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Record W6999506174

Controlling the performance of sliplined pipe with grouting = L'impact de l'injection de coulis sur la performance lors de la réhabilitation d'une conduite par insertion conventionnelle

2003· article· fr· W6999506174 on OpenAlexvenueaboutno aff

Bibliographic record

VenueNPARC · 2003
Typearticle
Languagefr
FieldEngineering
TopicGrouting, Rheology, and Soil Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsTrenchless technologyAnnulus (botany)GroutCulvertPipeline transportSilt
DOInot available

Abstract

fetched live from OpenAlex

Annulus grouting, or non-grouting, is often the question in the minds of municipal engineers when considering trenchless sliplining technique for pipe rehabilitation. For ease of installation in sliplining rehabilitation, the outside diameter of the liner pipe is usually at least 10% smaller than the inside diameter of the host pipe, resulting in an annular space (or annulus) between the two pipes. Currently the decision on annulus grouting is mainly based on its impact on construction and cost. Based on a three-year study on the performance of a sliplined water main in the City of Ottawa, the effect of the grout on the performance of slipline pipe is presented in this paper. The pros and cons are explained in terms of buckling resistance, potential shear failure at lateral connections, protection of liner pipe when the host pipe fails, load-carrying capacity and control of load-sharing. Furthermore, how grouting can be used to maximise the desired performance of slipline pipe, or to strengthen an existing culvert is suggested in thepaper. This paper fits well with the theme of INFRA 2003 as it shares proven solutions to sliplining rehabilitation and promotes innovation in construction to maximize pipe performance.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.218
Teacher spread0.212 · 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 designObservational
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

Citations1
Published2003
Admission routes2
Has abstractyes

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