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Record W2127387947 · doi:10.1139/cgj-2014-0531

Field performance of in-service cast iron water reticulation pipe buried in reactive clay

2015· article· en· W2127387947 on OpenAlexfundvenueno aff
Derek Chan, Chaminda Gallage, Pathmanathan Rajeev, Jayantha Kodikara

Bibliographic record

VenueCanadian Geotechnical Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersQueen's UniversitySouth East WaterCommonwealth Scientific and Industrial Research OrganisationCity West Water
KeywordsWater contentGeotechnical engineeringSuctionBreakageEnvironmental scienceMoisturePore water pressureSoil waterUltimate tensile strengthField capacityPipeline transportCreepMaterials scienceGeologySoil scienceComposite materialEngineeringEnvironmental engineering

Abstract

fetched live from OpenAlex

Field monitoring is an important means for understanding soil behaviour and its interaction with buried structures such as pipeline. This paper details the successful instrumentation of a section of an in-service cast iron water main buried in an area of reactive clay where frequent water pipe breakage has been observed. The instrumentation included measurement of pipe strain; pipe water pressure and temperature; soil pressure, temperature, moisture content, and matric suction; as well as the meteorological conditions on site. The data generally indicated that changes in soil temperature, suction, and moisture content were directly related to the local climatic variations. The suction and moisture content data indicated that the soil profile at the site down to around 700 mm, and probably down to 1000 mm, is affected by changes in surface weather, while soil conditions below this depth appear to be more stable. Analysis of pipe strain indicated that the pipe behaves like a cantilever beam, with the top experiencing predominantly tensile strains during summer. Subsequently, these trends reduce to compressive strains as soil swelling occurs because of the increase of moisture content with the onset of winter.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.194
Teacher spread0.185 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations35
Published2015
Admission routes2
Has abstractyes

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