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Record W2006749851 · doi:10.1139/l05-049

A new concept of isolation bearings for highway steel bridges using shape memory alloys

2005· article· en· W2006749851 on OpenAlexvenueno aff
Eunsoo Choi, Tae-Hyun Nam, Baik-Soon Cho

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2005
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsnot available
Fundersnot available
KeywordsStructural engineeringBearing (navigation)Deformation (meteorology)Natural rubberDeckStiffnessShape-memory alloyDisplacement (psychology)ResidualMaterials scienceTension (geology)EngineeringComposite materialComputer scienceCompression (physics)

Abstract

fetched live from OpenAlex

Conventional lead–rubber bearings (LRB) may have a problem of instability and unrecovered deformation with a strong ground motion. To improve the problems, this study proposed a new concept of an isolation device in which shape memory alloy wires were incorporated in an elastomeric bearing. This study illustrated the behavior of shape memory alloy in tension and discussed the variation of stiffness and stress on the hyteresis curves. A three-span continuous steel bridge was used for seismic analyses to compare the performance of lead–rubber bearings with the proposed bearings. This study showed that large residual deformation of LRB occurred even with a weak ground motion of peak ground acceleration (PGA) of 0.2g. The proposed bearings effectively limited the relative displacement of the deck when tested for the strong ground motions and almost recovered the original undeformed shape. However, the proposed bearing increases the demand on bridge columns compared with the LRB.Key words: shape memory alloy, lead–rubber bearings, seismic bridge analysis, isolation device, residual deformation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.018
GPT teacher head0.221
Teacher spread0.204 · 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 designBench or experimental
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

Citations89
Published2005
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Civil EngineeringSame topicShape Memory Alloy TransformationsFrench-language works237,207