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Record W4386641097 · doi:10.1002/cepa.2756

Memory‐Steel for Smart Steel Structures: A Review on Recent Developments and Applications

2023· review· en· W4386641097 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuece/papers · 2023
Typereview
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsSMA*Shape-memory alloyPseudoelasticityMaterials scienceBridge (graph theory)Structural engineeringSmart materialComposite materialMechanical engineeringMetallurgyEngineeringComputer scienceMartensiteMicrostructure

Abstract

fetched live from OpenAlex

Abstract This study reviews the recent works on the development and application of iron‐based shape memory alloy (Fe‐SMA), the so‐called memory‐steel, for steel structures. First, the studies on the material properties of Fe‐SMA in terms of shape memory effect and superelasticity are discussed. Next, the use of Fe‐SMA in prestressed strengthening of steel structures is explained, including the applications in strengthening of steel girders, connections, and fatigue crack repairs. Various strengthening solutions such as using mechanically anchored or adhesively‐bonded Fe‐SMA, as well as the studies on the behavior of the Fe‐SMA‐to‐steel bonded joints, are discussed. The use and application of Fe‐SMA for strengthening of a 113‐years steel bridge has been explained. In addition, studies on the innovative application of the Fe‐SMA as pipe couplers are presented. At the end, innovative ongoing research on the additive manufacturing of architected Fe‐SMA (4D‐printing) are discussed.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.086
GPT teacher head0.351
Teacher spread0.265 · 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