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Record W4417460268 · doi:10.1038/s41598-025-05097-8

Development a practical method for calculation of the block volume and block surface in a fractured rock mass

2025· article· en· W4417460268 on OpenAlexaff
Alireza Shahbazi, Ali Saeidi, Alain Rouleau, Romain Chesnaux

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsStereographic projectionJoint (building)Surface (topology)Intersection (aeronautics)Block (permutation group theory)Rock mass classificationFracture (geology)Volume (thermodynamics)

Abstract

fetched live from OpenAlex

This study introduces a practical method for calculating the volume and surface area of blocks formed by the intersection of three persistent joint sets in a fractured rock mass. The proposed method considers the geometrical characteristics of these joint sets, demonstrating the relationship between block volume, surface area, true spacings of joint sets, and the angle between edge vectors and the normal to joint sets. The determination of required angles is facilitated through stereographic projections of joint sets using known spacing values. Validation is performed using 3DEC version 7.0 software and field data from a quarries mine, while response surface methodology (RSM) illustrates the parameter effects. Additionally, the analysis of errors from previous models identifies the reliability range of each method. The stereographic projection is further employed to analytically determine volumetric fracture intensity (P 32 ) by dividing block surface by block volume.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.280
Teacher spread0.265 · 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
GenreMethods

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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