Park Gwansik, Forman Jason, Kim Taewung, Panzer Matthew B, Crandall Jeff R
a Center for Applied Biomechanics, University of Virginia , Charlottesville , Virginia.
Traffic Inj Prev. 2018 Feb 28;19(sup1):S59-S64. doi: 10.1080/15389588.2017.1398402.
The goal of this study was to explore a framework for developing injury risk functions (IRFs) in a bottom-up approach based on responses of parametrically variable finite element (FE) models representing exemplar populations.
First, a parametric femur modeling tool was developed and validated using a subject-specific (SS)-FE modeling approach. Second, principal component analysis and regression were used to identify parametric geometric descriptors of the human femur and the distribution of those factors for 3 target occupant sizes (5th, 50th, and 95th percentile males). Third, distributions of material parameters of cortical bone were obtained from the literature for 3 target occupant ages (25, 50, and 75 years) using regression analysis. A Monte Carlo method was then implemented to generate populations of FE models of the femur for target occupants, using a parametric femur modeling tool. Simulations were conducted with each of these models under 3-point dynamic bending. Finally, model-based IRFs were developed using logistic regression analysis, based on the moment at fracture observed in the FE simulation. In total, 100 femur FE models incorporating the variation in the population of interest were generated, and 500,000 moments at fracture were observed (applying 5,000 ultimate strains for each synthesized 100 femur FE models) for each target occupant characteristics.
Using the proposed framework on this study, the model-based IRFs for 3 target male occupant sizes (5th, 50th, and 95th percentiles) and ages (25, 50, and 75 years) were developed. The model-based IRF was located in the 95% confidence interval of the test-based IRF for the range of 15 to 70% injury risks. The 95% confidence interval of the developed IRF was almost in line with the mean curve due to a large number of data points.
The framework proposed in this study would be beneficial for developing the IRFs in a bottom-up manner, whose range of variabilities is informed by the population-based FE model responses. Specifically, this method mitigates the uncertainties in applying empirical scaling and may improve IRF fidelity when a limited number of experimental specimens are available.
本研究的目的是探索一种自下而上的框架，用于基于代表典型人群的参数可变有限元（FE）模型的响应来开发损伤风险函数（IRF）。
首先，使用特定个体（SS）-FE建模方法开发并验证了一种参数化股骨建模工具。其次，使用主成分分析和回归来识别人类股骨的参数化几何描述符以及3种目标乘员尺寸（第5、第50和第95百分位数男性）的这些因素的分布。第三，使用回归分析从文献中获取3种目标乘员年龄（25、50和75岁）的皮质骨材料参数分布。然后使用参数化股骨建模工具，通过蒙特卡罗方法生成目标乘员的股骨有限元模型群体。对这些模型中的每一个进行三点动态弯曲模拟。最后，基于有限元模拟中观察到的骨折时刻，使用逻辑回归分析开发基于模型的IRF。总共生成了100个包含感兴趣人群变化的股骨有限元模型，并且针对每个目标乘员特征观察到了500,000个骨折时刻（为每个合成的100个股骨有限元模型应用5,000个极限应变）。
使用本研究中提出的框架，开发了针对3种目标男性乘员尺寸（第5、第50和第95百分位数）和年龄（25、50和75岁）的基于模型的IRF。在15%至70%的损伤风险范围内，基于模型的IRF位于基于测试的IRF的95%置信区间内。由于大量数据点，所开发的IRF的95%置信区间几乎与均值曲线一致。
本研究中提出的框架将有利于以自下而上的方式开发IRF，其变异性范围由基于人群的有限元模型响应提供信息。具体而言，当可用的实验标本数量有限时，这种方法减轻了应用经验缩放时的不确定性，并可能提高IRF的保真度。
