TY - JOUR
T1 - Prediction of pedestrian brain injury due to vehicle impact using computational biomechanics models
T2 - Are head-only models sufficient?
AU - Wang, Fang
AU - Yu, Chao
AU - Wang, Bingyu
AU - Li, Guibing
AU - Miller, Karol
AU - Wittek, Adam
PY - 2020/1/2
Y1 - 2020/1/2
N2 - Objectives: Accident reconstruction using computational biomechanics models plays an important role in research and prevention of human brain injury caused by car-to-pedestrian impacts. Finite element (FE) “head-only” models (that represent only the pedestrian head and brain) used in such reconstruction do not account for the influence of the rest of the pedestrian body on the head kinematics due to the accident and, consequently, on the brain injury risk prediction. Application of full-scale FE pedestrian models, on the other hand, is limited by their high computational cost and, more importantly, by the time-consuming preprocessing when repositioning the model to represent the pedestrian posture and location in relation to the impacting car. The objective of this study is to propose a computational biomechanics modeling approach to overcome these challenges. Methods: First, we couple a validated commercial FE head-neck complex model and a multibody (MB) pedestrian model. This coupled FE-MB model is evaluated through application in reconstruction of a real-world car-to-pedestrian impact accident and comparison of the pedestrian kinematics predicted using this model with the results obtained from the established full-scale MB pedestrian model. Finally, we compare the results obtained using the coupled FE-MB model proposed in this study and FE head-only model in terms of both the head kinematics and brain injury risk predicted using the two models. Results: The results of analysis of head injury criterion (HIC15) and brain deformation-based injury criteria (instantaneous value of cumulative strain damage measure iCSDM and maximum shear strain of the brain tissue) indicate substantial differences in the head kinematics and brain injury risk predicted using the two models. The coupled FE-MB model predicts a high risk of the brain injury which is consistent with the database record of the analyzed accident, in particular for the impact between the pedestrian head and road surface. In contrast, the head-only model did not predict that such impact can occur. The FE head head-only model with rigid skull and prescribed acceleration–time history of the head center of gravity determined from accident reconstruction using the purely MB pedestrian model, predicted appreciably lower iCSDM than the coupled FE-MB model that accounts for skull deformations using the linear elastic-plastic material model. Conclusions: This study suggests that the FE head-only models may be deficient for car-to-pedestrian impact accident reconstruction and estimation of risk of the pedestrian brain injury. In particular, this applies to the models that simplify the pedestrian skull as a rigid body.
AB - Objectives: Accident reconstruction using computational biomechanics models plays an important role in research and prevention of human brain injury caused by car-to-pedestrian impacts. Finite element (FE) “head-only” models (that represent only the pedestrian head and brain) used in such reconstruction do not account for the influence of the rest of the pedestrian body on the head kinematics due to the accident and, consequently, on the brain injury risk prediction. Application of full-scale FE pedestrian models, on the other hand, is limited by their high computational cost and, more importantly, by the time-consuming preprocessing when repositioning the model to represent the pedestrian posture and location in relation to the impacting car. The objective of this study is to propose a computational biomechanics modeling approach to overcome these challenges. Methods: First, we couple a validated commercial FE head-neck complex model and a multibody (MB) pedestrian model. This coupled FE-MB model is evaluated through application in reconstruction of a real-world car-to-pedestrian impact accident and comparison of the pedestrian kinematics predicted using this model with the results obtained from the established full-scale MB pedestrian model. Finally, we compare the results obtained using the coupled FE-MB model proposed in this study and FE head-only model in terms of both the head kinematics and brain injury risk predicted using the two models. Results: The results of analysis of head injury criterion (HIC15) and brain deformation-based injury criteria (instantaneous value of cumulative strain damage measure iCSDM and maximum shear strain of the brain tissue) indicate substantial differences in the head kinematics and brain injury risk predicted using the two models. The coupled FE-MB model predicts a high risk of the brain injury which is consistent with the database record of the analyzed accident, in particular for the impact between the pedestrian head and road surface. In contrast, the head-only model did not predict that such impact can occur. The FE head head-only model with rigid skull and prescribed acceleration–time history of the head center of gravity determined from accident reconstruction using the purely MB pedestrian model, predicted appreciably lower iCSDM than the coupled FE-MB model that accounts for skull deformations using the linear elastic-plastic material model. Conclusions: This study suggests that the FE head-only models may be deficient for car-to-pedestrian impact accident reconstruction and estimation of risk of the pedestrian brain injury. In particular, this applies to the models that simplify the pedestrian skull as a rigid body.
KW - accident reconstruction
KW - biomechanics
KW - Car-to-pedestrian impact
KW - computational model
KW - coupling
KW - head/brain injury risk
UR - http://www.scopus.com/inward/record.url?scp=85075912019&partnerID=8YFLogxK
U2 - 10.1080/15389588.2019.1680837
DO - 10.1080/15389588.2019.1680837
M3 - Article
C2 - 31770038
AN - SCOPUS:85075912019
SN - 1538-9588
VL - 21
SP - 102
EP - 107
JO - Traffic Injury Prevention
JF - Traffic Injury Prevention
IS - 1
ER -