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Record W1928607674 · doi:10.1139/cjce-2012-0482

Seismic performance of shear wall buildings with gravity-induced lateral demands

2014· article· en· W1928607674 on OpenAlexaffvenueabout
Michael R. Dupuis, Tyler D.D. Best, Kenneth J. Elwood, Donald L. Anderson

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of British ColumbiaDouglas CollegeRead Jones Christoffersen (Canada)
Fundersnot available
KeywordsShear wallOpenSeesStructural engineeringCantileverParametric statisticsSeismic analysisShear (geology)Nonlinear systemGeotechnical engineeringEngineeringGeologySeismic loadingReinforced concretePhysics

Abstract

fetched live from OpenAlex

Architectural features and other irregularities in the gravity system which apply gravity-induced lateral demands to the seismic force resisting system are being incorporated in new buildings. These gravity-induced demands have raised concerns due to the perceived potential for a ratcheting effect to occur during seismic loading. This paper summarizes the results of a study to identify if there are behavioral trends not recognized within the scope of current building codes. To this end, a nonlinear, parametric study was conducted in OpenSees to investigate the inelastic response of concrete shear wall buildings with a range of design characteristics, including gravity-induced lateral demands. The results demonstrated that a seismic ratcheting effect can develop and amplify inelastic displacement demands. The effect is significantly more prevalent in coupled shear walls compared with cantilevered shear walls. An irregularity class to address buildings with gravity-induced lateral demands on the seismic force resisting system is proposed for the 2015 National Building Code of 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 score0.014
Threshold uncertainty score0.639

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.004
GPT teacher head0.162
Teacher spread0.158 · 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
Published2014
Admission routes3
Has abstractyes

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