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Sustainable Decision in using Tuned Liquid Column Dampers for the Structural Energy Dissipation of Earthquake

2024· article· en· W4406521767 on OpenAlexaff
Basil Ibrahim, Moussa Leblouba, Samer Barakat, Hamdy M. Mohamed, Hend Elzefzafy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsDissipationDamperStructural engineeringColumn (typography)Seismic energyEnergy (signal processing)Computer scienceEngineeringGeologyPhysics

Abstract

fetched live from OpenAlex

This paper introduces a novel simplified modeling approach for tuned liquid column dampers (TLCDs) designed to enhance the response control of single degree of freedom (SDOF) systems. The study focuses on the operational principles of TLCDs, which utilize the inertia of liquid within a column to dissipate energy and mitigate vibrations induced by dynamic loads, such as those from wind and seismic activity. Through the linearization of the governing equations of motion using MATLAB Simulink, the effectiveness of TLCDs is analyzed under the influence of El Centro ground motion. A comparative case study evaluates the structural response of an SDOF system both with and without the integration of a TLCD, demonstrating the significant benefits of employing these dampers in reducing acceleration and displacement. Results indicate that TLCDs dissipate motion significantly faster, achieving an 80% reduction in structural displacement within the first 10 seconds of El Centro ground motion. The findings suggest that properly tuned TLCDs can substantially enhance the stability and safety of structures subjected to dynamic forces.

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 score0.409
Threshold uncertainty score0.150

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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