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Record W2783040449 · doi:10.1139/cgj-2017-0292

Measurement of pressure in viewable hole erosion test

2018· article· en· W2783040449 on OpenAlexvenueno aff
Liquan Xie, Xin Liang, Tsung-Chow Su

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsPipingErosionGeotechnical engineeringInternal erosionGeologyFlow (mathematics)MechanicsEngineeringMechanical engineeringPhysicsGeomorphology

Abstract

fetched live from OpenAlex

The hole erosion test (HET) is commonly used to study the occurrence of internal soil erosion when water concentrated leaks occur. This erosion is known as “piping” in soil mechanics. Piping erosion is invisible and occurs randomly within the soil body. Therefore, to gain a better understanding of how piping erosion develops, it would be helpful to utilize a viewable HET design in which the dynamics of the piping hole can be observed directly. In this note, a new HET apparatus is presented that can be used to observe the development of piping erosion and to monitor the dynamic pressure condition during the hole erosion process. A preliminary model test was carried out based on the new viewable HET apparatus and “pressure heads” monitoring technique. The results successfully verified the performance of the proposed apparatus and experimental methods during the process of hole erosion, indicating that the hole shape changes during continuous erosion and is not fully symmetrical because of the initial profile of the hole. The internal hole becomes increasingly curved when subjected to continuous piping flow. Test results agree with the numerical simulation reported in 2015 by Riha and Jandora, who considered the effect of the hole entrance shape.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.194
Teacher spread0.182 · 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 designObservational
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

Citations13
Published2018
Admission routes1
Has abstractyes

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