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Record W4280596383 · doi:10.3390/app12105084

Effects of Slope Angle on Toppling Deformation of Anti-Dip Layered Rock Slopes: A Centrifuge Study

2022· article· en· W4280596383 on OpenAlexaff
Da Zheng, Hongke Zhou, Hang Zhou, Fangzhou Liu, Qiang Chen, Zhanglei Wu

Bibliographic record

VenueApplied Sciences · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversity of Alberta
FundersNational Key Research and Development Program of ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection
KeywordsGeologySlippingGeotechnical engineeringDeformation (meteorology)Magnetic dipCentrifugeSlip (aerodynamics)Geometry

Abstract

fetched live from OpenAlex

To reveal the impact of slope angle change on toppling deformation, this paper takes the dam toppling deformation of Gushui Hydropower Station based at Lancang River as an example, based on which three groups of models with different slope angles are conceptualized and designed. Through the centrifuge test, the evolution of toppling deformation of a counter-tilt layered slope under different slope angles was simulated. The research results show that: (1) The steeper the slope angle, the shorter the cumulative time required for such deformation is, the larger the toppling deformation will be, and it is more likely that multi-stage fracture zones will occur during the toppling process. (2) The toppling deformation process can be summarized as four stages. (3) The toppling deformation mainly occurs above the datum surface of the toppling fracture; the angle between the datum surface and the normal of the layer is between 12° to 16°. This value shall not change as the slope angle changes. (4) The lesser the slope angle is, the more likely the toppling deformed slope will be experience overall slipping instability. while the steeper the slope angle is, the greater the possibility of collapse after the rock mass falls.

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)0.001
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 score0.578
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.217
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 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

Citations12
Published2022
Admission routes1
Has abstractyes

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