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Record W2891047213 · doi:10.1155/2018/8193638

Damage Evaluation for Rock Burst Proneness of Deep Hard Rock under Triaxial Cyclic Loading

2018· article· en· W2891047213 on OpenAlexaff
Shuang You, Hongguang Ji, Zijian Zhang, Chenghan Zhang

Bibliographic record

VenueAdvances in Civil Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of British Columbia
FundersChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsRock burstGeologyGeotechnical engineeringDissipationExcavationMining engineeringEngineering

Abstract

fetched live from OpenAlex

High stress and strong excavation disturbance are the main causes of dynamic disasters, rock burst in deep hard rocks, and are more frequent and violent than those in shallow, which seriously restricts the deep mining. Given rock burst encountered in deep mining of Lingnan metal mines, the optimized triaxial cyclic loading and unloading tests are designed to characterize the performance of rock failure and to evaluate the rock burst proneness. The correlation between elastic energy index and damage evolution is built, and rock burst proneness in each status is analyzed; furthermore, the dissipation energy in the failure process of deep rocks is explicated. In this paper, the law that the elastic energy index via damage increases is drawn. In terms of the dynamic disaster conditions in the deep rock, the identification approach for the damage zone of the rock burst is established.

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 score0.002
Threshold uncertainty score0.003

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.271
Teacher spread0.252 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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