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Numerical investigation of driver injury risks in car-to-end terminal crashes using a human finite element model. | LitMetric

Numerical investigation of driver injury risks in car-to-end terminal crashes using a human finite element model.

Comput Methods Biomech Biomed Engin

Department of Biomedical Engineering and Mechanics, VA Tech, Blacksburg, Virginia, USA.

Published: August 2024

AI Article Synopsis

  • Although U.S. guardrail end terminal safety is evaluated using crash tests, the existing injury risk assessment methods don't consider modern vehicle safety features like seatbelts and airbags.
  • This study simulated car crashes with guardrail end terminals using a sedan and a human body model, testing various speeds, angles, and offsets to analyze the impact on occupant injuries.
  • Findings revealed that higher speeds significantly increased injury risks, with angles affecting injury likelihood more than offsets; furthermore, vehicle-based metrics had strong correlations with specific injuries, suggesting improvements in predicting occupant risks.

Article Abstract

Although the safety performance of guardrail end terminals is tested using crash tests in the U.S., occupant injury risks are evaluated based on the flail-space model. This approach developed in the early 1980s neglects the influence of safety features (e.g. seatbelt, airbags, etc.) installed in late model vehicles. In this study, a vehicle (sedan, 1100 kg), a guardrail end terminal (ET-Plus) and a human body model (Global Human Body Model Consortium, GHBMC) were integrated to simulate car-to-end terminal crashes. Five velocities, two offsets, and two angles were used as pre-impact conditions. In all the 20 simulations, kinematics and kinetic data were recorded in GHBMC and vehicle models to calculate the GHBMC injury probabilities and vehicle-based injury metrics, correspondingly. Pre-impact velocity was observed to have the largest effect on the occupant injury measures. All the body-region and full-body injury risks increased with the increasing velocity. Meanwhile, the angles had a larger effect than offset to the change of full-body injury risk (9.1% vs. 0.3%). All the vehicle-based metrics had good correlations to full-body injury probabilities. Occupant Impact Velocity (OIVx), Acceleration Severity Index (ASI), and Theoretical Head Impact Velocity (THIV) had a good correlation to chest, thigh, upper tibia, and lower tibia injuries. All the other correlations (e.g. brain/head injuries) were not statistically significant. The results pointed out that more vehicle-based metrics (ASI and THIV) could help improve the predictability in terms of occupant injury risks in the tests. Numerical methodology could be used to assess head and brain injury probabilities, which were not predictable by any vehicle-based metrics.

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Source
http://dx.doi.org/10.1080/10255842.2024.2387223DOI Listing

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