A practical approach to modeling time‐dependent nonlinear creep behavior of polyethylene for structural applications
Bibliographic record
Abstract
Abstract The purpose of this work is to develop a practical method for constitutive modeling of polyethylene, based on a phenomenological approach, which can be applied for structural analysis. Polyethylene is increasingly used as a structural material, for example, in pipes installed by trenchless methods, where the relatively low stiffness of polyethylene reduces the required installation forces, chemical inertness makes it applicable for corrosive environments, and adequate strength allows its use in sewer, gas, and water lines. Polyethylene exhibits time‐dependent constitutive behavior which is also dependent on the applied stress level resulting in nonlinear stress–strain relationships. Nonlinear viscoelastic theory has been well established and a variety of modeling approaches have been derived from it. To realistically utilize the nonlinear modeling approaches in design, a simple method is needed for finding a constitutive formulation for a specific polyethylene type. This paper presents such a practical approach to nonlinear viscoelastic modeling utilizing both the multi‐Kelvin element theory and the power law functions to model creep compliance. Creep tests are used to determine material parameters and models are generated for four different polyethylene materials. The corroboration of the models is completed by comparisons with results from different tensile creep, step‐loading creep, and load‐rate tests. POLYM. ENG. SCI., 48:159–167, 2008. © 2007 Society of Plastics Engineers
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".