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Fracture Undulation Modelling in Discontinuum Analysis: Implications for Rock Mass Strength Assessment

2025· preprint· en· W4411680127 on OpenAlexfundno aff
Emmanuela Ambah, Davide Elmo, Yuzhe Zhang

Bibliographic record

VenuePreprints.org · 2025
Typepreprint
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeologyFracture (geology)Rock mass classificationGeological Strength IndexGeotechnical engineering

Abstract

fetched live from OpenAlex

Synthetic rock mass (SRM) models are an invaluable component of rock engineering. Thus far, joints are implicitly modelled by adjusting rock mass properties, while flat joints are adopted for discontinuum models. This paper does not question the fundamental validity of these well-established methods for characterizing discontinuity shear strength under controlled laboratory conditions. The implicit assumption underlying this application is that surface undulation has a negligible impact on rock mass interlocking mechanisms and, consequently, on overall modelled rock mass strength. Compelling evidence suggests this assumption requires careful reconsideration. The difference in rock mass behaviour between explicitly undulated models and flat joints did not appear to exhibit any consistent patterns. Rough and flat joints did not, in the findings of this article, result in any consistent rock mass peak strength. Therefore, engineers may ascertain rock mass behaviour and strength without the use of explicit rock fracture undulations.

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)0.006
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.088
GPT teacher head0.354
Teacher spread0.266 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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