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Record W2321207977 · doi:10.1061/9780784413142.119

Response of Bell and Spigot Joints in Culverts under Vehicle Load

2013· article· en· W2321207977 on OpenAlexafffund
David Becerril García, 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
KeywordsCulvertJoint (building)Structural engineeringContext (archaeology)Geotechnical engineeringReinforced concreteDesign loadEngineeringMaterials scienceGeology

Abstract

fetched live from OpenAlex

While a significant number of long-term sewer pipe and culvert failures result from joint leakage and erosion of backfill, there have been very few studies examining the behaviour of pipe joints. There is no established method for the design of bell and spigot or other joint types. Reported here, therefore, are test results on 600-mm diameter reinforced concrete, 900-mm diameter polyvinyl chloride, and 1500-mm diameter high density polyethylene pipes with gasketed bell and spigot joints. Full-scale live load tests on jointed pipe systems were conducted in the test facility at Queen's University, involving two different burial depths and three different surface load locations relative to the joints being evaluated. The experimental results are presented and assessed to determine the key demands generated at the joints and how they are influenced by the loading location, burial depth and pipe material. The different characteristics of soil-structure interaction for joints in rigid and flexible pipes are highlighted. Finally, joint performance in the laboratory is discussed in the context of the development of structural design methods for joints.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.005
GPT teacher head0.185
Teacher spread0.179 · 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

Citations2
Published2013
Admission routes2
Has abstractyes

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