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Record W2736959740 · doi:10.1139/cgj-2017-0084

Pre- and post-rehabilitation behaviour of a deteriorated horizontal ellipse culvert

2017· article· en· W2736959740 on OpenAlexafffundvenue
Jacob Tetreault, Neil A. Hoult, Ian D. Moore

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaOhio Department of Transportation
KeywordsCulvertStiffnessStructural engineeringGeotechnical engineeringAxleEllipseEngineeringCorrosionGeologyMaterials scienceComposite materialMathematicsGeometry

Abstract

fetched live from OpenAlex

An experimental campaign was undertaken to assess the impact of deterioration and rehabilitation on the performance of corrugated steel horizontal ellipse culverts. A corrugated steel horizontal ellipse culvert was corroded using an accelerated corrosion technique and then buried up to a soil cover of 0.45 m. It was subsequently tested using simulated tandem axle loading. The culvert was then rehabilitated using the paved invert technique and tested under the same loading arrangement before being loaded up to its ultimate capacity. Results were compared with a previous study on an intact horizontal ellipse culvert. For the level of corrosion present, there seemed to be no impact on the structural behaviour as both the intact and corroded culverts had similar stiffness. Paving the invert did improve the structural performance compared to the deteriorated culvert. For example, the axial strains were reduced by 23% at the crown and 31% at the shoulder. The ultimate capacity of the rehabilitated structure was also improved as it failed at a total tandem axle load of 1600 kN while the intact culvert failed at 1325 kN. It is worth noting that both of these culverts failed at a load greater than twice the fully factored design load.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.899
Threshold uncertainty score1.000

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 head0.005
GPT teacher head0.206
Teacher spread0.201 · 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

Citations31
Published2017
Admission routes3
Has abstractyes

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