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Record W2901502563 · doi:10.25071/10315/35398

Dynamic Testing System For Rocks Under In Situ Stresses

2018· article· en· W2901502563 on OpenAlexaff
Wei Yao, Kaiwen Xia

Bibliographic record

VenueProgress in Canadian Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicRock Mechanics and Modeling
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIn situComputer scienceGeologyChemistry

Abstract

fetched live from OpenAlex

Rocks may be subjected to dynamic disturbances while under high in situ stresses. When disturbed by dynamic loads from blasting, seismicity or rockbursts, the underground structures would be vulnerable to failure. Depending on the distance from the underground opening, the in situ stress states change from hydrostatic in the far-field, to triaxial in the intermediate distance, and to the pre-tension nearby the opening. Thus, SHPB testing system is further adjusted with confining pressure system into dynamic testing system of rocks under different in situ states. In the experiment with this dynamic testing system, the Brazilian disc rock specimens are first subjected to pre-stresses simulating in-situ stresses underground (including pre-tension, hydrostatic confinement, and triaxial confinement) and then loaded dynamically using the modified SHPB system. The dependence of dynamic tensile strength of the rock material on the static pre-stress and loading rate is investigated. These experimental results will be of great importance in the design and safety of underground rock engineering projects.

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)0.001
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 score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.002

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 head0.231
Teacher spread0.217 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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