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Record W3025742438 · doi:10.1080/19648189.2020.1763840

Analytical method of stability analyses of toppling rock slopes subjected to flexural toppling failure damage

2020· article· en· W3025742438 on OpenAlexaff
Weijie Zhou, Weiya Xu, Haibin Xiao, Wei‐Chau Xie

Bibliographic record

VenueEuropean Journal of Environmental and Civil engineering · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversity of Waterloo
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsGeologyGeotechnical engineeringFlexural strengthFailure mode and effects analysisHydrogeologySlope stabilityShear (geology)LandslideMagnetic dipInstabilityPetrologyStructural engineeringEngineering

Abstract

fetched live from OpenAlex

The mechanism of toppling deformation and failure is important in the stability analysis of anti-dip layered rock slopes, especially in a complex hydrogeological environment. In this paper, the flexural failure model is proposed for describing the mechanical behavior of toppling rock slope using the cantilever beam theory. A method for safety evaluation, considering the action of hydrodynamic force from groundwater, is developed. This model is then applied to conduct a case study of a toppling rock slope at a hydropower station on the Lancang River. The results show that the variation of reservoir water level increases both the tensile stresses and the deflections of rock slabs, which is manifested macroscopically by flexural displacements and failure. Based on the failure surfaces, the slope can be divided into three parts: toppling zone, creep zone, and shear zone. The failure mode of toppling rock slope is a combination of shear and toppling. Under the combined action of earthquake and rapid water drawdown, the toppling rock slope is at risk of shear instability at the leading edge; hence monitoring should be strengthened and prevention measures should be taken.

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 score0.511
Threshold uncertainty score0.796

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 communication0.0000.000
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 head0.018
GPT teacher head0.231
Teacher spread0.213 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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