Effect of joint contamination on the quality of butt-fused high-density polyethylene (HDPE) pipe joints
Bibliographic record
Abstract
Butt fusion is an effective method for joining high-density polyethylene (HDPE) pipe on job sites. However, there is lack of quantitative methods to evaluate pipe joint quality. The objective of this study was to develop such a quantitative approach to determine the effect of joint contamination on the quality of the fused joints. The study included testing a total of 21 butt-fused HDPE pipe joints that were made under different dust conditions. The results of the tests were then used to determine the effect of dust contamination on the quality of the fused joints. Microscopic examination of the fracture surfaces of the test specimens showed that not all the contaminants at the welding interface were squeezed out into the beads formed during the fusion process. Four failure modes were identified and a failure criterion was established to categorize the quality of a fused joint into four grades: bad, poor, good, and excellent. Among the properties of the materials considered, tensile energy to break (TEB) and maximum strain were found to be the most acceptable parameters for distinguishing between joints of different quality. Furthermore, the results showed that under optimum joining conditions, the quality of butt-fused joints can be as good as that of the parent pipe.Key words: polyethylene pipe, butt fusion, joint contamination, tensile energy to break, failure criterion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".