Experimental and Statistical Analyses of the Tribological and Weathering Behaviors of Pipeline Support Materials
Bibliographic record
Abstract
Abstract Polymers such as Neoprene, Polytetrafluoroethylene (PTFE), or Ultra-high-molecular-weight (UHMW) polyethylene (PE) are typically used as an intermediary layer between the pipeline or piping structures and rigid ground supports. Neoprene is a commonly used support material but suffers from water accumulation problems and subsequent corrosion of the pipe surfaces in contact with it. Although alternative materials are available, there is a lack of comprehensive data on their friction behaviors with various pipe surface conditions, or their weathering behavior in common environmental conditions. Therefore, in this study friction experiments were conducted on various material/surface combinations, and weathering experiments were conducted on common support materials in relevant environmental conditions. Friction results show that material combinations with Neoprene have the highest static coefficient of friction (CoF), on average. In contrast, combinations with PTFE or in some cases UHMW-PE have the lowest static CoF behaviors, on average. The results of this study are important as inputs in pipeline structural design calculations and vibrational studies or dynamic loading simulations. Comparisons of the performance of various pipe support materials is also useful for high-level material selection and screening purposes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".