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Record W4225156746 · doi:10.1155/2022/7574272

Finite-Element Analysis of Vertical Displacement of Laminated Rubber Bearing under Earthquake Action for Disaster Resilience of the Smart City

2022· article· en· W4225156746 on OpenAlexvenueno aff
Denglian Yang, Yanli Liu

Bibliographic record

VenueJournal of Advanced Transportation · 2022
Typearticle
Languageen
FieldEngineering
TopicElasticity and Material Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsBearing (navigation)Structural engineeringNatural rubberFinite element methodDisplacement (psychology)Resilience (materials science)Deformation (meteorology)Shear (geology)Vertical displacementGeotechnical engineeringGeologyEngineeringMaterials scienceComputer scienceComposite material

Abstract

fetched live from OpenAlex

With the construction and development of smart cities, higher requirements have been put forward for the prediction and control of major natural disasters. For the prevention of earthquake disasters in super high-rise buildings, the rubber bearings of high-rise seismic isolation structures may have the risk of tensile damage in high-intensity areas, which has always been an urgent problem to be solved. In this paper, a unidirectional horizontal compression-shear experiment with 400% large deformation was conducted on the laminated rubber bearing (LNR500), and the relationship curve of vertical displacement with horizontal displacement of the bearing was obtained. According to the experimental data, an ideal elastic-plastic principal structure model was selected for the steel and a Yeoh principal structure model was selected for the rubber material, and the finite element analysis was carried out for the bearing. The time course curves of displacement, velocity and acceleration of the vertical and horizontal deformation of the laminated rubber bearing under the earthquake were obtained.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.195

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 head0.017
GPT teacher head0.254
Teacher spread0.238 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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