MétaCan
Menu
Back to cohort

Distribution of Earth Pressure on Induced Trench Culverts

2022· article· en· W4224080699 on OpenAlexaff
Campbell Bryden, Kaveh Arjomandi, Arun J. Valsangkar

Bibliographic record

VenueInternational Journal of Geomechanics · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsCulvertTrenchLateral earth pressureParametric statisticsFinite element methodParametric designStructural engineeringEngineeringGeologyGeotechnical engineeringCivil engineeringMaterials scienceMathematics

Abstract

fetched live from OpenAlex

The induced trench construction method has been in practice across North America for the last century, and the analysis and design of such structures are conducted in accordance with Marston’s theory and the indirect design method. Recent North American design standards have adopted the Standard Installations for Direct Design approach for the design of rigid culverts, which does not address induced trench installations. In order to advance the state of practice for induced trench design so as to align with modern design standards, it is necessary to first achieve a fundamental understating of their loading conditions as uncertainties remain with regards to the actual distribution of earth pressures around the circumference of induced trench culverts. In this study, finite-element modeling was employed to evaluate the distribution of earth pressures acting on induced trench culverts; numerical modeling techniques were validated with experimental data and parametric studies were performed to investigate the influence of key design parameters for culverts installed in both single and twin configurations. The results of this study suggest that the distribution of earth pressure acting on induced trench culverts differs significantly from that acting on positive projecting culverts and that the interaction between induced trench twin culverts is not insignificant.

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 score0.481
Threshold uncertainty score0.313

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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of GeomechanicsSame topicGeotechnical Engineering and Underground StructuresFrench-language works237,207