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Predicting pedestrian lower limb fractures in real world vehicle crashes using a detailed human body leg model Cover

Predicting pedestrian lower limb fractures in real world vehicle crashes using a detailed human body leg model

Open Access
|Dec 2021

Abstract

Purpose: The purpose of this study was to evaluate the capability of a detailed FE human body lower limb mode, called HALL (Human Active Lower Limb) model, in predicting real world pedestrian injuries and to investigate injury mechanism of pedestrian lower limb in vehicle collisions.

Methods: Two real world vehicle-to-pedestrian crashes with detailed information were selected. Then, a pedestrian model combining the HALL model and the upper body of the 50th% Chinese dummy model and vehicle front models were developed to reconstruct the selected real world crashes, and the predictions of the simulations were analyzed together with observations from the accident data.

Results: The results show that the predictions of the HALL model for pedestrian lower limb long bone fractures match well with the observation from hospital data of the real world accidents, and the predicted thresholds of bending moment for tibia and femur fracture are close to the average values calculated from cadaver test data. Analysis of injury mechanism of pedestrian lower limb in collisions indicates that the relatively sharper bumper of minivan type vehicles can produce concentrated loading to the lower leg and a high risk of tibia/fibula fracture, while the relatively sharper and lower bonnet leading edge may cause concentrate loading to the thigh and high femur fracture risk.

Conclusions: The findings imply that the HALL model could be used as an effective tool for predicting pedestrian lower limb injuries in vehicle collisions and improvements to the minivan bumper and sedan bonnet leading edge should be concerned further in vehicle design.

DOI: https://doi.org/10.37190/abb-01894-2021-02 | Journal eISSN: 2450-6303 | Journal ISSN: 1509-409X
Language: English
Page range: 33 - 41
Submitted on: Sep 24, 2021
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Accepted on: Oct 27, 2021
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Published on: Dec 21, 2021
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2021 Zhewu Chen, Xing Huang, Donghua Zou, Fuhao Mo, Jin Nie, Guibing Li, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.