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SMA in Mitigation of Extreme Loads in Civil Engineering: Study of their Application in a Realistic Steel Portico

2011· article· en· W2048443444 on OpenAlexaff
Vicenç Torra, A. Isalgué, C. Auguet, Guillem Carreras, Fabio Casciati, F.C. Lovey, Patrick Terriault

Bibliographic record

VenueApplied Mechanics and Materials · 2011
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsSMA*Structural engineeringShape-memory alloyDamperOscillation (cell signaling)DiagonalNickel titaniumMaterials scienceEngineeringComputer scienceMathematicsComposite materialGeometry

Abstract

fetched live from OpenAlex

Guaranteeing the use of Shape Memory Alloys (SMA) in mitigation of extreme load effects requires a deep study of the SMA behavior according to the specific requirements of the applications in damping. The damper was defined according the expected requirements (length of SMA and number of SMA wires). It is applied to two types of alloys (CuAlBe and NiTi) in the diagonals of a realistic steel portico (2.47 m x 4.10 m). The measurements establish that the used SMA reduces the oscillation amplitude to a less than a half.

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)0.001
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
Metaresearch0.0000.001
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)0.0010.000

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 head0.030
GPT teacher head0.216
Teacher spread0.186 · 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

Citations6
Published2011
Admission routes1
Has abstractyes

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