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Record W2029541099 · doi:10.1139/cgj-2014-0493

Scaled physical and numerical modelling of static soil pressures on box culverts

2015· article· en· W2029541099 on OpenAlexafffundvenue
Osama Abuhajar, Tim Newson, M. Hesham El Naggar

Bibliographic record

VenueCanadian Geotechnical Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCulvertGeotechnical engineeringLateral earth pressureStiffnessSoil structure interactionSlabBending momentGeologyCentrifugeOverburden pressureStructural engineeringEngineeringFinite element method

Abstract

fetched live from OpenAlex

The response of buried box culverts is a complex soil–structure interaction problem, where the relative stiffness between the soil and structure is a critical factor. In addition, soil arching is an important aspect of the soil–culvert interaction problem. A series of static scaled physical model centrifuge tests were performed to investigate soil–culvert interaction. Two different box culvert thicknesses and Nevada sand specimens with different relative densities were used to explore the interaction between the sand and box culverts under different conditions. The static loading consisted of the self-weight from the soil body. The responses of the box culvert were recorded for all loading conditions. The results were evaluated in terms of bending moment, soil pressure, and soil–culvert interaction factors. Soil pressures were evaluated using different experimental methods, which provided comparable results. The soil pressure observed on the culvert top slab showed parabolic distribution, i.e., higher values at the edges and lower at the centre than the theoretical vertical soil (overburden) pressure. The horizontal soil pressure on the side wall increased with depth. The soil–culvert interaction factors decreased at the centre and increased at the edges of the top slab, as the thickness and relative stiffness of the culvert decreased.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.582
Threshold uncertainty score0.759

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 head0.015
GPT teacher head0.203
Teacher spread0.188 · 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

Citations23
Published2015
Admission routes3
Has abstractyes

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