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Record W2066557620 · doi:10.1155/2012/251543

Proposed Configurations for the Use of Smart Dampers with Bracings in Tall Buildings

2012· article· en· W2066557620 on OpenAlexaff
Alberto Zasso, Ferruccio Resta

Bibliographic record

VenueSmart Materials Research · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsDamperLeverStructural engineeringEngineeringFlexural strengthController (irrigation)Mechanism (biology)Reduction (mathematics)VibrationVibration controlMechanical engineeringAcoustics

Abstract

fetched live from OpenAlex

This paper presents wind-induced response reduction in a very slender building using smart dampers with proposed bracings-lever mechanism system. The building presents a case study of an engineered design that is instructive. The paper shows that shear response and flexural response of tall buildings present two very different cases for vibration suppression. Smart dampers are implemented optimally in the building to reduce its response in the lateral directions for both structural safety and occupant comfort concerns. New bracings-lever mechanism configurations are proposed for the dampers to improve their performance. The study shows how the proposed configurations can enable application to flexural response and scenarios where the interstory drift is not enough for dampers to work effectively. In addition, a decentralized bang-bang controller improved the performance of the smart dampers.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.101
GPT teacher head0.327
Teacher spread0.225 · 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

Citations12
Published2012
Admission routes1
Has abstractyes

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