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Record W2801009924 · doi:10.1139/cgj-2017-0578

Mechanical analysis and interpretation of excavation damage zone formation around deep tunnels within massive rock masses using hybrid finite–discrete element approach: case of Atomic Energy of Canada Limited (AECL) Underground Research Laboratory (URL) test tunnel

2018· article· en· W2801009924 on OpenAlexafffundvenueabout
Ioannis Vazaios, Nicholas Vlachopoulos, Mark S. Diederichs

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsRoyal Military College of CanadaQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaNuclear Waste Management Organization
KeywordsSpallBrittlenessExcavationRock mass classificationGeotechnical engineeringDiscrete element methodGeologyFinite element methodFracture mechanicsRock mechanicsStress fieldEngineeringStructural engineeringMechanicsMaterials science

Abstract

fetched live from OpenAlex

The construction of an underground opening leads to changes in the in situ stress regime surrounding the excavation. The opening influences the rock mass owing to the redistribution of the stresses and results in the disturbance of the surrounding ground. At great depths, massive to slightly or moderately fractured rock masses are usually encountered, and under high stresses, they are more likely to behave in a brittle manner during an excavation. While constitutive models have been developed and proposed for the numerical simulation of such excavations using continuum mechanics, this brittle response cannot be simulated accurately enough, since the material behaviour is governed by fracture initiation and propagation. On the contrary, discontinuum approaches are more suitable in such cases. For the purposes of this paper, the brittle behaviour of hard, massive rock masses and the associated spalling failure mechanisms were simulated by employing a finite–discrete element method (FDEM) approach using Irazu software. The generated numerical model was utilized to replicate field conditions based on the observations at the Atomic Energy of Canada Limited (AECL) Underground Research Laboratory (URL) test tunnel located in Pinawa, Manitoba, Canada. The model results are compared with field observation data to explicitly demonstrate the suitability of the 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 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)0.001
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 score0.561
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.023
GPT teacher head0.246
Teacher spread0.223 · 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

Citations60
Published2018
Admission routes4
Has abstractyes

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