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Record W4318613121 · doi:10.1080/19648189.2023.2172083

Integration of rock joint roughness into the Mohr-Coulomb shear behaviour model – application to dam safety analysis

2023· article· en· W4318613121 on OpenAlexafffund
Adrien Rullière, Laurent Peyras, J Duriez, Patrice Rivard, Pierre Breul

Bibliographic record

VenueEuropean Journal of Environmental and Civil engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementHydro-Québec
KeywordsMohr–Coulomb theoryCohesion (chemistry)Direct shear testJoint (building)Geotechnical engineeringShear (geology)Surface finishGeologyGravity damConstitutive equationShear strength (soil)Shear stressMechanicsStructural engineeringFinite element methodMaterials scienceEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

Based on a previous experimental study that we conducted on granite joint replicas including thirty direct shear tests, this article proposes to integrate the joint roughness in the Mohr-Coulomb shear behaviour model (MC). The model integrates joint roughness into constitutive stress-displacement relationships describing mechanical behaviour of rock joint. The shear strength of rock joint is assessed by the MC shear criterion that takes into account the apparent cohesion. This MC model integrating rock joint roughness component is validated against other experimental results from the literature. It is able to accurately predict the peak shear strength of an unbounded rock joint with an average relative error of 7.9%. It is finally used to assess the role of joint roughness on the shear behaviour of a gravity dam foundation.

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)0.000
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: none
Teacher disagreement score0.545
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.181
Teacher spread0.174 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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