A pseudoelastic response of hyperelastic composites reinforced with nonlinear elastic fibrous materials: Continuum modeling and analysis
Bibliographic record
Abstract
The present study aims to develop a continuum-based model to predict the pseudoelastic behavior of biological composites subjected to finite plane elastostatics. The proposed model incorporates a hyperelastic matrix material reinforced with nonlinear fibers, addressing challenges such as irreversible softening responses, large deformations, and nonlinear stress–strain responses. The kinematics of reinforcing fibers are formulated via the first and second gradient of continuum deformations and, more importantly, damage function and damage variables of Ogden–Roxburgh and Weibull type are integrated into the model to assimilate the various aspects of damage mechanisms present in soft tissues. Adopting the framework of variational principles and a virtual work statement, the Euler equation and admissible boundary conditions are obtained. The proposed model successfully predicts the Mullins effect observed in the human aorta and the Manduca muscle. Experimental validation with elastomeric composites demonstrates its utility to replicate softening and fiber damage phenomena, including deformation profiles. Further, the proposed molecular dynamics scheme offers an enhanced understanding of polymer chain entanglement processes, thereby facilitating the quantification of permanent damage in elastomeric composites. The obtained results may provide valuable insight toward understanding and modeling the mechanical behavior of soft biological tissues with practical implications for the design and analysis of biofabricated composites aimed at mimicking biological tissues.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".