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Record W2891719616 · doi:10.1139/cgj-2018-0506

Experimental behaviour of concrete box culverts — comparison with current codes of practice

2018· article· en· W2891719616 on OpenAlexvenueno aff
Nuno Cristelo, Carlos Félix, Joaquim Figueiras

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
Fundersnot available
KeywordsCulvertLateral earth pressureSlabStructural engineeringSettlement (finance)Finite element methodGeotechnical engineeringCurrent (fluid)LeveeEngineeringDeformation (meteorology)Scale modelCivil engineeringGeologyComputer science

Abstract

fetched live from OpenAlex

It is now accepted that current expeditious models for determining earth pressures on flexible underground structures under compacted layers do not include several technical nuances of the soil–structure interaction. Thus, these models are not capable of delivering an optimized design. The present paper compares the results from the well-known American Association of State Highway and Transportation Officials (AASHTO) model with two different numerical models — a user-friendly elastic model and a more robust finite element model — and with results retrieved from a full-scale monitoring of a concrete box culvert, 5.5 m high and 3.77 m width, over which a 15 m high embankment was built. This structure was instrumented selectively, over a period of almost 1 year, during which several parameters were recorded, including earth pressures and structural deformation. Results have shown that the two most significant drawbacks associated with the use of the AASHTO model are the inadequate evaluation of vertical pressure on the top slab and the coefficient of earth pressure, which results in a significant overestimation of the lateral pressures and, consequently, in an overall inefficient design of the structure.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.535
Threshold uncertainty score0.664

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 head0.011
GPT teacher head0.254
Teacher spread0.243 · 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 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

Citations7
Published2018
Admission routes1
Has abstractyes

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