Reconstruction of Real-World Car-to-Pedestrian Accident Using Computational Biomechanics Model: Effects of the Choice of Boundary Conditions of the Brain on Brain Injury Risk

  • Fang WangEmail author
  • Bingyu Wang
  • Yong Han
  • Qian Peng
  • Fan Li
  • Adam Wittek
Conference paper


In the current study, the effects of the approach for modelling the brain–skull interface on prediction of the brain injury risk are investigated using a previously validated computational head-brain model. Four types of brain–skull interface modelling approaches (1): the method used in original Total HUman Model for Safety THUMS Head-brain model, (2): brain rigidly attached to the skull, (3): frictionless contact between the brain and skull, and (4): cohesive layer (spring-type) between the brain and skull are employed in numerical reconstruction of a real-world car-to-pedestrian impact accident. The results indicate that the predicted brain injury risk is strongly affected by the approach for modelling the brain–skull interface. The comparison of the predicted risk of diffuse axonal injury DAI and brain contusions with the injuries sustained by the pedestrian involved in the accident seems to suggest that accurate prediction of the brain injury risk using computational biomechanics models requires direct representation of the meninges and subarachnoidal space with the CSF.


Brain–skull boundary conditions Brain–skull interface Finite element brain model Explicit finite element modeling Pedestrian accident Accident reconstruction Traumatic brain injury Diffuse axonal injury 



The research was supported by National Natural Science Foundation of China (Grant No. 51605407, 51505403), Fujian Provincial Department of Science and Technology (Grant No. 2017J01652), and State Administration of Foreign Experts Affairs P.R. China (Grant~No.~GDT20173600566). All FE simulations using Total HUman Model for Safety THUMS Version 4.0 human body model and all the simulations for the accident reconstruction using MADYMO multibody code in this research were conducted at Xiamen University of Technology.


