Bailey Ann M, McMurry Timothy L, Poplin Gerald S, Salzar Robert S, Crandall Jeff R
a Department of Mechanical and Aerospace Engineering , University of Virginia, Center for Applied Biomechanics , Charlottesville , Virginia.
b School of Medicine, University of Virginia , Department of Public Health Sciences , Charlottesville , Virginia.
Traffic Inj Prev. 2015;16 Suppl 2:S96-S102. doi: 10.1080/15389588.2015.1061185.
Understanding how lower extremity injuries from automotive intrusion and underbody blast (UBB) differ is of key importance when determining whether automotive injury criteria can be applied to blast rate scenarios. This article provides a review of existing injury risk analyses and outlines an approach to improve injury prediction for an expanded range of loading rates. This analysis will address issues with existing injury risk functions including inaccuracies due to inertial and potential viscous resistance at higher loading rates.
This survival analysis attempts to minimize these errors by considering injury location statistics and a predictor variable selection process dependent upon failure mechanisms of bone. Distribution of foot/ankle/leg injuries induced by axial impact loading at rates characteristic of UBB as well as automotive intrusion was studied and calcaneus injuries were found to be the most common injury; thus, footplate force was chosen as the main predictor variable because of its proximity to injury location to prevent inaccuracies associated with inertial differences due to loading rate. A survival analysis was then performed with age, sex, dorsiflexion angle, and mass as covariates. This statistical analysis uses data from previous axial postmortem human surrogate (PMHS) component leg tests to provide perspectives on how proximal boundary conditions and loading rate affect injury probability in the foot/ankle/leg (n = 82).
Tibia force-at-fracture proved to be up to 20% inaccurate in previous analyses because of viscous resistance and inertial effects within the data set used, suggesting that previous injury criteria are accurate only for specific rates of loading and boundary conditions. The statistical model presented in this article predicts 50% probability of injury for a plantar force of 10.2 kN for a 50th percentile male with a neutral ankle position. Force rate was found to be an insignificant covariate because of the limited range of loading rate differences within the data set; however, compensation for inertial effects caused by measuring the force-at-fracture in a location closer to expected injury location improved the model's predictive capabilities for the entire data set.
This study provides better injury prediction capabilities for both automotive and blast rates because of reduced sensitivity to inertial effects and tibia-fibula load sharing. Further, a framework is provided for future injury criteria generation for high rate loading scenarios. This analysis also suggests key improvements to be made to existing anthropomorphic test device (ATD) lower extremities to provide accurate injury prediction for high rate applications such as UBB.
在确定汽车伤害标准是否可应用于爆炸率场景时，了解汽车侵入和车底爆炸（UBB）导致的下肢损伤有何不同至关重要。本文回顾了现有的伤害风险分析，并概述了一种方法，以改进对更广泛加载率范围内的伤害预测。该分析将解决现有伤害风险函数存在的问题，包括在较高加载率下由于惯性和潜在粘性阻力导致的不准确问题。
本生存分析试图通过考虑伤害位置统计数据以及依赖于骨骼失效机制的预测变量选择过程来最小化这些误差。研究了在UBB以及汽车侵入特征加载率下轴向冲击载荷引起的足/踝/腿损伤分布，发现跟骨损伤是最常见的损伤；因此，由于足板力与伤害位置接近，可防止因加载率导致的惯性差异相关的不准确问题，所以选择足板力作为主要预测变量。然后以年龄、性别、背屈角度和体重作为协变量进行生存分析。此统计分析使用先前轴向尸体人类模拟物（PMHS）组件腿部测试的数据，以提供关于近端边界条件和加载率如何影响足/踝/腿损伤概率的观点（n = 82）。
由于所使用数据集中的粘性阻力和惯性效应，在先前分析中胫骨骨折时的力被证明高达20%不准确，这表明先前的伤害标准仅在特定加载率和边界条件下才准确。本文提出的统计模型预测，对于踝关节中立位的第50百分位男性，足底力为10.2 kN时受伤概率为50%。由于数据集中加载率差异范围有限，发现力率是一个不显著的协变量；然而，通过在更接近预期伤害位置测量骨折时的力来补偿惯性效应，提高了模型对整个数据集的预测能力。
由于对惯性效应和胫腓骨负荷分担的敏感性降低，本研究为汽车和爆炸率提供了更好的伤害预测能力。此外，为未来高速加载场景的伤害标准生成提供了一个框架。该分析还表明，需要对现有的人体模型试验装置（ATD）下肢进行关键改进，以针对UBB等高加载率应用提供准确的伤害预测。
