Nonlinear Viscoelastic Creep Prediction of HDPE-Agro-fiber Composites
Bibliographic record
Abstract
Agro-based plastic composites are being used as deck boards and other load-bearing materials. In these composites, both fibers and plastics contribute to creep when they carry loads. However, the existing literature concentrates on creep prediction and characterization of composites as a single-phase material. In our study, an attempt is made to develop a generic creep prediction model that describes the creep behavior of composites with the constituents’ creep behavior. The ‘theory of mixture’ for composites is extended to describe the creep behavior of this material, which is two phase. This model is validated for HDPE-rice husk composites with power-law-Boltzmann’s superposition principles. The model works well not only to describe creep, but also its nonlinearity. The model is generic enough for extending it to incorporate varying environmental conditions, such as time and temperature. This is the first model to describe creep for a two-phase bio-based composite. This study is a vanguard in correlating Schapery’s ‘single-phase’ model with a ‘two-phase’ model, where the same is validated for step-loading situations.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".