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Record W1532239519

Numerical modelling of helical culverts during backfilling

2006· article· en· W1532239519 on OpenAlexaboutno aff
R Pritchard

Bibliographic record

VenueQueensland's institutional digital repository (The University of Queensland) · 2006
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsCulvertBending momentEngineeringStructural engineeringGeotechnical engineeringBendingOverburdenDeformation (meteorology)Geology
DOInot available

Abstract

fetched live from OpenAlex

This paper presents the findings from numerical modelling of a sinusoidal culvert and ribbed profile helical steel culvert during backfilling with low overburden fill. Deformation, hoop forces and bending moment in the culvert and stress in the backfill are obtained. A detailed understanding of the culvert throughout the backfilling operation has been developed. The deformed shape of the culvert during backfilling is consistent with the high bending moments. The actual response of an instrumented test culvert is shown to be similar to the predictions from the numerical modelling. The Canadian Highway Bridge Design Code (2000) considers bending during backfilling. The methods of design in other current world design standards ignore bending effects, assuming hoop compression force is dominant. This assumption does not accurately represent the real behaviour of a helical culvert. It may lead to unacceptably high bending stresses and yielding during backfilling. (a) For the covering entry of this conference, please see ITRD abstract no. E214936.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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

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 head0.006
GPT teacher head0.153
Teacher spread0.147 · 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 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

Citations1
Published2006
Admission routes1
Has abstractyes

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