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Record W2766057061 · doi:10.1139/cgj-2016-0626

Chemically dependent mechanical properties of natural andesite rock fractures

2017· article· en· W2766057061 on OpenAlexvenueno aff
Ehsan Mohtarami, Alireza Baghbanan, Mohammadreza Akbariforouz, Hamid Hashemolhosseini, E. Asadollahpour

Bibliographic record

VenueCanadian Geotechnical Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsAndesiteCohesion (chemistry)Surface finishGeotechnical engineeringMaterials scienceRock mass classificationFracture (geology)Hydraulic conductivityOverburden pressureComposite materialMineralogyGeologySoil waterVolcanic rockChemistryVolcano

Abstract

fetched live from OpenAlex

Different engineering rock works are affected by chemical–mechanical coupling phenomena in rock mass. Friction, strength of fracture walls, shape of asperities, and roughness control hydraulic and mechanical behaviors of fractures, which are all affected by chemical solutions. In addition, the efficiency of some operations such as leaching depend on the fracture conductivity in a confined condition. To evaluate the effects of leaching agents on the mechanical and geometrical properties of andesite rock fractures, a set of laboratory-scale experiments were conducted on three types of andesite in a sulfate medium with different pH values. Although the cohesion and shear strength reduced by decreasing the pH value, the roughness and shear stiffness of fractures did not follow a unique trend. Scanning electron microscope images indicated that the reason is mainly due to the selective chemical dissolution of the more reactive minerals in acidic solution. It is shown that fracture conductivity is a strong function of confining stress, asperities resistance, and aperture in such a way that confining pressure decreases the conductivity appreciably and the reduction rate depends on the mineralogy of fracture surfaces.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.557

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 science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.211
Teacher spread0.198 · 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 designBench or experimental
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

Citations25
Published2017
Admission routes1
Has abstractyes

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