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Record W4200352044 · doi:10.1785/0120210114

A Stochastic Model for Simulating Vertical Pulseless Near-Fault Seismic Ground Motions

2021· article· en· W4200352044 on OpenAlexaff
X.Z. Cui, Yong Xu Liu, Han Hong

Bibliographic record

VenueBulletin of the Seismological Society of America · 2021
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsWestern University
Fundersnot available
KeywordsGeologySeismologyGround motionEarthquake simulationPeak ground accelerationSlip (aerodynamics)Spectral accelerationAccelerationFault (geology)Spectral densityEarthquake shaking tableDeformation (meteorology)GeodesyGeotechnical engineeringEngineeringStatisticsMathematics

Abstract

fetched live from OpenAlex

ABSTRACT The vertical near-fault seismic ground-motion component can cause significant structural deformation and damage, which can be evaluated from time history analysis using actual or synthetic ground-motion records. In this study, we propose a new stochastic model for the vertical pulseless near-fault ground motions that depends on earthquake magnitude, rupture distance, and site condition. The proposed model is developed based on the time–frequency characteristics of 606 selected actual vertical record components in strike-slip earthquakes. The use and validation of the model are presented using simulated records obtained by two simulation techniques. For the validation, the statistics of time–frequency-dependent power spectral acceleration estimated from the simulated records using the proposed stochastic model are compared with those from the actual records and the ground-motion models available in the literature.

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 score0.697
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0000.000

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 head0.018
GPT teacher head0.235
Teacher spread0.217 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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