MétaCan
Menu
Back to cohort
Record W4319842744 · doi:10.1177/87552930221145455

Seismic resilience of typical code‐conforming RC moment frame buildings in Canada

2023· article· en· W4319842744 on OpenAlexafffundabout
Prakash S. Badal, Solomon Tesfamariam

Bibliographic record

VenueEarthquake Spectra · 2023
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOccupancyResilience (materials science)Moment magnitude scaleBuilding codeEarthquake magnitudeSeismic hazardEngineeringStructural engineeringCivil engineeringMathematics

Abstract

fetched live from OpenAlex

This article examines the baseline resilience of reinforced concrete (RC) moment frame buildings conforming to the seismic design standards of Canada. Metrics for robustness, rapidity, and resilience are evaluated to capture the system’s reliability, speed of recovery, and socioeconomic impacts. Buildings of different heights are evaluated using nonlinear time‐history analyses. Six damage states are defined as disjoint branches of an event tree depending on the building’s path to recovery. For a scenario earthquake of magnitude 7.3 magnitude at a distance of 30 km from Vancouver, the housing occupancy recovery trajectory is developed. Monte Carlo simulations are used to propagate uncertainty from seismic hazards to the building response to the lead time required for recovery. Buildings are found to maintain 50%–65% of their pre‐event housing occupancy in the immediate aftermath. The housing occupancy is restored to 90% within 2–4 months, with a shorter recovery period for low‐rise buildings, whereas the system resilience level requires 6 months to 1 year for restoration to 90%. Empirical data from the Loma Prieta (1989) and Northridge (1994) earthquakes are used to compare analytically predicted repair times. The findings from this article will facilitate the shift to resilience‐based design in 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)0.000
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 score0.182
Threshold uncertainty score0.890

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 head0.007
GPT teacher head0.201
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

Citations11
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueEarthquake SpectraSame topicSeismic Performance and AnalysisFrench-language works237,207