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Record W4382519203 · doi:10.5610/jaee.23.3_1

ANALYSIS OF CORRELATION COEFFICIENTS BETWEEN TWO ORTHOGONAL COMPONENTS OF STRONG-MOTION RECORDS

2023· article· en· W4382519203 on OpenAlexaff
Makoto Takao, H. Sato

Bibliographic record

VenueJournal of Japan Association for Earthquake Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsRandomnessGround motionCorrelation coefficientResponse spectrumCorrelationPhase (matter)MathematicsStatistical physicsPhysicsMathematical analysisGeologyStatisticsGeometrySeismologyQuantum mechanics

Abstract

fetched live from OpenAlex

With regard to horizontal components in seismic design of nuclear facilities in Japan when input ground motions are generated based on response spectrum, simulated ground motions composed of two mutually orthogonal components are generated for one target response spectrum. In such a case, the characteristics of the two ground motions are distinguished by the randomness of the phase angle given by the uniformly distributed random numbers and/or the difference in the phase characteristics of the two different components of the observation records. On the other hand, US Nuclear Regulatory Commission standards state that, when performing seismic response analysis for nuclear facilities using the method of simultaneous input of three earthquake ground motion components, the three components should be shown to be statistically independent of each other, and an absolute value for correlation coefficient as proposed by Chen (1975) is introduced as a criterion. In this paper, focusing on the correlation coefficient by Chen (1975), we found the correlation coefficients between two orthogonal components in records of observed strong motions in Japan after 2000, and performed statistical analyses of these correlation coefficients, then analyzed the impact of various earthquake-related parameters upon them. In addition, we actually generated simulated ground motions via a common practice based on the response spectrum and analyzed their correlation coefficients.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.015
GPT teacher head0.242
Teacher spread0.227 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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