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Record W4283216080 · doi:10.1139/cjce-2021-0603

Seismic performance assessment of conventional construction concrete moment-resisting frame buildings in Canada using the FEMA P695 methodology

2022· article· en· W4283216080 on OpenAlexafffundvenueabout
Farrokh Fazileh, Sadegh Khosravi, Abouzar Dolati, Reza Fathi-Fazl, Murat Saatçioğlu

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of OttawaNational Research Council Canada
FundersNational Research Council CanadaNational Institute of Standards and Technology
KeywordsStructural engineeringSeismic analysisMoment (physics)Building codeFrame (networking)Seismic loadingIncremental Dynamic AnalysisEngineeringDuctility (Earth science)Structural systemMechanical engineering

Abstract

fetched live from OpenAlex

National Building Code of Canada (NBC) specifies 45 Seismic Force Resisting Systems to ensure the safety of building structures. Conventional construction concrete moment-resisting frame (CC-CMF) is one of these systems whose seismic response has not been systematically studied in Canada. This paper is an attempt to study the seismic performance of this system using the FEMA P695 methodology. In this regard, different archetype configurations were developed and analyzed through nonlinear static and dynamic analyses. Performance assessment results show that the CSA and NBC requirements for CC-CMF system represent the lower bound of values for seismic force modification factors, and conservatively meet life safety objectives presented in the NBC. Furthermore, different scenarios were considered for identifying ductility-related seismic force modification factor from pushover curves. The effect of height and gravity load levels on seismic force modification factors and collapse margin ratios are also presented by comparing archetypes having different configurations.

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)0.000
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 score0.221
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.019
GPT teacher head0.226
Teacher spread0.207 · 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

Citations6
Published2022
Admission routes4
Has abstractyes

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