Comprehensive Characterization of Soft Tissue and Surrogate Materials across Varied Loading Methods
Bibliographic record
Abstract
Exploring the mechanical properties of soft tissues under compressive loading is crucial for understanding their role in automobile incidents. Soft tissues, which serve as cushions or padding between bone and vehicle interiors, significantly influence contact duration and forces, thereby altering incident kinematics and injury. In this investigation, muscle and soft connective tissues from post-mortem human subjects (PMHS) forearms were excised and subjected to compression and indentation testing methods at various rates and strains. Specific samples with higher proportions of muscle were compared against samples without muscle tissues to evaluate the role of compositional changes. Anthropomorphic test device (ATD) upper extremity foam and vinyl–foam composite analog tissues underwent similar testing for comparison. High impact rates simulating those in high-speed automotive collisions were achieved using a custom-built drop tower impactor setup. The results revealed significantly higher stiffness values for samples with large proportions of muscle tissue compared to no muscle samples at smaller deformations. Substantial differences in stiffness were seen between soft tissues and ATD materials across most loading rates and strains, although some exceptions were noted at higher rates and strains. An indentation and modified Zener model were used to quantify material parameters. These findings provide a solid basis for advancing ATD analogs and have broader implications for soft tissue research. Moreover, this work represents a crucial step toward enhancing safety standards in the automotive industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".