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Record W4293593443 · doi:10.3390/su141710720

Seismic Performance of a Sliding Isolation Bridge System with a New Spring Re-Centering Device

2022· article· en· W4293593443 on OpenAlexaff
Pengcheng Yin, Jianguo Wang, Yutao Pang

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsStructural engineeringGirderSpring (device)Displacement (psychology)ResidualBridge (graph theory)PierParametric statisticsPylonFinite element methodEngineeringDeformation (meteorology)Mechanism (biology)Geotechnical engineeringGeologyComputer sciencePhysicsMathematics

Abstract

fetched live from OpenAlex

Residual displacements between the girder and piers were observed in previous strong earthquakes. These are caused by the limited re-centering capacity of sliding isolation bearings. With this concern, a spring re-centering device is proposed to improve the re-centering capacity of sliding isolation systems. The working mechanism is illustrated, and the force–deformation relationship of this device was investigated in theoretical, experimental, and finite element methods. An extra-dosed multi-pylon cable-stayed bridge was introduced to demonstrate the re-centering effect. The results show that this spring re-centering device could slightly mitigate the seismic forces and significantly mitigate the residual displacement between the girder and piers. After that, the parametric analysis was conducted to investigate the effect of critical parameters of re-centering bearings on seismic performances.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.211
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

Citations3
Published2022
Admission routes1
Has abstractyes

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