  1. 1.
    Fahlstedt M, Halldin P, Alvarez VS et al (2016) Influence of the body and neck on head kinematics and brain injury risk in bicycle accident situations. In: 2016 IRCOBI conference proceedings, Malaga, SpainGoogle Scholar
  2. 2.
    Gabler LF, Crandall JR, Panzer MB (2016) Investigating brain injury tolerance in the sagittal plane using a finite element model of the human head. Int J Automot Eng 7:37–43Google Scholar
  3. 3.
    Hardy WN, Foster CD, Mason MJ et al (2001) Investigation of head injury mechanisms using neutral density technology and high-speed biplanar X-ray. Stapp Car Crash J 45:337–368Google Scholar
  4. 4.
    Melvin JW, Lighthall JW, Kazunari U (1993) Brain-injury biomechanics. In: Nahum AM, Melvin JW (eds) Accidental injury. Springer, New YorkGoogle Scholar
  5. 5.
    Bain AC, Meaney DF (2000) Tissue-level thresholds for axonal damage in an experimental model of central nervous system white matter injury. J Biomech Eng 122:615–622CrossRefGoogle Scholar
  6. 6.
    Zhang L, Yang KH, King AI (2004) A proposed injury threshold for mild traumatic brain injury. J Biomech Eng 126:226–236CrossRefGoogle Scholar
  7. 7.
    Kleiven S (2006) Evaluation of head injury criteria using a finite element model validated against experiments on localized brain motion, intracerebral acceleration, and intracranial pressure. Int J Crashworthiness 11:65–79CrossRefGoogle Scholar
  8. 8.
    Takhounts EG, Craig MJ, Moorhouse K et al (2013) Development of brain injury criteria (BrIC). Stapp Car Crash J 57:243Google Scholar
  9. 9.
    Miller K, Chinzei K (2002) Mechanical properties of brain tissue in tension. J Biomech 35:483–490CrossRefGoogle Scholar
  10. 10.
    Hardy WN (2007) Response of the human cadaver head to impact. Dissertation, Wayne State UnivesityGoogle Scholar
  11. 11.
    Mazumder MMG, Miller K, Bunt S et al (2013) Mechanical properties of the brain–skull interface. Acta Bioeng Biomech 15:9Google Scholar
  12. 12.
    Agrawal S, Wittek A, Joldes G et al (2015) Mechanical properties of brain-skull interface in compression. In: Doyle B, Miller K, Wittek A, Nielsen PMF (eds) Computational biomechanics for medicine. Springer, New YorkGoogle Scholar
  13. 13.
    Nahum AM, Smith R, Ward CC (1977) Intracranial pressure dynamics during head impact. Stapp Car Crash J 21:339–366Google Scholar
  14. 14.
    Claessens M, Sauren F, Wismans J (1997) Modeling of the human head under impact conditions: a parametric study. SAE Technical Paper No. 973338Google Scholar
  15. 15.
    Miller RT, Margulies SS, Leoni M et al (1998) Finite element modeling approaches for predicting injury in an experimental model of severe diffuse axonal injury. SAE Technical Paper No. 983154Google Scholar
  16. 16.
    Wittek A, Omori K (2003) Parametric study of effects of brain-skull boundary conditions and brain material properties on responses of simplified finite element brain model under angular acceleration in sagittal plane. JSME Int J 46:1388–1398CrossRefGoogle Scholar
  17. 17.
    Marjoux D, Baumgartner D, Deck C et al (2008) Head injury prediction capability of the HIC, HIP, SIMon and ULP criteria. Accid Anal Prev 40:1135–1148CrossRefGoogle Scholar
  18. 18.
    Wittek A, Joldes G, Couton M et al (2010) Patient-specific non-linear finite element modelling for predicting soft organ deformation in real-time: application to non-rigid neuroimage registration. Prog Biophys Mol Biol 103:292–303CrossRefGoogle Scholar
  19. 19.
    Wittek A, Grosland NM, Joldes GR et al (2016) From finite element meshes to clouds of points: a review of methods for generation of computational biomechanics models for patient-specific applications. Ann Biomed Eng 44:3–15CrossRefGoogle Scholar
  20. 20.
    Zhang L, Yang KH, Dwarampudi R et al (2001) Recent advances in brain injury research: a new human head model development and validation. Stapp Car Crash J 45:369–394Google Scholar
  21. 21.
    Kleiven S, Hardy WN (2002) Correlation of an FE model of the human head with local brain motion: consequences for injury prediction. Stapp Car Crash J 46:123–144Google Scholar
  22. 22.
    Yang J, Xu W, Otte D (2008) Brain injury biomechanics in real world vehicle accident using mathematical models. Chin J Mech 32:81–86CrossRefGoogle Scholar
  23. 23.
    Miller K, Wittek A, Joldes G et al (2010) Modelling brain deformations for computer-integrated neurosurgery. Int J Numer Methods Biomed Eng 26:117–138CrossRefGoogle Scholar
  24. 24.
    Miller K (2011) Biomechanics of the brain. Springer, New YorkCrossRefGoogle Scholar
  25. 25.
    Yang KH, King AI (2011) Modeling of the brain for injury simulation and prevention. In: Miller K (ed) Biomechanics of the brain. Springer, New YorkGoogle Scholar
  26. 26.
    Mao H, Zhang L, Jiang B et al (2013) Development of a finite element human head model partially validated with thirty five experimental cases. J Biomech Eng 135:111002CrossRefGoogle Scholar
  27. 27.
    Bayly PV, Clayton EH, Genin GM (2012) Quantitative imaging methods for the development and validation of brain biomechanics models. Annu Rev Biomed Eng 14:369–396CrossRefGoogle Scholar
  28. 28.
