Assessment of Thorax Finite Element Model Response for Behind Armor Blunt Trauma Impact Loading Using an Epidemiological Database
Bibliographic record
Abstract
Nonperforating ballistic impacts on thoracic armor can cause blunt injuries, known as behind-armor blunt trauma (BABT). To evaluate the potential for this injury, the back face deformation (BFD) imprinted into a clay backing is measured; however, the link between BFD and potential for injury is uncertain. Computational human body models (HBMs) have the potential to provide an improved understanding of BABT injury risk to inform armor design but require assessment with relevant loading scenarios. In this study, a methodology was developed to apply BABT loading to a computational thorax model, enhanced with refined finite element mesh and high-deformation rate mechanical properties. The model was assessed using an epidemiological BABT survivor database. BABT impact boundary conditions for 10 cases from the database were recreated using experimentally measured deformation for specific armor/projectile combinations, and applied to the thorax model using a novel prescribed displacement methodology. The computational thorax model demonstrated numerical stability under BABT impact conditions. The predicted number of rib fractures, the magnitude of pulmonary contusion, and injury rank, increased with armor BFD, back face velocity, and input energy to the thorax. In three of the 10 cases, the model overpredicted the number of rib fractures, attributed to impact location positional sensitivity and limited details from the database. The integration of an HBM with the BABT loading method predicted rib fractures and injury ranks that were in good agreement with available medical records, providing a potential tool for future armor evaluation and injury assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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".