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Record W2600266851 · doi:10.1061/9780784480465.009

Numerical Modelling, Design, and Construction of a Geotextile-Reinforced Soil-Metal Buried Structure (GRS) under Deep Fills in Challenging Soil Conditions

2017· article· en· W2600266851 on OpenAlexaff
Meckkey El-Sharnouby, Dave Worsley, Phil Carroll, Calvin D. VanBuskirk

Bibliographic record

VenueGeotechnical Frontiers 2017 · 2017
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsAtlantic Industries (Canada)Terahertz Technology Solutions (Canada)
Fundersnot available
KeywordsCulvertLeveeGeotechnical engineeringConsolidation (business)GeotextileFinite element methodGeologySettlement (finance)Soil waterEnvironmental scienceEngineeringStructural engineeringSoil scienceComputer science

Abstract

fetched live from OpenAlex

This paper presents a case history of design and construction of a geotextile reinforced soil-metal (GRS) 2040 mm diameter structural plate culvert under a 42 m high embankment at a mine in South America. The geotechnical data indicated that the underlying SAPROLITE and laterite soils were very soft with predicted consolidation settlement under the conduit and embankment of up to 3m. Due to the complexity of the problem, two-dimensional non-linear finite element analysis was employed to analyze the soil-structure and soil-geotextile interaction. Results showed that the composite system considerably improved the performance of the culvert under the considered loading conditions. The structure was constructed in 2015 to 2016 and it is reported that the structure is performing satisfactory. The composite GRS-culvert system provided a viable solution for challenging site conditions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2017
Admission routes1
Has abstractyes

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