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Record W2765575611 · doi:10.1115/pvp2017-66146

Acceptance Limits for Subsurface Voids in HDPE Piping

2017· article· en· W2765575611 on OpenAlexaff
Phillip Rush, Douglas A. Scarth, Douglas Munson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsKinectrics (Canada)
Fundersnot available
KeywordsPipingHigh-density polyethyleneVoid (composites)CreepService lifeGeotechnical engineeringVoid ratioMaterials scienceForensic engineeringStructural engineeringEnvironmental sciencePolyethyleneEngineeringComposite materialMechanical engineering

Abstract

fetched live from OpenAlex

Mandatory Appendix XXVI in Section III of the ASME Code (2015 Edition) includes the requirements for the construction of Class 3 high density polyethylene (HDPE) pressure piping. Currently, there are no acceptance limits for subsurface voids in HDPE piping included in the Code. Data from recent EPRI tests conducted to determine the rate of growth of flaws in HDPE piping can be used to develop acceptance criteria for subsurface voids. The tests exposed specimens with surface flaws to sustained loads under elevated temperature and stress conditions. No failures were observed in the tests. Therefore, the results are useful to establish lower bound lifetime estimates for flawed piping under the maximum allowable temperature and stress conditions for buried pipe. The HDPE material has considerable ability to resist crack growth at temperatures well above the maximum allowable service conditions permitted in Appendix XXVI. The test data were used to establish allowable subsurface void sizes in HDPE piping. Correlations for rupture time that are based on the applied stress intensity and net section stress were used to determine an allowable subsurface void size. Because the creep failure mechanism in HDPE piping is time-dependent, an allowable void size was developed to ensure HDPE pipe integrity at the void location for a 50-year service life. The allowable flaw sizes can be used for the disposition of void indications identified in piping both before and after installation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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.

Opus teacher head0.029
GPT teacher head0.269
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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Same topicFatigue and fracture mechanicsFrench-language works237,207