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Record W2999899192

A Stepwise Approach to Verification of the Combined Finite-Discrete Element Method for Modelling Instability Around Tunnels in Brittle Rock

2019· dissertation· en· W2999899192 on OpenAlexfundno aff
S.L. Markus

Bibliographic record

VenueQSpace (Queen's University Library) · 2019
Typedissertation
Languageen
FieldEngineering
TopicGeotechnical Engineering and Underground Structures
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNuclear Waste Management Organization
KeywordsInstabilityFinite element methodBrittlenessStructural engineeringGeologyDiscrete element methodGeotechnical engineeringEngineeringMaterials scienceMechanicsPhysicsComposite material
DOInot available

Abstract

fetched live from OpenAlex

Numerical modelling of excavations in rock has advanced considerably in recent decades. While continuum numerical models form their basis in methods which can be verified by analytical solutions, discontinuum and hybrid numerical modelling software are challenging to verify. This necessitates the development of processes that can verify individual aspects of complex models. The combined finitediscrete element method (FDEM) allows for the numerical representation of progressive fracture in a simulated elastic material. The FDEM is a powerful tool for modelling instability around tunnels in brittle rock; however, significant verification of the method is required for its use in predictive modelling in critical engineering projects. To verify the FDEM for the purpose of modelling instability around tunnels in brittle rock, a multi-method and multi-scale stepwise verification approach is proposed. Multi-scale verification is required due to the practical limitations of current computational power. Simulation of a laboratory scale tests generally requires a mesh size not significantly larger than the median grain size of the material, limiting the size of elements to a few millimeters for most rock types. In tunnel scale models, a larger element sizes must be used. To relate the input parameters obtained from the laboratory scale calibration to parameters which can be used in tunnel-scale modelling, a gradual upscaling process for Unconfined Compressive Strength (UCS) test simulations is developed. The results of the upscaling process provide guidance for input parameter selection for tunnel-scale models, and insight into scale effects in FDEM models. In multi-method verification, equivalent modelling scenarios are represented using analytical and numerical methods of increasing complexity, to allow individual model behaviours to be progressively verified. A pseudo-discontinuum finite element method (FEM) approach is compared with the FDEM for modelling fracture propagation. Agreement of results is found for simulated non-frictional materials; however, for frictional materials, results agreement is not achieved. Further assessment of tunnel model response to pseudo-discontinuum FEM and FDEM input parameters will lead to improvement of input parameter and result equivalency between methods.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.196
Teacher spread0.187 · 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 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

Citations3
Published2019
Admission routes1
Has abstractyes

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