    Jin X (2009) Biomechanical response and constitutive modeling of bovine pia-arachnoid complex. Dissertation, Wayne State UniversityGoogle Scholar
  29. 29.
    Jin X, Yang KH, King AI (2011) Mechanical properties of bovine pia–arachnoid complex in shear. J Biomech 44:467–474CrossRefGoogle Scholar
  30. 30.
    Jin X, Mao H, Yang KH et al (2014) Constitutive modeling of pia–arachnoid complex. Ann Biomed Eng 42:812–821CrossRefGoogle Scholar
  31. 31.
    Mao H, Zhang L, Yang KH et al (2006) Application of a finite element model of the brain to study traumatic brain injury mechanisms in the rat. Stapp Car Crash J 50:583Google Scholar
  32. 32.
    Shigeta K, Kitagawa Y, Yasuki T (2009) Development of next generation human FE model capable of organ injury prediction. In: International technical conference on the enhanced safety of vehicles (ESV), Stuttgart, GermanyGoogle Scholar
  33. 33.
    Watanabe R, Miyazaki H, Kitagawa Y et al (2011) Research of collision speed dependency of pedestrian head and chest injuries using human FE model (THUMS version 4). In: Proceedings of 22nd enhanced safety of vehicles (ESV) conference, Washington DC, USAGoogle Scholar
  34. 34.
    Yang J (2011) Investigation of brain trauma biomechanics in vehicle traffic accidents using human body computational models. In: Wittek A, Nielsen PMF, Miller K (eds) Computational biomechanics for medicine. Springer, New YorkGoogle Scholar
  35. 35.
    Al-Bsharat AS, Hardy WN, Yang KH et al (1999) Brain/skull relative displacement magnitude due to blunt head impact. In: Proceedings of the 1999 43rd Stapp car crash conference, San Diego, CA, USAGoogle Scholar
  36. 36.
    Wang F, Geng Z, Agrawal S et al (2017) Computation of brain deformations due to violent impact: quantitative analysis of the importance of the choice of boundary conditions and brain tissue constitutive model. In: Wittek A, Joldes G, Nielsen PMF, Doyle BJ, Miller K (eds) Computational biomechanics for medicine. Springer, New YorkGoogle Scholar
  37. 37.
    Li F, Yang J (2010) A study of head–brain injuries in car-to-pedestrian crashes with reconstructions using in-depth accident data in China. Int J Crashworthiness 15:117–124CrossRefGoogle Scholar
  38. 38.
    Nie J, Yang J (2014) A study of bicyclist kinematics and injuries based on reconstruction of passenger car–bicycle accident in China. Accid Anal Prev 71:50–59CrossRefGoogle Scholar
  39. 39.
    Nie J, Li G, Yang J (2015) A study of fatality risk and head dynamic response of cyclist and pedestrian based on passenger car accident data analysis and simulations. Traffic Inj Prev 16:76–83CrossRefGoogle Scholar
  40. 40.
    Kong C, Yang J (2010) Logistic regression analysis of pedestrian casualty risk in passenger vehicle collisions in China. Accid Anal Prev 42:987–993CrossRefGoogle Scholar
  41. 41.
    AAAM (2008) Abbreviated injury scale 2005, update 2008. Association for Advancement of Automatic Medicine, Barrington, USAGoogle Scholar
  42. 42.
    Yang J (1997) Injury biomechanics in car-pedestrian collisions: development, validation and application of human-body mathematical models. Dissertation, Chalmers University of TechnologyGoogle Scholar
  43. 43.
    Yang J, Lövsund P, Cavallero C et al (2000) A human-body 3D mathematical model for simulation of car-pedestrian impacts. Traffic Inj Prev 2:131–149Google Scholar
  44. 44.
    TNO (2013) MADYMO utilities manual, version 7.5. TASS International, Helmond, The NetherlandsGoogle Scholar
  45. 45.
    Yao J, Yang J, Otte D (2008) Investigation of head injuries by reconstructions of real-world vehicle-versus-adult-pedestrian accidents. Saf Sci 46:1103–1114CrossRefGoogle Scholar
  46. 46.
    Bandak FA, Zhang AX, Tannous RE et al (2001) SIMon: a simulated injury monitor; application to head injury assessment. In: International technical conference on the enhanced safety of vehicles, Amsterdam, The NetherlandsGoogle Scholar
  47. 47.
    Takhounts EG, Eppinger RH, Campbell JQ et al (2003) On the development of the SIMon finite element head model. Stapp Car Crash J 47:107–133Google Scholar
  48. 48.
    Takhounts EG, Ridella SA, Hasija V et al (2008) Investigation of traumatic brain injuries using the next generation of simulated injury monitor (SIMon) finite element head model. Stapp Car Crash J 52:1–31Google Scholar
  49. 49.
    Hu J, Jin X, Lee JB et al (2007) Intraoperative brain shift prediction using a 3D inhomogeneous patient-specific finite element model. J Neurosurg 106:164–169CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Fang Wang
    • 1
    • 2
    Email author
  • Bingyu Wang
    • 1
    • 2
  • Yong Han
    • 1
    • 2
  • Qian Peng
    • 1
    • 2
  • Fan Li
    • 3
  • Adam Wittek
    • 4
  1. 1.School of Mechanical and Automotive EngineeringXiamen University of TechnologyXiamenChina
  2. 2.Fujian Collaborative Innovation Center for R&D of Coach and Special VehicleXiamenChina
  3. 3.College of Mechanical and Vehicle EngineeringHunan UniversityChangshaChina
  4. 4.Intelligent Systems for Medicine Laboratory, School of Mechanical and Chemical EngineeringThe University of Western AustraliaCrawley-PerthAustralia

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