad University, Lahijan, Iran

Abstract: (267 Views)

Thin-walled tubes are effective crashworthiness structures that absorb energy under axial loading, while preventing the transmission of injurious acceleration and excessive forces to the protected section and thereby reducing damage severity. This study presents a high-accuracy artificial neural network (ANN) framework for predicting the dynamic response and impact resistance of thin-walled steel tubes under high-velocity axial impacts. The model was developed using a hybrid dataset comprising 300 experimental impact tests and 4000 finite element (FE) simulations, with systematic variations in tube geometry and impact conditions. After parameter optimization, the final model consisted of a 24-layer network with 180 neurons per layer and achieved high accuracy (R-value above 0.985). Error assessments across the four physical criteria (PFE, MFE, AEE, and SHE) show that the ANN predicts peak force, mean force, absorbed energy, and shortening with average errors generally below 10%, demonstrating strong predictive capability. Overall, the model not only offers a much faster alternative to FE simulations but also accurately reproduces oscillatory behavior and deformation progression, making it a reliable tool for impact response prediction.

Keywords: Artificial neural network, Machine learning, Crashworthiness, Force-time response, high velocity impact

Full-Text [PDF 1626 kb] (91 Downloads)

Type of Study: Research | Subject: Body structure