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Record W3087590506 · doi:10.1111/risa.13593

Portfolio Seismic Loss Estimation and Risk‐based Critical Scenarios for Residential Wooden Houses in Victoria, British Columbia, and Canada

2020· article· en· W3087590506 on OpenAlexafffundabout
Katsuichiro Goda, Lizhong Zhang, Solomon Tesfamariam

Bibliographic record

VenueRisk Analysis · 2020
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSeismic riskUrban seismic riskFragilityEarthquake scenarioSeismic hazardAftershockRisk managementRisk assessmentForensic engineeringEstimationIdentification (biology)SeismologyEngineeringCivil engineeringRisk analysis (engineering)Computer scienceGeologyBusinessComputer security

Abstract

fetched live from OpenAlex

This study presents a city-wide seismic risk assessment of single-family wooden houses in Victoria, British Columbia, and Canada. The novelty and uniqueness of this study include considerations of detailed building-by-building exposure model for residential houses, current national seismic hazard model for Canada, and rigorous seismic fragility modeling of wooden houses based on nonlinear dynamic analysis of structures subjected to mainshock-aftershock sequences. A full consideration of stochastic event scenarios in probabilistic seismic risk analysis allows the identification of critical scenarios from overall regional seismic risk perspectives and provides valuable insights in informing earthquake disaster risk management actions. Outputs from the developed catastrophe model for Victoria are compared with the empirical model that was developed based on insurance claim data from the 1994 Northridge earthquake. Results of the seismic loss calculations highlight the importance of seismic resistance of the existing houses and of aftershock effects. The integrated use of the outputs from the advanced catastrophe model facilitates risk-based identification of critical earthquake scenarios, which are useful for different stakeholders for earthquake risk management purposes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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

Citations19
Published2020
Admission routes3
Has abstractyes

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