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Validation of a Continuum-based Weak Zone Joint Model for Simulating Anisotropic Rock Mass Strength

2023· article· en· W4313908469 on OpenAlexaff
R. A. Ziebarth, A G Corkum, D Kinakin, I Stilwell

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2023
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsBGC Engineering (Canada)Dalhousie University
Fundersnot available
KeywordsRock mass classificationAnisotropyJoint (building)Computer scienceContinuum mechanicsGeologyProbabilistic logicGeotechnical engineeringMechanicsStructural engineeringPhysicsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract In recent decades, the approach to estimating rock mass strength and its anisotropic behaviour more accurately has been a growing topic of interest in the rock mechanics community. To evaluate jointed rock mass behaviour more accurately, sophisticated modelling techniques such as Synthetic Rock Mass (SRM) models are becoming increasingly popular. Still, these methods are computationally intensive and require detailed site characterization data that are often unavailable during the initial stages of project development and are not always the most practical option. This study developed an alternative continuum-based SRM-like model where thin contiguous regions of weak continuum material with equivalent joint properties are in place of explicit interface joint elements. The finite-difference program FLAC used these weak zone joints (WZJ) to generate a jointed rock mass continuum model (JRCM). The JRCM concept was applied to systematic sets of persistent joints, a single non-persistent joint, and two intersecting joints to validate the practicality of using WZJ. Results found that the concept produced reasonable anisotropic rock mass strength estimates for most cases, with some limitations. The model captured the anisotropic behaviour while reducing time requirements to develop and recalibrate a conventional SRM. The JRCM concept demonstrates a practical alternative to estimating anisotropic rock mass strength for specific projects where the quantity or quality of data is limited. Furthermore, the reduced run times could allow multiple iterations to perform a probabilistic assessment of the rock mass strength.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.455

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 head0.022
GPT teacher head0.208
Teacher spread0.186 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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