Browse Publications Technical Papers 2015-01-1437

Field-based Assessments of Various AIS2+ Head Risk Curves for Frontal Impact 2015-01-1437

In the present study, various risk curves for moderate-to-fatal head injury (AIS2+) were theoretically assessed by comparing model-based injury rates with field-based injury rates. This was accomplished by applying the risk curves in corresponding field models. The resulting injury rates were considered from two perspectives: aggregate (0-56 kph events) and point-estimate (higher-speed, barrier-like events).Four risk curves were studied: a HIC15-based curve from Mertz et al. (1997), a BRIC-based curve from Takhounts et al. (2011), a BrIC-based curve from Takhounts et al. (2013) and a Concussion-Correlate-based curve from Rowson et al. (2013).The field modeling pertained to adult drivers in 11-1 o'clock, towaway, full-engagement frontal crashes in the National Automotive Sampling System (NASS, calendar years = 1993-2012), and the model-year range of the passenger vehicles was 1985-2010. The attendant inhomogeneity of the restraint systems was approximated by four representative systems.Prior to the field modeling, base models were developed and validated to acceptable levels. Specifically, there were seven assessments of head-impact tests (avg PctDiff = 5.2%) and nine assessments of representative restraint-system models relative to fleet-wide tests (avg PctDiff = 5.0% relative to fleet medians).Subsequently, 36 comparisons were conducted via the field modeling relative to NASS (i.e., four curves and nine aggregate/point assessments). On average, the Concussion-Correlate-based curve demonstrated the best fidelity from the aggregate perspective (+7% difference), with the following rank ordering: Concussion Correlate, HIC15, BRIC, and BrIC. As for the point-estimate perspective, the HIC15 curve demonstrated the best fidelity (−26% difference), rank ordered as HIC15, Concussion Correlate, BRIC, and BrIC. The data underlying these results indicated that more study is needed to improve the fidelity of curve-based risk estimation.


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