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Record W2791237239 · doi:10.17580/gzh.2018.02.04

Tunnel support in weak rocks using self-fastening rock bolts SZA

2018· article· en· W2791237239 on OpenAlexaboutno aff
S. S. Neugomonov, П. В. Волков, A. A. Zhirnov

Bibliographic record

VenueGornyi Zhurnal · 2018
Typearticle
Languageen
FieldEngineering
TopicTunneling and Rock Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsGeotechnical engineeringRock boltGeology

Abstract

fetched live from OpenAlex

The article considers the issue of ensuring stability of mine tunnels in weak rocks using the reinforced combined support on the basis of self-fastening rock bolts (SZA) in Artemyevsk Mine of Vostoktsvetmet. The experience of Australian and Canadian mines show that support of mine tunnels in heavily deformable and rockburst-hazardous rocks should possess high energy absorption capacity, i.e., the support must be strong enough to withstand high loads, and at the same time flexible enough to permit a slight displacement of the walls of the mine. Aiming to estimate load-bearing capacity of concrete lining, it was decided to estimate wave impact of blasting (seismic vibration) expressed in terms of explosion output energy. The results of the full-scale trials show that the combination support technology in weak rocks using self-fastening friction-type rock bolts (SZA model) and MasterRoc STS 1510 shotcrete ensures stability of surfaces in tunnels and is applicable in Artemyevsk mine without signifi cant change in the list of available drilling, rockbolting and shotcreting machines, which favors transition to the described design of support.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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 designBench or experimental
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

Citations6
Published2018
Admission routes1
Has abstractyes

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