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Record W636679090

Use of CANDE and Design Codes to Assess Stability of Deteriorated Metal Culverts

2012· article· en· W636679090 on OpenAlexaboutno aff
Van ThienMai, Neil A. Hoult, Ian D. Moore

Bibliographic record

VenueTransportation Research Board 91st Annual MeetingTransportation Research Board · 2012
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsCulvertThrustFinite element methodStructural engineeringEngineeringCompression (physics)Geotechnical engineeringDesign loadNumerical analysisGeologyMaterials scienceMathematicsMechanical engineeringComposite material
DOInot available

Abstract

fetched live from OpenAlex

Three design cases are used to study the effects of corrosion, burial depth, and staged construction on the capacity of steel culverts. The finite element packages CANDE and ABAQUS are used to perform the numerical investigation. The results of these numerical models are compared to current and proposed design methods to determine which approach gives the most conservative estimation of thrust force for both new and corroded culverts. Simple ring compression theory (springline thrust equal to half the soil prism load) produced thrusts that were 42%, 16% and 7% lower than those based on “staged construction” finite element analysis for the 4m diameter example culvert buried 1.5m, 3m, and 10m respectively. The American Association of State Highway and Transportation Officials (AASHTO), Canadian, and proposed design equations all underestimated thrust compared to those finite element results (by 46%, 22% and 24% respectively for 4m diameter culvert at 1.5m burial depth, and with 12.5% wall thickness remaining). Thrust forces obtained using CANDE were slightly lower than those produced by ABAQUS, and it appears that CANDE can be used to estimate thrust forces after corrosion, even though the CANDE analysis featured uniform wall loss around the whole pipe circumference and ABAQUS was used to model wall loss across the invert only.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.141
GPT teacher head0.354
Teacher spread0.213 · 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.

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

Citations10
Published2012
Admission routes1
Has abstractyes

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