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Record W2150403302 · doi:10.1002/tal.1104

On the efficiency of semi‐active smart structures: self‐regulating MR dampers control system for tall buildings

2013· article· en· W2150403302 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Structural Design of Tall and Special Buildings · 2013
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDamperStructural engineeringController (irrigation)Smart materialStructural systemEngineeringVibration controlMagnetorheological fluidControl (management)Control engineeringVibrationComputer scienceMaterials scienceArtificial intelligence

Abstract

fetched live from OpenAlex

SUMMARY Many severe dynamical loadings such as earthquakes and strong winds may subject to structural systems during their lifetime and lead to changes in structural characteristics. Hence, employing an adaptive control strategy that can deal with these alterations compound with design of the structural elements would undoubtedly be the most effective alternative design for the old‐fashioned design methods, which are relatively inefficient in response to these unforeseen conditions. In the current study, benefits of employing the modern control systems for design of tall buildings in comparison with the uncontrolled traditionally designed structures are thoroughly investigated. To contract the vibrational responses due to seismic excitations, the innovative direct‐modulating semi‐active controller is designed for magneto‐rheological dampers, which are installed in an 11‐storey sample building converting it to a smart structure. Copyright © 2013 John Wiley & Sons, Ltd.

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.

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 score0.698
Threshold uncertainty score0.564

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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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