MétaCan
Menu
Back to cohort
Record W4206464191 · doi:10.1139/cjce-2021-0256

Semi-quantitative seismic risk screening tool for existing buildings in Canada

2022· article· en· W4206464191 on OpenAlexafffundvenueabout
Reza Fathi-Fazl, Zhen Cai, W. Leonardo Cortés-Puentes, Farrokh Fazileh

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete Corrosion and Durability
Canadian institutionsNational Research Council Canada
FundersSponsored Research and Industrial ConsultancyNational Research Council Canada
KeywordsSeismic riskRisk assessmentRanking (information retrieval)EngineeringRisk analysis (engineering)Civil engineeringForensic engineeringConstruction engineeringComputer scienceComputer securityBusiness

Abstract

fetched live from OpenAlex

The National Research Council of Canada (NRC) recently developed a semi-quantitative seismic risk screening tool (SQST) for existing buildings in Canada. The SQST is intended to supersede the Manual for Screening of Buildings for Seismic Investigation developed by the NRC in the early 1990s. The SQST consists of three key components: (i) a structural scoring system that quantitatively assesses the structural seismic risk based on probability of collapse; (ii) a nonstructural component scoring system that qualitatively assesses the seismic risk of nonstructural components based on seismic demand; and (iii) a ranking procedure that prioritizes potentially hazardous buildings for seismic evaluations and possible upgrading. The intent of the SQST is to inexpensively identify and exempt buildings with acceptable life safety risk and optimize the allocation of resources to assess the seismic risks to portfolios of buildings. Seismic screening with the SQST can be completed with either paper-based screening forms or a web-based application. The applicability of the SQST was demonstrated with a pilot study for 33 existing buildings across 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)0.001
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 score0.598
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.016
GPT teacher head0.202
Teacher spread0.186 · 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

Citations5
Published2022
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Journal of Civil EngineeringSame topicConcrete Corrosion and DurabilityFrench-language works237,207