MétaCan
Menu
Back to cohort
Record W2989197616 · doi:10.1139/cgj-2019-0560

Evaluation of induced trench twin culverts constructed in deep fill

2019· article· en· W2989197616 on OpenAlexaffvenue
Campbell Bryden, Kaveh Arjomandi, Arun J. Valsangkar

Bibliographic record

VenueCanadian Geotechnical Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsCulvertTrenchLeveeLateral earth pressureGeotechnical engineeringOverburdenGeologyEarthworksStructural engineeringEngineeringMaterials science

Abstract

fetched live from OpenAlex

When culverts are installed beneath high embankments, earth loads become excessive and the induced trench construction method is a viable design option to reduce the culvert loads to acceptable levels. However, limited field studies evaluating the performance of induced trench twin culverts are reported in the literature and the practicality and effectiveness of the induced trench construction method (in general) has been subject to recent criticism. This paper describes the performance of twin 3048 mm inside-diameter reinforced concrete culverts constructed with an induced trench beneath 15.3 m of fill. Research instruments and autonomous data acquisition systems were installed during construction to monitor (i) culvert earth pressures, (ii) embankment deformations, and (iii) groundwater elevations in the vicinity of the compressible fill. The experimental observations recorded throughout the construction phase are presented herein; the embankment deformations are indicative of effective positive arching within the induced trench region, and the average earth pressure at the culvert crown was reduced to approximately 48% of the overburden soil pressure. The experimental data are compared with those reported in the literature by others, and the conclusions attained from this study demonstrate the effectiveness of the induced trench construction method.

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)0.001
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 score0.034
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.012
GPT teacher head0.216
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

Citations11
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Geotechnical JournalSame topicGeotechnical Engineering and Underground StructuresFrench-language works237,207