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Record W2801083327 · doi:10.1061/9780784481325.011

Seismic Performance of Reinforced Concrete Frame Buildings Equipped with Friction Dampers

2018· article· en· W2801083327 on OpenAlexafffundabout
Ali Naghshineh, Amina Kassem, Anne-Gaelle Pilorge, Oscar Romero Galindo, Ashutosh Bagchi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDamperDissipationStructural engineeringRetrofittingFrame (networking)Seismic retrofitEngineeringNonlinear systemSeismic loadingSeismic analysisSlip (aerodynamics)Reinforced concreteMechanical engineering

Abstract

fetched live from OpenAlex

Friction dampers dissipate energy through the friction that develops between two solid bodies sliding relative to one another. When a major earthquake occurs, the friction dampers slip at a predetermined load before yielding occurs in the members of a frame, which dissipate a major part of the energy. It saves the initial cost of new construction or retrofitting of existing buildings, with a very high energy dissipation. In this paper, the seismic performance of a 14 story moderately ductile concrete frame, designed based on the current edition of the National Building Code of Canada (NBCC) has been studied with and without friction dampers. The building is assumed to be located in Victoria, BC, in the western part of Canada. Nonlinear dynamic time history using a set of ground motion records has been performed to determine their effects. The costs of the building frame with and without friction dampers have been evaluated for comparison. The seismic performance of the building in both cases have been evaluated using nonlinear dynamic analysis and the building with dampers has been found to achieve the desired level of performance without any significant damage in the frame.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.196
Teacher spread0.190 · 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

Citations4
Published2018
Admission routes3
Has abstractyes

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