MétaCan
Menu
Back to cohort
Record W2335450394 · doi:10.1061/40994(321)54

Wavy Imperfections and the Strength of Cast-in-Place Pressure Pipe Liners

2008· article· en· W2335450394 on OpenAlexafffund
Nancy Ampiah, Amir Fam, Ian D. Moore

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCircumferenceMaterials scienceCrackingCast ironComposite materialInternal pressureWrinkleAmplitudeStructural engineeringEngineeringOpticsPhysics

Abstract

fetched live from OpenAlex

The installation of resin-impregnated felt tubes within cast iron water pipes can lead to wavy imperfections (wrinkles) if the external circumference of the liner exceeds the internal circumference of the cast iron pipe. A split-disk test apparatus has been developed and a series of laboratory experiments have been conducted to measure the strength of liners with wavy imperfections. The design of the test apparatus is described, and results are reported for liners installed within cast iron pipes of six inch nominal internal diameter. Results for three different kinds of imperfections are provided, where measurements of maximum hoop force are related to the imperfection size (wavelength and amplitude). Most tests resulted in failures at or in the vicinity of the wrinkles. As the wrinkle size increased, the ultimate strength of the liner and its strength at first cracking were reduced. However, the effect was more detrimental on the latter than the former.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.178
Teacher spread0.174 · 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

Citations3
Published2008
Admission routes2
Has abstractyes

Explore more

Same topicGeotechnical Engineering and Underground StructuresFrench-language works237,207