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Tunnel rockbursts induced by dynamic disturbances: mechanism and mitigation

2024· article· en· W4398203399 on OpenAlexafffund
Jian Deng, Fengqiang Gong, Hehua Zhu

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2024
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaGovernment of Canada
KeywordsMechanism (biology)Forensic engineeringEnvironmental scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Rockbursts are characterized by violent rock fractures and pose a significant threat to hard-rock tunnels, potentially resulting in casualties and damage to excavation spaces. Globally recognized as one of the least understood and most feared challenges in underground excavations, rockbursts are often triggered by dynamic disturbances such as engineering activities or nearby vibrations. This study conceptualizes rockbursts as dynamic buckling or instability issues inherent in rock structures. It specifically investigates the mechanism of tunnel rockbursts induced by ambient blasting. The derivation of the governing equation of motion, which incorporates shear deformation and rotary inertia of the rock column, results in coupled Mathieu equations. By employing the proposed numerical method, the conditions triggering rockburst were established using instability diagrams. The study examines the effects of static components, dynamic loading, and frequency on a tunnel example, revealing that the amplitude and frequency of dynamic disturbances are critical in influencing the occurrence of tunnel rockbursts through perturbation effects and parametric resonance mechanisms. These insights offer valuable understanding into the mechanisms, mitigation, and control of tunnel rockbursts.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.478

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 communication0.0000.001
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 head0.007
GPT teacher head0.184
Teacher spread0.177 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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