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Record W4409799990 · doi:10.11159/icgre25.136

Stability Assessment of Vertical Remnant Pillars In Cut and Fill Mining Method with Numerical Modelling

2025· article· en· W4409799990 on OpenAlexvenueno aff
Sumant Mohanto, Aryan Upare, Santosh Murali, Sandeep Panchal, Amrites Senapati

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicGeotechnical and Geomechanical Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsStability (learning theory)Computer scienceNumerical modelsGeologyComputer simulationMachine learningSimulation

Abstract

fetched live from OpenAlex

Cut and fill mining method involves extraction of orebody in horizontal slices in weak rock formations.The void created as a result of excavation is backfilled and vertical pillars are left at intervals if the overlying roof is weak.This method is advantageous in terms of ore recovery and safety, making it a preferred method for steeply dipping orebodies in challenging underground environments.These remnant pillars left intact plays a crucial role in supporting the overlying strata and protecting a safe environment for ore exploitation.The stability of these pillars is important since pillar failure results in catastrophic consequences including subsidence or even loss of lives.Hence, the pillar dimension is one of the important parameters which governs the stability of the overlying strata in cut and fill mining method.The present study focuses on the assessment of vertical pillar stability with 5 m 5 m dimension left intact throughout the entire depth of orebody in cut and fill post pillar mining method considering three-dimensional finite element analyses.Based on the simulation results obtained from numerical modeling, it was found that the pillar dimension of 5 m 5 m was stable enough for the considered geo-mining condition with factor of safety above unity.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.735

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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicGeotechnical and Geomechanical EngineeringFrench-language works237,207