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Record W3132891376 · doi:10.1139/cjce-2019-0349

Methodology for seismic risk screening of existing buildings in Canada: Non-structural component scoring system

2021· article· en· W3132891376 on OpenAlexaffvenueabout
W. Leonardo Cortés-Puentes, Zhen Cai, Reza Fathi-Fazl

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsComponent (thermodynamics)Seismic riskRisk assessmentOccupancyEarthquake scenarioEngineeringInduced seismicityCivil engineeringReliability engineeringForensic engineeringSeismic hazardRisk analysis (engineering)Computer science

Abstract

fetched live from OpenAlex

The National Research Council of Canada (NRC) has recently developed a Semi-Quantitative Seismic Risk Screening Tool (SQST) to supersede the 1993 NRC Manual for Screening of Buildings for Seismic Investigation. The proposed screening tool incorporates a methodology for estimating global seismic risk of existing buildings associated with failure of their non-structural components. The methodology assesses global seismic risk using a qualitative yet comprehensive scoring system. The scoring system consists of a global non-structural component score and acceptable threshold scores. The global score is based on the most critical components. It combines a basic score with score modifiers for key parameters affecting the seismic response of non-structural components. The acceptable threshold score is based on the building’s consequence of failure, the building’s importance category, and the component factor. The scoring system is calibrated to be consistent with the seismic risk acceptance criteria previously developed for preliminary seismic risk screening of existing buildings, based on Canadian seismicity, building age, remaining occupancy time, and consequences of failure.

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: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.028
GPT teacher head0.224
Teacher spread0.195 · 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

Citations7
Published2021
Admission routes3
Has abstractyes

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