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Record W3095685676 · doi:10.1177/1369433220969023

Tuned mass dampers for the drift reduction of structures with soil effects using ant colony optimization

2020· article· en· W3095685676 on OpenAlexaff
Saeed Soheili, Hooman Zoka, Mahdi Abachizadeh

Bibliographic record

VenueAdvances in Structural Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsConcordia University
Fundersnot available
KeywordsTuned mass damperReduction (mathematics)Ant colony optimization algorithmsDisplacement (psychology)AccelerationStructural engineeringMaterials scienceDamperComputer scienceEnvironmental scienceBiological systemEngineeringMathematicsPhysicsAlgorithmBiologyGeometry

Abstract

fetched live from OpenAlex

This paper probes the effects of the Tuned Mass Damper (TMD) device on the response of a 40-story building including three types of soils and experiencing 16 far-field earthquakes. The Ant Colony Optimization (ACO) method is employed to obtain the best settings for TMD values and the objective is reducing the maximum drift of the structure considering soil structure interaction (SSI) effects. The TMD effects on the displacement and acceleration response of the structure as well as its influence on the drift response are studied. Moreover, the frequency analyses of the drift responses in addition to the story locations with the highest drifts are also investigated. It is shown that the optimized design results in considerable reduction in drifts enhancing the profits of utilizing the TMD device.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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

Citations28
Published2020
Admission routes1
Has abstractyes

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