The effect of impulse on the axial fracture tolerance of the isolated tibia during automotive and military impacts

Martinez, Alberto; Chakravarty, Avery; Quenneville, Cheryl E.

Affiliations

¹Department of Mechanical Engineering, McMaster University, Hamilton, Ontario, Canada

²School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada

Abstract

Axial impacts to the lower leg during debilitating events such as frontal automotive collisions and military underbody blasts can cause significant injuries to the tibia. Several studies have conducted axial impact tests to determine the injury limits of the lower leg, mostly focused on automotive intrusions, resulting in an established force criterion for injury assessments. Due to the viscoelastic properties of bone, it remains unclear whether results from automotive experiments can be applied to higher-rate military blasts. In this study, the effect of impulse and loading rate on the fracture tolerance of the tibia was investigated. Eight male isolated cadaveric tibia specimens (from six pairs, mean age: 62 ± 8 years) were subjected to axial impact loads using a custom-built pneumatic impactor. Foam of varying levels of compliance was placed in line with the impacts to control the impact durations. One specimen from each pair was tested for the military blast condition and the contralateral for the automotive condition, with right-left selection randomized. Impacts were applied in increasing levels of intensity (defined using energy levels) until fracture occurred. Impact levels were selected to limit the number of strikes to each specimen (to minimize any accumulated damage). Paired ttests were used to determine whether there was a statistically significant difference between the two test conditions for several of the impact parameters. It was found that there was a statistically significant difference in peak force (p = 0.022), acceleration (p = 0.04), and kinetic energy (p = 0.082) between the automotive and military test conditions, but not impulse (p = 0.216). The model determined to be most successful for predicting fracture by a best subsets regression analysis included projectile velocity, peak force, kinetic energy, impulse, and impact plate acceleration. Ongoing testing will increase the sample size of the study and allow development of a rate-dependent injury criterion.

Links

URL: http://ibrc.osu.edu/wp-content/uploads/2015/05/Martinez_Manuscript_2016.pdf

Cited Works (7)

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