Relationship between texture of polypropylene coatings and interface friction for sand at low stress levels
Bibliographic record
Abstract
Pipelines in the offshore sector make common use of polypropylene (and other polymer) coating systems to protect the infrastructure and provide thermal insulation. High pressure high temperature pipelines are subject to large axial loads due to restraint by seafloor soils of thermal strains in the pipe wall. Friction between pipe coating material and seafloor soil plays a defining role in the build-up of axial stress and the formation of lateral buckling. Accurate quantification of pipe–soil interface friction is key to robust pipeline stability design and possibilities to enhance or manipulate the friction coefficient may be attractive to designers. An extensive campaign of soil and interface tests using a range of granular materials and polypropylene surface specimens engineered to achieve varying surface textures was carried out. The results show that interface friction primarily depends on stress level and the magnitude of the surface texture in relation to the particle size. Herein, a new relative texture parameter, T a , is developed that, unlike alternative relative roughness parameters, can be obtained using conventional profilometry measurement techniques. An expression for estimating the friction coefficient in relation to texture and stress level is proposed that can serve as a useful predictor of pipe–soil interface friction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".