MétaCan
Menu
Back to cohort
Record W3123083977 · doi:10.1139/cjce-2019-0783

Stability analysis of double V-shaped gully embankment: dimension-reduced calculation method

2021· article· en· W3123083977 on OpenAlexaffvenue
Yun Que, Xianyong Chen, Yanyu Chen, Zhenliang Jiang, Yonghui Qiu, Said M. Easa

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsToronto Metropolitan University
FundersNatural Science Foundation of Fujian ProvinceNational Natural Science Foundation of China
KeywordsDimension (graph theory)Stability (learning theory)Factor of safetyLeveeSafety factorSlope stability analysisSlope stabilityComputer scienceGeologyMathematicsGeotechnical engineeringMathematical optimizationCivil engineeringStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Slope stability is generally analyzed by a two-dimensional (2D) method, ignoring the spatial effect (SE), which no longer meets the evaluation accuracy requirement. This problem is particularly prominent for embankments built on double V-shaped gullies (E-DVSG), which is a typical but unique topography in mountainous areas. Moreover, the technical requirements for engineers will increase sharply if a three-dimensional (3D) slope stability calculation method is adopted. To tackle this issue, pragmatically, a dimension-reduced slope stability analysis method (DR-SSAM) considering the SE of the E-DVSG was proposed and verified by a case study. Specifically, the DR-SSAM obtained the 3D safety factor of the E-DVSG by using the spatial effect curves and the safety factor from the 2D model. The results showed that this method was rational and efficient for engineering applications. In addition, the SE generation mechanism and condition, along with the influences resulting from the major factors, were addressed thoroughly.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.703
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.013
GPT teacher head0.222
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 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

Citations7
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicGeotechnical Engineering and AnalysisFrench-language works237,207