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Investigation on risk prediction of pedestrian head injury by real-world accidents
- Source :
- Transport, Vol 34, Iss 3 (2019)
- Publication Year :
- 2019
- Publisher :
- Vilnius Gediminas Technical University, 2019.
-
Abstract
- Head injury is the most common and fatal injury in car-pedestrian accidents. Due to the lack of human test data, real-world accident data is useful for the research on the mechanism and tolerance of head injuries. The objective of the present work is to investigate pedestrian head-brain injuries through real car-pedestrian accidents and evaluate the existed injury criteria. Seven car-to-pedestrian accidents in China were selected from the IVAC (Investigation of Vehicle Accident in Changsha) database. Accident reconstructions using multi-body models were conducted to determine the kinematic parameters associated with the injury and were used to measure head injury criteria. Kinematic parameters were input into a finite element model to run simulations on the head-brain and car interface to determine levels of brain tissue stress, strain, and brain tissue injury criteria. A binary logistic regression model was used to determine the probability of head injury risk associated with AIS3+ injuries (Abbreviated Injury Scale). The results showed that head injury criteria using kinematic parameters can effectively predict injury risk of a pedestrians’ head skull. Regarding brain injuries, physical parameters like coup/countercoup pressure are more effective predictors. The results of this study can be used as the background knowledge for pedestrian friendly car design.
Details
- Language :
- English
- ISSN :
- 16484142 and 16483480
- Volume :
- 34
- Issue :
- 3
- Database :
- Directory of Open Access Journals
- Journal :
- Transport
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.2502000c86e94960bd9318c90f00058d
- Document Type :
- article
- Full Text :
- https://doi.org/10.3846/transport.2019.10410