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

Author

Listed:
  • Chao Yu

    (School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China
    Fujian Collaborative Innovation Center for R&D of Coach and Special Vehicle, Xiamen 361024, China)

  • Fang Wang

    (School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China
    School of Automotive and Mechanical Engineering, Changsha University of Science & Technology, Changsha 410205, China)

  • Bingyu Wang

    (School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China
    Fujian Collaborative Innovation Center for R&D of Coach and Special Vehicle, Xiamen 361024, China)

  • Guibing Li

    (School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)

  • Fan Li

    (State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China)

Abstract

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.

Suggested Citation

  • Chao Yu & Fang Wang & Bingyu Wang & Guibing Li & Fan Li, 2020. "A Computational Biomechanics Human Body Model Coupling Finite Element and Multibody Segments for Assessment of Head/Brain Injuries in Car-To-Pedestrian Collisions," IJERPH, MDPI, vol. 17(2), pages 1-32, January.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:2:p:492-:d:308080
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    Citations

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    Cited by:

    1. Lihai Ren & Dangdang Wang & Xi Liu & Huili Yu & Chengyue Jiang & Yuanzhi Hu, 2020. "Influence of Skull Fracture on Traumatic Brain Injury Risk Induced by Blunt Impact," IJERPH, MDPI, vol. 17(7), pages 1-12, April.
    2. Luiz H. Palucci Vieira & Paulo R. P. Santiago & Allan Pinto & Rodrigo Aquino & Ricardo da S. Torres & Fabio A. Barbieri, 2022. "Automatic Markerless Motion Detector Method against Traditional Digitisation for 3-Dimensional Movement Kinematic Analysis of Ball Kicking in Soccer Field Context," IJERPH, MDPI, vol. 19(3), pages 1-13, January.
    3. Lin Hu & Guangtao Guo & Jing Huang & Xianhui Wu & Kai Chen, 2022. "The Real-World Effects of Route Familiarity on Drivers’ Eye Fixations at Urban Intersections in Changsha, China," IJERPH, MDPI, vol. 19(15), pages 1-13, August.
    4. Yuning Qiao & Yong Peng & Ping Cheng & Xuefei Zhou & Fang Wang & Fan Li & Kui Wang & Chao Yu & Honggang Wang, 2023. "Study on the Cell Magnification Equivalent Method in Out-of-Plane Compression Simulations of Aluminum Honeycomb," Sustainability, MDPI, vol. 15(3), pages 1-15, January.
    5. Shang Gao & Mao Li & Qian Wang & Xianlong Jin & Xinyi Hou & Chuang Qin & Shuangzhi Fu, 2022. "A Research on Accident Reconstruction of Bus–Two-Wheeled Vehicle Based on Vehicle Damage and Human Head Injury," IJERPH, MDPI, vol. 19(22), pages 1-16, November.

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