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Damping by SMA in Civil Engineering Structures

2008· article· en· W2051576724 on OpenAlexaff
F. Martorell, Vicenç Torra, A. Isalgué, M.L. Perea, Patrick Terriault, F.C. Lovey

Bibliographic record

VenueAdvances in science and technology · 2008
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsSMA*Shape-memory alloyDamperThermoelastic dampingMaterials scienceStructural engineeringHysteresisWork (physics)Bridge (graph theory)Mechanical engineeringEngineeringComposite materialComputer scienceThermalPhysicsThermodynamics

Abstract

fetched live from OpenAlex

The particular properties of Shape Memory Alloys associated to their thermoelastic martensitic transformation with hysteresis permits applications of SMA as a damper via the conversion of the work absorbed in each cycle in heat. The guaranteed behavior requires the appropriateness of SMA for the complete requirements of the application. This work shows two complementary aspects of the SMA application in Civil Engineering, the first of them, the SMA dampers in earthquake damping of a family house. The second aspect relates an elementary approach to the damping of stayed cables in bridges, using some data from the Iroise Bridge. The application in the first case needs long time constancy of properties and then around 200 cycles during an earthquake. In the second case, the fatigue life of SMA imposes that only small strains in the alloy can be accepted. Finally, it is emphasized the importance of working conditions, including temperature and time.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.007
GPT teacher head0.245
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 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

Citations1
Published2008
Admission routes1
Has abstractyes

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