Uncertainty Analysis of Head Injury via Reconstruction of Electric Two-wheeler Accidents

被引：0
|
作者：
Han Y. [1 ,2 ]
Xu J.-S. [1 ]
Shi L.-L. [3 ]
Gao X.-J. [1 ,2 ]
Qian Y.-B. [4 ]
Yang Z. [5 ]
机构：
[1] Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, 361024, Fujian
[2] Fujian Collaborative Innovation Center for R & D of Coach and Special Vehicle, Xiamen University of Technology, Xiamen, 361024, Fujian
[3] School of Aerospace Engineering, Xiamen University, Xiamen, 361005, Fujian
[4] School of Automotive Engineering, Shanghai University of Engineering Science, Shanghai
[5] Key Laboratory of Automobile Safety Technology of Zhejiang Province, Ningbo, 315336, Zhejiang
来源：
Zhongguo Gonglu Xuebao/China Journal of Highway and Transport | 2020年 / 33卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Automotive engineering; Electric two-wheeler accidents; Experimental design; Head injuries; Monte Carlo methods; Uncertainty analysis;
D O I：
10.19721/j.cnki.1001-7372.2020.01.018
中图分类号：
学科分类号：
摘要：
Uncertain information has a negative influence on the accuracy of injury reconstruction of vulnerability in an accident. This study combined Latin hypercube sampling (LHS) design with a response surface Monte Carlo method to address head injury uncertainties. Two electric two-wheeler (ETW) accidents were reconstructed using a multi-body system, in which accident collision information (video information, final position, and kinematics of riders) was compared with simulation results to verify the effectiveness of the accident reconstruction. The head impact conditions of the riders were used as the boundary conditions in each injury reconstruction by using finite element method. Then, the total human model for safety (THUMS) finite element model of the pedestrian head was adopted in the analysis. The parameters of the riders' head injuries were analyzed, and their distributions on the cumulative frequency abbreviated injury scale (AIS), as predicted by the simulations, were compared with the head injury forensic identification records. Results show that Monte Carlo uncertainty analysis can accurately predict vehicle collision velocity in ETW accidents. In addition, the head injury levels obtained by uncertainty analysis are highly consistent with the forensic brain injury records. The research clearly indicates that Monte Carlo uncertainty analysis can be used to predict the level of a rider's head injury and can provide a theoretical basis and an empirical approach to investigate the head injuries of riders in ETW accidents. © 2020, Editorial Department of China Journal of Highway and Transport. All right reserved.
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页码：172 / 180and190
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