A finite element-guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions

Berthelson, P. R., Ghassemi, P., Wood, J. W., Stubblefield, G. G., Al. Graitti, A. J., Jones, M. D. ORCID: https://orcid.org/0000-0002-6058-6029, Horstemeyer, M. F., Chowdhury, S. and Prabhu, R. K. 2021. A finite element-guided mathematical surrogate modeling approach for assessing occupant injury trends across variations in simplified vehicular impact conditions. Medical and Biological Engineering and Computing 59 (5) , 1065–1079. 10.1007/s11517-021-02349-3

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Abstract

A finite element (FE)–guided mathematical surrogate modeling methodology is presented for evaluating relative injury trends across varied vehicular impact conditions. The prevalence of crash-induced injuries necessitates the quantification of the human body’s response to impacts. FE modeling is often used for crash analyses but requires time and computational cost. However, surrogate modeling can predict injury trends between the FE data, requiring fewer FE simulations to evaluate the complete testing range. To determine the viability of this methodology for injury assessment, crash-induced occupant head injury criterion (HIC15) trends were predicted from Kriging models across varied impact velocities (10–45 mph; 16.1–72.4 km/h), locations (near side, far side, front, and rear), and angles (−45 to 45°) and compared to previously published data. These response trends were analyzed to locate high-risk target regions. Impact velocity and location were the most influential factors, with HIC15 increasing alongside the velocity and proximity to the driver. The impact angle was dependent on the location and was minimally influential, often producing greater HIC15 under oblique angles. These model-based head injury trends were consistent with previously published data, demonstrating great promise for the proposed methodology, which provides effective and efficient quantification of human response across a wide variety of car crash scenarios, simultaneously.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Springer
ISSN:	0140-0118
Date of Acceptance:	17 March 2021
Last Modified:	09 Nov 2022 11:20
URI:	https://orca.cardiff.ac.uk/id/eprint/142836

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