MétaCan
Menu
Back to cohort
Record W2999339056 · doi:10.5772/intechopen.88451

Design and Analysis of SMA-Based Tendon for Marine Structures

2020· book-chapter· en· W2999339056 on OpenAlexafffund
Shahin Zareie, Abolghassem Zabihollah

Bibliographic record

VenueIntechOpen eBooks · 2020
Typebook-chapter
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsSMA*Shape-memory alloySubseaStructural engineeringPseudoelasticityTendonDeformation (meteorology)Tension (geology)Materials scienceEngineeringGeologyComposite materialGeotechnical engineeringComputer scienceAnatomyUltimate tensile strengthMicrostructureMartensite

Abstract

fetched live from OpenAlex

A tension-leg platform (TLP), as an offshore structure, is a vertically moored floating structure, connecting to tendon groups, fixed to subsea by foundations, to eliminate its vertical movements. TLPs are subjected to various non-deterministic loadings, including winds, currents, and ground motions, keeping the tendons under ongoing cyclic tensions. The powerful loads can affect the characteristics of tendons and cause permanent deformation. As a result of exceeding the strain beyond the elastic phase of the tendons, it makes unbalancing on the floated TLPs. Shape memory alloy (SMA)-based tendons due to their superelasticity properties may potentially resolve such problem in TLP structures. In the present work, performance and functionality of SMA wire, as the main component of SMA-based tendon under cyclic loading, have been experimentally investigated. It shows a significant enhancement in recovering large deformation and reduces the amount of permanent 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.043
GPT teacher head0.264
Teacher spread0.221 · 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
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueIntechOpen eBooksSame topicShape Memory Alloy TransformationsFrench-language works237,207