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Record W2165845838 · doi:10.1193/061414eqs086m

Damping Reduction Factors for Crustal, Inslab, and Interface Earthquakes Characterizing Seismic Hazard in Southwestern British Columbia, Canada

2015· article· en· W2165845838 on OpenAlexafffundabout
Poulad Daneshvar, Najib Bouaanani, Katsuichiro Goda, Gail M. Atkinson

Bibliographic record

VenueEarthquake Spectra · 2015
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsWestern UniversityColumbia CollegePolytechnique Montréal
FundersCenter for Spintronics Research Network, Tohoku UniversityNatural Sciences and Engineering Research Council of CanadaUC Berkeley College of ChemistryStrong
KeywordsSeismologySeismic hazardGeologySubductionSeismic riskHazardDisplacement (psychology)Event (particle physics)Seismic analysisTectonicsGeotechnical engineeringPhysics

Abstract

fetched live from OpenAlex

High‐damping displacement spectra and corresponding damping reduction factors ( η ) are important ingredients in seismic design and analysis of structures equipped with seismic protection systems, as well as in displacement‐based design methodologies. In this study, we investigated η factors for three types of earthquake characterizing seismic hazard in southwestern British Columbia, Canada: shallow crustal, deep inslab, and interface subduction. We used a large and comprehensive database including records from recent relevant earthquakes, such as the 2011 Tohoku event. Our key observations were as follows: (1) there is negligible dependence of η on soil class; (2) there is significant dependence of η on the frequency content and duration of ground motions that characterize the different record types, and (3) η is dependent on period, particularly for inslab events. Period‐dependent equations were proposed to predict η for damping ratios between 5% and 30% corresponding to the three event types.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.207
Teacher spread0.192 · 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

Citations24
Published2015
Admission routes3
Has abstractyes

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