Effectiveness of Viscoelastic Models for Prediction of Tensile Axial Strains during Cyclic Loading of High-Density Polyethylene Pipe
Bibliographic record
Abstract
High-density polyethylene (HDPE) pipelines are commonly installed using horizontal directional drilling (HDD), a trenchless construction technique used to replace or expand underground pipelines which generates cyclic axial forces on the pipe. To evaluate the ability of existing linear and nonlinear viscoelastic models to predict HDPE pipe response during this cyclic loading, calculations of axial strain are compared with the laboratory measurements. The linear viscoelastic and nonlinear viscoelastic models provide reasonable estimates of the maximum strain levels during installation; however, maximum strains were underestimated by the linear viscoelastic model and overestimated by the nonlinear viscoelastic model. During periods of strain reversal, both models overestimated the amount of axial strain recovery. A parametric study showed how the magnitude of these strains depends on the peak stress during each cycle, the number of cycles, and the period of time stresses are applied. The work also quantifies how increases in peak stress and the number of cycles increase the maximum axial strain. Conventional creep functions can provide reasonable conservative estimations of the maximum strain during a HDD installation provided that the maximum pulling force is known.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".