Impact Location Dependence of Behind Armor Blunt Trauma Injury Assessed Using a Human Body Finite Element Model
Bibliographic record
Abstract
Behind armor blunt trauma (BABT), resulting from dynamic deformation of protective ballistic armor into the thorax, is currently assessed assuming a constant threshold of maximum backface deformation (BFDs) (44 mm). Although assessed for multiple impacts on the same armor, testing is focused on armor performance (shot-to-edge and shot-to-shot) without consideration of the underlying location on the thorax. Previous studies identified the importance of impacts on organs of animal surrogates wearing soft armor. However, the effect of impact location was not quantified outside the threshold of 44 mm. In the present study, a validated biofidelic advanced human thorax model (50th percentile male) was utilized to assess the BABT outcome from varying impact location. The thorax model was dynamically loaded using a method developed for recreating BABT impacts, and BABT events within the range of real-world impact severities and locations were simulated. It was found that thorax injury depended on impact location for the same BFDs. Generally, impacts over high compliance locations (anterolateral rib cage) yielded increased thoracic compression and loading on the lungs leading to pulmonary lung contusion (PLC). Impacts at low compliance locations (top of sternum) yielded hard tissue fractures. Injuries to the sternum, ribs, and lungs were predicted at BFDs lower than 44 mm for low compliance locations. Location-based injury risk curves demonstrated greater accuracy in injury prediction. This study quantifies the importance of impact location on BABT injury severity and demonstrates the need for consideration of location in future armor design and assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".