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Record W4224869272 · doi:10.1177/10775463221086351

Investigations of the effects of a passive bumper on the seismic response of base-isolated buildings: Experimental study and parameter optimization

2022· article· en· W4224869272 on OpenAlexaff
Weijian Zhong, Yanhui Liu, Oya Mercan, Fulin Zhou

Bibliographic record

VenueJournal of Vibration and Control · 2022
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsStructural engineeringBase isolationShock absorberDisplacement (psychology)Base (topology)Shock (circulatory)Nonlinear systemRange (aeronautics)SortingEngineeringShock response spectrumComputer scienceAccelerationMechanical engineeringAerospace engineeringPhysicsMathematics

Abstract

fetched live from OpenAlex

The horizontal displacement values experienced by the isolation layer of base-isolated buildings can exceed the allowable range and cause failures during the rare or very-rare earthquakes. Excessive horizontal displacements of the isolation layer may cause collisions between the building and retaining walls of the isolation ditch and even cause the collapse of the isolated building. This paper proposes a cost-effective, easy-to-build, passive bumper device, called Flexible Limit Protective Device (FLPD) in order to act as shock-absorbers. Through numerical simulations and experiments, the nonlinear behavior of the FLPD is investigated. Subsequently, through structural simulations, the effectiveness of using FLPDs is studied. The elitist non-dominated sorting genetic algorithm (NSGA-II) is used to optimize the design of FLPDs, and the response of structures equipped with optimized FLPDs are simulated numerically. The results indicate that proposed optimized FLPDs can effectively work as shock-absorbers for base-isolated structures. This paper can provide a guideline for the design of shock-absorbers.

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 score0.308
Threshold uncertainty score0.127

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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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