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Residual Drift Response of SMRFs and BRB Frames in Steel Buildings Designed according to ASCE 7-05

2010· article· en· W2017940924 on OpenAlexafffund
Jeffrey Erochko, Constantin Christopoulos, Robert Tremblay, Hyunhoon Choi

Bibliographic record

VenueJournal of Structural Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsPolytechnique MontréalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaSENSHIN Medical Research Foundation
KeywordsResidualNonlinear systemFraming (construction)Structural engineeringMoment (physics)GeologyMathematicsEngineeringPhysicsAlgorithm

Abstract

fetched live from OpenAlex

A recent study has shown that residual drifts after earthquakes that are greater than 0.5% in buildings may represent a complete loss of the structure from an economic perspective. To study the comparative residual drift response of special moment-resisting frames (SMRFs) and buckling-restrained braced (BRB) frames, buildings between 2 and 12 stories in height are designed according to ASCE 7-05 and investigated numerically. This investigation includes pushover analyses as well as two-dimensional nonlinear time-history analyses for two ground motion hazard levels. The two systems show similar peak drifts and drift concentration factors. The BRB frames experience larger residual drifts than the SMRFs; however, the scatter in the residual drift results is large. Expressions are proposed to estimate the residual drifts of these systems as a function of the expected peak drifts, the initial recoverable elastic drift, and the drift concentration factor of each system. When subjected to a second identical earthquake, both framing systems experienced larger-than-expected drifts when an initial drift greater than 0.5% was present.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.212
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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

Citations486
Published2010
Admission routes2
Has abstractyes

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