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Record W3126521454 · doi:10.1139/cjce-2019-0405

Methodology for seismic risk screening of existing buildings in Canada: Structural scoring system

2021· article· en· W3126521454 on OpenAlexaffvenueabout
Reza Fathi-Fazl, Zhen Cai, Eric Jacques, W. Leonardo Cortés-Puentes

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSeismic riskStructural systemInduced seismicityEngineeringRisk assessmentCivil engineeringComputer science

Abstract

fetched live from OpenAlex

The National Research Council Canada recently developed a Semi-Quantitative Seismic Risk Screening Tool (SQST) for seismic risk screening of existing buildings in Canada. This paper presents the structural scoring system that is part of the SQST. The structural scoring methodology is based on FEMA P-154, but has been substantially modified to suit Canadian seismicity and seismic design and construction practices. Structural scores are obtained by adding structural basic scores and applicable score modifiers. For an existing building, a structural basic score is determined by calculating the probability of collapse on the basis of a number of assumptions. Then a series of score modifiers are calculated to address the effects of applicable building characteristics on the building’s seismic performance. The structural score is compared with an acceptable structural threshold to determine whether the structural seismic risk is acceptable. The objective of the structural scoring system is to ensure an acceptable and consistent seismic risk in building portfolios while minimizing life safety threats.

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 score0.580
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.031
GPT teacher head0.227
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

Citations12
Published2021
Admission routes3
Has abstractyes

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