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Record W2050924437 · doi:10.3141/1845-19

Response of Repaired Sewers Under Earthloads

2003· article· en· W2050924437 on OpenAlexaff
T. C. Michael Law, Ian D. Moore

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2003
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsQueen's UniversityWestern University
Fundersnot available
KeywordsTrenchless technologyCulvertSanitary sewerBendingHost (biology)Context (archaeology)Geotechnical engineeringPipeline transportCanalisationEngineeringElectrical conduitStructural engineeringStiffnessCurrent (fluid)PipingGeologyMechanical engineering

Abstract

fetched live from OpenAlex

The trenchless rehabilitation of damaged rigid sewers has become a competitive alternative to conventional methods of pipeline replacement. However, buckling caused by fluid load is identified as the important limit state in the current pipe-liner design standard, while the contribution of the damaged rigid host pipe in the assessment of resistance to earth loads as well as disturbance to the liner (e.g., vehicle loads) is neglected. Fullscale testing in the laboratory is used to investigate the soil–host and pipe–liner interaction. Two host-pipe–liner systems are examined. The first system involves a liner that fits perfectly inside a host pipe. The second system features initial lack of fit between the liner and the host pipe, and gaps across the fractures in the host pipe, to investigate ungrouted repair of rigid pipe with severe damage. The test geometry and measurement scheme to evaluate local bending and movement at the fractures in the host pipe are described. Key parameters affecting local bending are identified, including initial lack of fit between the liner and the host pipe as well as the hoop stiffness of the host pipe. This recent research on repair of sanitary and storm water sewers is discussed in the context of culvert rehabilitation.

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)0.005
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.044
GPT teacher head0.318
Teacher spread0.274 · 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 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

Citations6
Published2003
Admission routes1
Has abstractyes

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