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Record W2334203653 · doi:10.1061/9780784479742.112

Seismic Fragility Analysis for Semi-Actively Controlled Structures Using MR Dampers

2016· article· en· W2334203653 on OpenAlexaff
Jong Wha Bai, Young‐Jin Cha

Bibliographic record

VenueGeotechnical and Structural Engineering Congress 2016 · 2016
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFragilitySeismic hazardReliability (semiconductor)Incremental Dynamic AnalysisStructural engineeringDamperSeismic analysisMagnetorheological fluidHazardReliability engineeringSeismic loadingEngineeringComputer scienceCivil engineering

Abstract

fetched live from OpenAlex

This paper focuses on seismic fragility analysis of a semi-actively controlled high-rise building with magnetorheological (MR) dampers. Many control devices and methodologies to reduce seismic damage of the building structures have been developed. However, little research has been carried out to evaluate seismic fragility of controlled buildings with a broad range of seismic hazard levels. In this study, a 9-story steel moment-resisting framed (MRF) building is selected as a case study structure, and an advanced decentralized output feedback polynomial control (DOFPC) algorithm is applied. Seismic fragility relationships are investigated using a system reliability approach and are compared those results based on 41 earthquake ground motions with two different hazard levels. The preliminary results of fragility estimate based on the system reliability approach show that the differences on seismic performance for multiple performance-based control designs against 41 earthquake motions match well with the initial objectives of performance-based design.

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 score0.432
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.226
Teacher spread0.216 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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