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Record W2791748077 · doi:10.1016/j.ijmst.2017.05.023

Development of a spring analogue approach for the study of pillars and shafts

2018· article· en· W2791748077 on OpenAlexaff
Amichai Mitelman, Davide Elmo, Doug Stead

Bibliographic record

VenueInternational Journal of Mining Science and Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsSpring (device)PillarStiffnessFinite element methodStructural engineeringEngineeringExcavationGeotechnical engineeringCivil engineering

Abstract

fetched live from OpenAlex

In civil and mining operations that involve ground excavation and support, the loads are distributed between the ground and support depending on their relative stiffness. This paper presents the development of conceptual single-degree-of-freedom models, which are used to derive equations for estimating displacements and stresses for ground-support interaction problems encountered in pillars in room-and-pillar mining (natural support system), and liners for circular vertical shafts (artificial support systems). For pillar assessment, mine-pillar interaction curves can be constructed using a double spring analogy. Additionally, the effectiveness of different support systems can be evaluated depending on their effect upon the mine-pillar system. For shaft design, an initial estimation of the required lining strength and thickness can be readily made based on a double ring analogue. For both problems, the results from the proposed approach compare well with those obtained by finite element numerical simulations.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
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)0.0030.001

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 head0.014
GPT teacher head0.253
Teacher spread0.240 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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