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Citation |
Han Y, Xu J, Thomson R, Mizuno K. Forensic Sci. Int. 2020; 309: e110195. |
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Affiliation |
Department of Mechanical Systems Engineering, Nagoya University, Nagoya, Japan. |
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Copyright |
(Copyright © 2020, Elsevier Publishing) |
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DOI |
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PMID |
32120191 |
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Abstract |
Traditional vehicle accident reconstructions do not take into account all existing uncertainties and may over- or under-estimate the injury risk. The objective of this study was to introduce a new uncertainty analysis method by applying Response Surface-Monte Carlo Methods (RS-MCM) to predict head injury risk in real electric two wheelers (ETW) to vehicle accidents. Vehicle impact velocity ranges in three detailed ETWs accidents (including video records and injury reports) were estimated using direct linear transformation (DLT) or video frame (VF) methods. A response surface methodology (RSM) was used to obtain an approximate model of the each real ETW accident, and a vehicle impact velocity distribution was estimated by applying the Monte Carlo Method (MCM) to the resulting model. If the velocity distribution was in agreement with the initial estimated velocity, the reconstruction quality was deemed acceptable. The injury severity was then assessed using the initial conditions resulting from the range of potential head impact conditions identified in the reconstruction activities. The identified head linear and angular impact velocities were input to finite element analyses to the THUMS Ver4.02 pedestrian head model and resulting in head injury criteria (HIC). The HIC values were further explored using the same RSM method used earlier to establish impact conditions. The distribution of reconstructed AIS levels show good agreement with the injury results from forensic reports. The results illustrated that the RS-MCM enriches the information for head trauma injury mechanisms caused by the vehicle collisions or ground impact. Language: en |
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Keywords |
Accident reconstruction; Finite element method; Head injury severity; Response Surface-Monte Carlo Method; Uncertainty analysis |