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Record W2911176181 · doi:10.3166/i2m.17.563-572

Stability analysis of accumulation body based on monitoring results of deep displacement

2018· article· en· W2911176181 on OpenAlexvenueno aff
Jianhui Dong, Shiming Wan, Qihong Wu

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2018
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsnot available
Fundersnot available
KeywordsDisplacement (psychology)Stability (learning theory)GeologyComputer scienceEnvironmental sciencePsychologyMachine learningPsychoanalysis

Abstract

fetched live from OpenAlex

The stability of accumulation body commonly adopts a qualitative and quantitative analysis method, which considers the geological boundary conditions, calculation model and mechanical parameters of rock mass and so on This causes the stability results be limited, so it is unlikely to make a real-time quantitative evaluation on the accumulation body.This paper proposes a method which avoids these problems, and timely evaluates the accumulation stability based on the monitoring results in the process of the deformation development.It involves two parameters, i.e. the integrity index S(i) and the destructive index S(d), used to evaluate quantitatively the dynamic change of accumulation body when the progressive destroy occurs from the bottom up.Take talus in front of the dam of Zippingpu hydraulic project as an example, this method not only measures the S(i) and S(d) during movements of its reservoir water level, but also evaluates the impact of Wenchuan earthquake on the accumulation body.it is proved by an example that this method is convenient, practical and feasible.. RÉSUMÉ.Afin d'analyser la stabilité du corps d'accumulation, généralement une méthode d'analyse qualitative et quantitative est adopté e, qui prend en compte les conditions des limites gé ologiques, le modè le de calcul et les paramè tres mé caniques de la masse rocheuse, etc. Cela entraî ne une limitation des ré sultats de stabilité , de sorte qu'il est peu probable qu'une é valuation quantitative en temps ré el du corps d'accumulation soit ré alisé e. Cet article propose une mé thode qui évite ces problèmes et évalue en temps voulu la stabilité de l'accumulation en fonction des ré sultats de la surveillance dans le processus du dé veloppement de la dé formation.Il implique deux paramè tres, à savoir l'indice d'inté grité S (i) et l'indice de destruction S (d), utilisé s pour é valuer quantitativement le changement dynamique du corps d'accumulation lorsque la destruction progressive a lieu de bas en haut.En prenant comme exemple le talus devant le barrage du projet hydraulique de Zippingpu, cette mé thode mesure non seulement les indices S (i) et S (d) lors des mouvements du niveau de son ré servoir, mais é value é galement l'impact du séisme de Wenchuan sur le corps d'accumulation.Un exemple montre que cette mé thode est simple, pratique et ré alisable.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.049
GPT teacher head0.324
Teacher spread0.276 · 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 designObservational
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

Citations3
Published2018
Admission routes1
Has abstractyes

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