A Computational Biomechanics Human Body Model Coupling Finite Element and Multibody Segments for Assessment of Head/Brain Injuries in Car-To-Pedestrian Collisions

It has been challenging to efficiently and accurately reproduce pedestrian head/brain injury, which is one of the most important causes of pedestrian deaths in road traffic accidents, due to the limitations of existing pedestrian computational models, and the complexity of accidents. In this paper, a new coupled pedestrian computational biomechanics model (CPCBM) for head safety study is established via coupling two existing commercial pedestrian models. The head–neck complex of the CPCBM is from the Total Human Model for Safety (THUMS, Toyota Central R&D Laboratories, Nagakute, Japan) (Version 4.01) finite element model and the rest of the parts of the body are from the Netherlands Organisation for Applied Scientific Research (TNO, The Hague, The Netherlands) (Version 7.5) multibody model. The CPCBM was validated in terms of head kinematics and injury by reproducing three cadaveric tests published in the literature, and a correlation and analysis (CORA) objective rating tool was applied to evaluate the correlation of the related signals between the predictions using the CPCBM and the test results. The results show that the CPCBM head center of gravity (COG) trajectories in the impact direction (YOZ plane) strongly agree with the experimental results (CORA ratings: Y = 0.99 ± 0.01; Z = 0.98 ± 0.01); the head COG velocity with respect to the test vehicle correlates well with the test data (CORA ratings: 0.85 ± 0.05); however, the correlation of the acceleration is less strong (CORA ratings: 0.77 ± 0.06). No significant differences in the behavior in predicting the head kinematics and injuries of the tested subjects were observed between the TNO model and CPCBM. Furthermore, the application of the CPCBM leads to substantial reduction of the computation time cost in reproducing the pedestrian head tissue level injuries, compared to the full-scale finite element model, which suggests that the CPCBM could present an efficient tool for pedestrian brain-injury research.