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Prediction on Autogenous Shrinkage of Self-Consolidating Concrete

2010· article· en· W2008814016 on OpenAlex
Wu-Jian Long, Kamal H. Khayat, Feng Xing

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

VenueAdvanced materials research · 2010
Typearticle
Languageen
FieldEngineering
TopicConcrete Properties and Behavior
Canadian institutionsUniversité de Sherbrooke
FundersGuangdong Academy of Sciences
KeywordsShrinkagePrecast concreteSlumpMaterials scienceSelf-consolidating concreteFly ashComposite materialCementAggregate (composite)Structural engineeringEngineeringCompressive strength

Abstract

fetched live from OpenAlex

Proper estimate of autogenous shrinkage of self-consolidating concrete (SCC) can provide engineers with the information necessary for producing high quality products manufactured with SCC. An experimental program was undertaken to evaluate autogenous shrinkage of precast, prestressed SCC. Sixteen SCC with slump flow of 680 ± 20 mm were evaluated. These mixtures were made with 440 to 500 kg/m3 of binder, Type MS cement or HE cement and 20% Class F fly ash, 0.34 to 0.40 w/cm, viscosity-modifying admixture content of 0 to 100 mL/100 kg of binder, and 0.46 to 0.54 sand-to-total aggregate volume ratio. Two high-performance concretes (HPC) with 0.34 and 0.38 w/cm and slump of 150 mm were also investigated. HPC developed similar autogenous shrinkage at 56 days compared to SCC made of a given binder type. Shrinkage was compared to prediction models proposed by Tawaza and Miyazawa 1997, Jonasson and Hedlund 2000, and CEB-FIP 1999. The Tazawa and Miyazawa model was modified to provide adequate prediction of autogenous shrinkage for precast, prestressed SCC.

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 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 score0.007
Threshold uncertainty score0.746

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 studies0.0000.000
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 head0.038
GPT teacher head0.302
Teacher spread0.264 · 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