Virginia Tech-Wake Forest University Center for Injury Biomechanics, Medical Center Boulevard, Winston-Salem, NC 27157, USA; Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157, USA.
Accid Anal Prev. 2014 Mar;64:1-8. doi: 10.1016/j.aap.2013.10.026. Epub 2013 Oct 30.
Improved understanding of the occupant loading conditions in real world crashes is critical for injury prevention and new vehicle design. The purpose of this study was to develop a robust methodology to reconstruct injuries sustained in real world crashes using vehicle and human body finite element models.
A real world near-side impact crash was selected from the Crash Injury Research and Engineering Network (CIREN) database. An average sedan was struck at approximately the B-pillar with a 290 degree principal direction of force by a lightweight pickup truck, resulting in a maximum crush of 45 cm and a crash reconstruction derived Delta-V of 28 kph. The belted 73-year-old midsized female driver sustained severe thoracic injuries, serious brain injuries, moderate abdominal injuries, and no pelvic injury. Vehicle finite element models were selected to reconstruct the crash. The bullet vehicle parameters were heuristically optimized to match the crush profile of the simulated struck vehicle and the case vehicle. The Total Human Model for Safety (THUMS) midsized male finite element model of the human body was used to represent the case occupant and reconstruct her injuries using the head injury criterion (HIC), half deflection, thoracic trauma index (TTI), and pelvic force to predict injury risk. A variation study was conducted to evaluate the robustness of the injury predictions by varying the bullet vehicle parameters.
The THUMS thoracic injury metrics resulted in a calculated risk exceeding 90% for AIS3+ injuries and 70% risk of AIS4+ injuries, consistent with her thoracic injury outcome. The THUMS model predicted seven rib fractures compared to the case occupant's 11 rib fractures, which are both AIS3 injuries. The pelvic injury risk for AIS2+ and AIS3+ injuries were 37% and 2.6%, respectively, consistent with the absence of pelvic injury. The THUMS injury prediction metrics were most sensitive to bullet vehicle location. The maximum 95% confidence interval width for the mean injury metrics was only 5% demonstrating high confidence in the THUMS injury prediction.
This study demonstrates a variation study methodology in which human body models can be reliably used to robustly predict injury probability consistent with real world crash injury outcome.
提高对现实世界碰撞中乘员加载条件的理解对于预防伤害和新车辆设计至关重要。本研究的目的是开发一种强大的方法，使用车辆和人体有限元模型来重建现实世界碰撞中遭受的伤害。
从 Crash Injury Research and Engineering Network（CIREN）数据库中选择了一起真实的侧面碰撞事故。一辆普通轿车在 B 柱附近被一辆轻型皮卡车以大约 290 度的主方向力撞击，导致最大压缩 45 厘米，碰撞重建得出的 Delta-V 为 28 公里/小时。系安全带的 73 岁中老年女性司机遭受严重的胸部损伤、严重的脑损伤、中度腹部损伤和无骨盆损伤。选择车辆有限元模型来重建碰撞。通过启发式优化子弹车参数，使模拟撞击车和案例车的压缩轮廓相匹配。使用 Total Human Model for Safety（THUMS）中型男性人体有限元模型来代表案例乘员，并使用头部损伤标准（HIC）、半挠度、胸部创伤指数（TTI）和骨盆力来重建她的损伤，以预测受伤风险。进行了变异性研究，通过改变子弹车参数来评估损伤预测的稳健性。
THUMS 胸部损伤指标导致 AIS3+损伤的计算风险超过 90%，AIS4+损伤的风险为 70%，与她的胸部损伤结果一致。THUMS 模型预测了 7 处肋骨骨折，而案例乘员有 11 处肋骨骨折，均为 AIS3 损伤。骨盆损伤 AIS2+和 AIS3+的风险分别为 37%和 2.6%，与骨盆无损伤一致。THUMS 损伤预测指标对子弹车位置最敏感。平均损伤指标的 95%置信区间宽度最大仅为 5%，表明 THUMS 损伤预测具有很高的置信度。
本研究展示了一种变异性研究方法，其中人体模型可以可靠地用于稳健地预测与现实世界碰撞损伤结果一致的受伤概率。
