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Record W4401527922 · doi:10.1139/cgj-2023-0667

An improved small-scale test setup for assessing backward erosion piping

2024· article· en· W4401527922 on OpenAlexafffundvenue
Sina Ramezanifouladi, Jean Côté

Bibliographic record

VenueCanadian Geotechnical Journal · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaHatchEnGlobeHydro-Québec
KeywordsPipingGeotechnical engineeringErosionScale (ratio)Internal erosionGeologyForensic engineeringEngineeringEnvironmental scienceStructural engineeringLeveeMechanical engineering

Abstract

fetched live from OpenAlex

Backward erosion piping is one of the erosion mechanisms that lead to embankment dam incidents, whereby foundation granular materials are transported to the downstream toe leaving a shallow pipe in place. Most previous studies have focused on secondary erosion as the responsible process for the pipe progression. On the other hand, a few experimental investigations have considered primary erosion by restricting the pipe pathway within a confined channel for easier monitoring of local gradients. This paper presents a modified small-scale setup with the hole-type exit configuration to capture local hydraulic conditions associated with the progression of the pipe in a realistic and unconstrained pathway. Horizontal compaction and a more accurate measurement and acquisition system are other improvements added to this setup. The test outcomes of the two fine sands have revealed that the scale factor of Sellmeijer’s model can be used to predict the critical global hydraulic gradient for the specific geometry of the small-scale setup with the hole-type exit. Moreover, the progression of the pipe remains unaffected by loading history. Results also confirm the previous findings regarding the mutual influence of local gradient and pipe tip progression.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0010.000
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 head0.018
GPT teacher head0.269
Teacher spread0.252 · 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.

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

Citations4
Published2024
Admission routes3
Has abstractyes

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