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Record W2953578162 · doi:10.1016/j.jrmge.2019.04.002

A method to determine relevant geomechanical parameters for evaluating the hydraulic erodibility of rock

2019· article· en· W2953578162 on OpenAlexafffund
Lamine Boumaiza, Ali Saeidi, Marco Quirion

Bibliographic record

VenueJournal of Rock Mechanics and Geotechnical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsHydro-QuébecUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of CanadaHydro-Québec
KeywordsGeologyGeotechnical engineeringErosionBlock (permutation group theory)Joint (building)Rock mass classificationCompressive strengthFlow (mathematics)Shear (geology)PetrologyEngineeringGeomorphologyMathematicsGeometry

Abstract

fetched live from OpenAlex

Among the methods used for evaluating the potential hydraulic erodibility of rock, the most common methods are those based on the correlation between the force of flowing water and the capacity of a rock to resist erosion, such as Annandale's and Pells' methods. The capacity of a rock to resist erosion is evaluated based on erodibility indices that are determined from specific geomechanical parameters of a rock mass. These indices include unconfined compressive strength (UCS) of rock, rock block size, joint shear strength, a block's shape and orientation relative to the direction of flow, joint openings, and the nature of the surface to be potentially eroded. However, it is difficult to determine the relevant geomechanical parameters for evaluating the hydraulic erodibility of rock. The assessment of eroded unlined spillways of dams has shown that the capacity of a rock to resist erosion is not accurately evaluated. Using more than 100 case studies, we develop a method to determine the relevant geomechanical parameters for evaluating the hydraulic erodibility of rock in unlined spillways. The UCS of rock is found not to be a relevant parameter for evaluating the hydraulic erodibility of rock. On the other hand, we find that the use of three-dimensional (3D) block volume measurements, instead of the block size factor used in Annandale's method, improves the rock block size estimation. Furthermore, the parameter representing the effect of a rock block's shape and orientation relative to the direction of flow, as considered in Pells' method, is more accurate than the parameter adopted by Annandale's 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 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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.020
GPT teacher head0.271
Teacher spread0.251 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations19
Published2019
Admission routes2
Has abstractyes

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