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Record W4309089466 · doi:10.1139/cjce-2021-0499

An update on the seismic categorization for seismic risk assessment of existing Canadian buildings

2022· article· en· W4309089466 on OpenAlexafffundvenueabout
Dariush Motazedian, Reza Fathi-Fazl, Zhen Cai, Farrokh Fazileh

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicStructural Health Monitoring Techniques
Canadian institutionsNational Research Council CanadaCarleton University
FundersNational Research Council Canada
KeywordsMercalli intensity scaleSpectral accelerationPeak ground accelerationAccelerationEnvironmental Seismic Intensity scaleSeismologySeismic riskSeismic hazardEarthquake scenarioCategorizationIncremental Dynamic AnalysisGeologyGround motionComputer scienceArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

An investigation has been done to update key input parameters in the recent seismic-risk-screening tools developed by the National Research Council Canada for the six seismic categories. In this study, the seismic categorization system in the recent seismic-risk-screening tools of the National Research Council Canada is extended to include Canadian buildings abroad by including threshold peak ground acceleration values, a more global set of modified Mercalli intensity, peak ground acceleration, and spectral response acceleration and peak ground acceleration relationships. The relationships among spectral response acceleration, peak ground acceleration, and peak ground velocity values have been derived using least-squares regression analyses to obtain the threshold values of both peak values associated with various modified Mercalli intensity values for each seismic category. Based on the updated relationships, the seismic categorization system is applied to 679 locations in Canada and 226 locations outside Canada.

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 score0.103
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.265
Teacher spread0.247 · 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
Published2022
Admission routes4
Has abstractyes

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