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Record W2241590440 · doi:10.1080/19648189.2015.1064625

Investigation on time-dependent behaviour and long-term stability of underground water-sealed cavern

2015· article· en· W2241590440 on OpenAlexaff
Huanling Wang, Weiya Xu, Long Yan, Qingxiang Meng, Rubin Wang, Haibin Zhao, Wei‐Chau Xie

Bibliographic record

VenueEuropean Journal of Environmental and Civil engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of Waterloo
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsCreepGeotechnical engineeringRheologyMaterials scienceStress (linguistics)Deformation (meteorology)GeologyComposite material

Abstract

fetched live from OpenAlex

With the help of an automatic servo-controlled triaxial rock creep system, typical whole process rheological curve is obtained through laboratory experiment on the surrounding diorite rock of a large underground water-sealed cavern project. Creep experiments show that diorite exhibits obvious creep behaviour under the high in situ stress. Based on the experiment results, this article studies the creep behaviour under different stress levels, analyses the creep failure mode and mechanism, and obtains the triaxial creep properties of diorite. Using a three-dimensional nonlinear numerical method and the modified hydro-mechanical coupled Cvisc creep model, creep behaviour of the surrounding rock during the long-term operation is simulated and time-dependent stability is analysed. The results indicate that creep deformation is evident, and local stress concentration is relieved due to stress redistribution. After a period of time, the creep rate becomes stable and the deformation is stabilised. Because of its theoretical and practical significance, hydro-mechanical coupled creep analysis is important for the long-term stability and safety evaluation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

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)0.0000.000

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 head0.019
GPT teacher head0.167
Teacher spread0.149 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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