A high-fidelity biomechanical modeling framework for injury prediction during frontal car crash

Ellahi, Ashique; Gupta, Shubham; Bose, Dhruv; Chanda, Arnab

doi:10.1080/10255842.2023.2281899

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Shouhei K conducted collision simulation experiments using finite element Sedan models and MADYMO pedestrian models to generate image datasets and used training datasets for deep learning to determine the impact of pedestrian physical differences in predicting head injuries in vehicle pedestrian accidents [ 30 ]. Ashique E used fracture modeling techniques to simulate a frontal collision between a vehicle and a rigid wall and used a human body model to study fractures and tears [ 31 ]. Pushpender P used multi-body software and finite element software to reconstruct human vehicle collision accidents, obtaining relevant collision conditions such as vehicle speed and pedestrian position through multi-body simulation and using them as initial conditions for a finite element simulation comparing the damage prediction ability of the THUMS finite element human body model in real human vehicle collisions [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Prediction Method for Pedestrian Head Injury in Intelligent Vehicle with Combined Active and Passive Safety System

Shi,

Zhang,

et al. 2024

Biomimetics

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With the development of intelligent vehicle technology, the probability of road traffic accidents occurring has been effectively reduced to a certain extent. However, there is still insufficient research on head injuries in human vehicle collisions, making it impossible to effectively predict pedestrian head injuries in accidents. To study the efficacy of a combined active and passive safety system on pedestrian head protection through the combined effect of the exterior airbag and the braking control systems of an intelligent vehicle, a “vehicle–pedestrian” interaction system is constructed in this study and is verified by real collision cases. On this basis, a combined active and passive system database is developed to analyze the cross-influence of the engine hood airbag and the vehicle braking curve parameters on pedestrian HIC (head injury criterion). Meanwhile, a hierarchy design strategy for a combined active and passive system is proposed, and a rapid prediction of HIC is achieved via the establishment of a fitting equation for each grading. The results show that the exterior airbag can effectively protect the pedestrian’s head, prevent the collision between the pedestrian’s head and the vehicle front structure, and reduce the HIC. The braking parameter H2 is significantly correlated with head injury, and when H2 is less than 1.8, the HIC value is less than 1000 in nearly 90% of cases. The hierarchy design strategy and HIC prediction method of the combined active and passive system proposed in this paper can provide a theoretical basis for rapid selection and parameter design.

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Section: Introductionmentioning

confidence: 99%

A Hierarchical Prediction Method for Pedestrian Head Injury in Intelligent Vehicle with Combined Active and Passive Safety System

Shi,

Zhang,

et al. 2024

Biomimetics

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“…Biomechanical modelling can also lead to designing tools that minimise the loading and tissue damage [ 13 , 14 ]. Biomechanical models are also employed in automotive safety research to understand damage to organs in impact [ 15 , 16 , 17 ], sports injuries [ 18 ] and elastography methods [ 19 , 20 , 21 ]. The key parameters considered in the biomechanical modelling of porcine kidney in our study were mechanical properties and tissue deformation.…”

Section: Introductionmentioning

confidence: 99%

Biomechanical Modelling of Porcine Kidney

Mishra,

Cleveland

2024

Bioengineering

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In this study, the viscoelastic properties of porcine kidney in the upper, middle and lower poles were investigated using oscillatory shear tests. The viscoelastic properties were extracted in the form of the storage modulus and loss modulus in the frequency and time domain. Measurements were taken as a function of frequency from 0.1 Hz to 6.5 Hz at a shear strain amplitude of 0.01 and as function of strain amplitude from 0.001 to 0.1 at a frequency of 1 Hz. Measurements were also taken in the time domain in response to a step shear strain. Both the frequency and time domain data were fitted to a conventional Standard Linear Solid (SLS) model and a semi-fractional Kelvin–Voigt (SFKV) model with a comparable number of parameters. The SFKV model fitted the frequency and time domain data with a correlation coefficient of 0.99. Although the SLS model well fitted the time domain data and the storage modulus data in the frequency domain, it was not able to capture the variation in loss modulus with frequency with a correlation coefficient of 0.53. A five parameter Maxwell–Wiechert model was able to capture the frequency dependence in storage modulus and loss modulus better than the SLS model with a correlation of 0.85.

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A high-fidelity biomechanical modeling framework for injury prediction during frontal car crash

Cited by 2 publications

References 23 publications

A Hierarchical Prediction Method for Pedestrian Head Injury in Intelligent Vehicle with Combined Active and Passive Safety System

A Hierarchical Prediction Method for Pedestrian Head Injury in Intelligent Vehicle with Combined Active and Passive Safety System

Biomechanical Modelling of Porcine Kidney

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