Vehicle crash accident reconstruction based on the analysis 3D deformation of the auto-body

Authors:
Xiao-yun Zhang;Xian-long Jin;Wen-guo Qi;Yi-zhi Guo
Affiliations:
Department of Mechanical Engineering, Shanghai Jiao Tong University, Room 909, No. 800 Dongchuan Road, Shanghai 200240, PR China;Department of Mechanical Engineering, Shanghai Jiao Tong University, Room 909, No. 800 Dongchuan Road, Shanghai 200240, PR China;Department of Mechanical Engineering, Shanghai Jiao Tong University, Room 909, No. 800 Dongchuan Road, Shanghai 200240, PR China;Department of Mechanical Engineering, Shanghai Jiao Tong University, Room 909, No. 800 Dongchuan Road, Shanghai 200240, PR China
Venue:
Advances in Engineering Software
Year:
2008

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Abstract

The objective of vehicle crash accident reconstruction is to investigate the pre-impact velocity. Elastic-plastic deformation of the vehicle and the collision objects are the important information produced during vehicle crash accidents, and the information can be fully utilized based on the finite element method (FEM), which has been widely used as simulation tools for crashworthiness analyses and structural optimization design. However, the FEM is not becoming popular in accident reconstruction because it needs lots of crash simulation cycles and the FE models are getting bigger, which increases the simulation time and cost. The use of neural networks as global approximation tool in accident reconstruction is here investigated. Neural networks are used to map the relation between the initial crash parameter and deformation, which can reduce the simulation cycles apparently. The inputs and outputs of the artificial neural networks (ANN) for the training process are obtained by explicit finite element analyses performed by LS-DYNA. The procedure is applied to a typical traffic accident as a validation. The deformation of the key points on the frontal longitudinal beam and the mudguard could be measured according to the simulation results. These results could be used to train the neural networks adapted back-propagation learning rule. The pre-impact velocity could be got by the trained neural networks, which can provide a scientific foundation for accident judgments and can be used for vehicle accidents without tire marks.