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Record W4283693115 · doi:10.3390/geosciences12070262

Evaluation of the Effect of Geomechanical Parameters and In Situ Stress on Tunnel Response Using Equivalent Mohr-Coulomb and Generalized Hoek-Brown Criteria

2022· article· en· W4283693115 on OpenAlexafffundabout
Ali Saeidi, Côme Cloutier, Abbas Kamalibandpey, Alireza Shahbazi

Bibliographic record

VenueGeosciences · 2022
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMohr–Coulomb theoryGeomechanicsRock mass classificationGeologyGeotechnical engineeringGeological Strength IndexRock mechanicsHoek–Brown failure criterionFinite element methodStress (linguistics)ShieldDisplacement (psychology)CoulombStress fieldStructural engineeringEngineeringPhysicsPetrology

Abstract

fetched live from OpenAlex

The generalized Hoek-Brown (GHB) failure criterion can estimate the rock mass parameters required for rock mechanics–related analyses such as numerical modeling in geomechanics. The determination of GHB parameters has been developed in the field of rock mechanics. Due to the wide use of the Mohr-Coulomb criterion and the lack of an existing relationship for determining its parameters for a rock mass, equivalent Mohr-Coulomb parameters (EMC) can be derived from the GHB. To determine the differences in the use of these two criteria, we analyzed the behavior of a deep circular tunnel in nine stress states for three metamorphic rocks recovered from the Canadian Shield from rock masses that present a very blocky structure. We carried out 241 simulations using the finite element code RS2 to assess the effect of the geological strength index (GSI), in situ stress, and rock type on the deviation of wall displacement, the number of yielded elements, and the differential stress obtained by the GHB and EMC parameters. A combination of low in situ stress and high GSI yielded similar results when using both failure criteria.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.046
GPT teacher head0.300
Teacher spread0.254 · 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

Citations10
Published2022
Admission routes3
Has abstractyes

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