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Numerical Modeling of Soil and Surface Foundation Pressure Effects on Buried Box Culvert Behavior

2016· article· en· W2478139088 on OpenAlexafffund
Osama Abuhajar, M. Hesham El Naggar, Tim Newson

Bibliographic record

VenueJournal of Geotechnical and Geoenvironmental Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCulvertLateral earth pressureGeotechnical engineeringFoundation (evidence)CentrifugeEngineeringBending momentParametric statisticsStructural engineeringGeologyMathematics

Abstract

fetched live from OpenAlex

Box culverts can be subjected to considerable induced earth pressure from overlaying structures and foundations. Therefore, the effect of surface foundations on soil pressures around box culverts needs to be properly investigated and incorporated into the analysis and design of culverts. In this study, the effect of a surface foundation on the response of box culverts was investigated experimentally and numerically. A series of centrifuge tests were performed to evaluate the additional soil pressures around box culverts installed in sand due to adjacent surface foundations. The experimental results were used to calibrate and verify a two-dimensional (2D) numerical model developed using computer software. This model was then used for a parametric study to investigate the effect of the relative foundation location on various aspects of the culvert response. The soil pressures, culvert bending moments, and soil-culvert interaction factors were all considered for different soil depths and surface foundation locations. The results from this analysis were used to establish charts that can aid in assessing the effect of surface foundations on box culvert behavior.

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: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.665

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 integrity0.0000.000
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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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

Citations29
Published2016
Admission routes2
Has abstractyes

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