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Record W7019449561

FLEXURAL DESIGN OF SHAPE MEMORY ALLOY REINFORCED CONCRETE SECTIONS FOR STRENGTH AND SERVICEABILITY REQUIREMENTS

2008· article· en· W7019449561 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueScholarship@Western (Western University) · 2008
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsnot available
Fundersnot available
KeywordsSMA*Serviceability (structure)Flexural strengthShape-memory alloyDeflection (physics)Reinforced concrete
DOInot available

Abstract

fetched live from OpenAlex

Superelastic shape memory alloys (SMAs) have the ability to recover plastic deformations upon unloading. This unique property has motivated researchers to utilize them as primary reinforcement for RC structures located in seismic regions. The lack of understanding the behaviour of SMA RC sections is constraining their use. This thesis investigates the flexural behaviour of SMA RC sections. The validity of the flexural design equations provided by the Canadian Standards for SMA RC sections is evaluated. The load-deflection behaviour of SMA RC members is also investigated. The results are used to assess the applicability of available deflection models for SMA RC members. Artificial neural networks (ANNs) are used to develop a new deflection model. The thesis provides flexural design equations that allow engineers to accurately design SMA RC members for strength and serviceability requirements.

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)
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 score0.161
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.151
GPT teacher head0.318
Teacher spread0.167 · 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