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Record W2385386288

Random Seismic Response and Dynamic Reliability Analysis of Frame with Prestressed Anchors for Slope Stability

2015· article· en· W2385386288 on OpenAlexaff
Dong Jian-hu

Bibliographic record

VenueZhongguo gonglu xuebao · 2015
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsStructural engineeringReliability (semiconductor)Frame (networking)Response analysisStability (learning theory)Seismic loadingMonte Carlo methodResponse spectrumEngineeringIncremental Dynamic AnalysisSeismic analysisGeotechnical engineeringComputer sciencePower (physics)MathematicsStatistics
DOInot available

Abstract

fetched live from OpenAlex

In order to simplify the random seismic response analysis and dynamic reliability calculation method of the frame with prestressed anchor for slope anchor structure,and improve the calculation efficiency,the pseudo-excitation method was adopted to analyze random seismic response of anchor for slope anchor structure,and the power spectrum of seismic response of the frame with prestressed anchor was obtained.The improved dynamic reliability calculation method was presented,an engineering example was conducted to verify the effectiveness and reliability,and the proposed method was compared with Monte-Carlo method.The results show that the physical concept of proposed analysis model is clear,which is a rapid,accurate and practicalanalysis method.The proposed method lays the theoretical basis and provides a new calculation way for dynamic reliability calculation and random seismic response analysis of frame with prestressed anchor for slope stability.Random seismic response increases gradually with the slope height,and the extremum occurs at the upper part of slopes.Anchorage structure dynamic response peak increases with the increase of reliability index.

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)0.001
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: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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