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Record W2128159249 · doi:10.3390/ma7042474

Performance-Based Specifications of Workability Characteristics of Prestressed, Precast Self-Consolidating Concrete—A North American Prospective

2014· article· en· W2128159249 on OpenAlexaff
Wu-Jian Long, Kamal H. Khayat, Guillaume Lemieux, Soo-Duck Hwang, Ningxu Han

Bibliographic record

VenueMaterials · 2014
Typearticle
Languageen
FieldEngineering
TopicStructural Behavior of Reinforced Concrete
Canadian institutionsUniversité de Sherbrooke
FundersNational Natural Science Foundation of China
KeywordsPrecast concreteSelf-consolidating concretePrestressed concreteStructural engineeringMaterials scienceEngineeringComposite materialCompressive strength

Abstract

fetched live from OpenAlex

Adequate selection of material constituents and test methods are necessary for workability specifications and performance of hardened concrete. An experimental program was performed to evaluate the suitability of various test methods for workability assessment and to propose performance specifications of prestressed concrete. In total, 33 self-consolidating concrete (SCC) mixtures made with various mixture proportioning parameters, including maximum size and type of aggregate, type and content of binder, and w/cm were evaluated. Correlations among various test results used in evaluating the workability responses are established. It is recommended that SCC should have slump flow values of 635-760 mm. To ensure proper filling capacity greater than 80%, such concrete should have a passing ability that corresponds to L-box blocking ratio (h₂/h₁) ≥ 0.5, J-Ring flow of 570-685 mm, slump flow minus J-Ring flow diameter ≤75 mm. Moreover, Stable SCC should develop a column segregation index lower than 5%, and rate of settlement at 30 min of 0.27%/h for SCC proportioned with 12.5 or 9.5 mm MSA. It is recommended that SCC should have a plastic viscosity of 100-225 Pa·s and 100-400 Pa·s for concrete made with crushed aggregate and gravel, respectively, to ensure proper workability.

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 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)0.000
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 score0.073
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.012
GPT teacher head0.205
Teacher spread0.192 · 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 teacher head, 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

Citations23
Published2014
Admission routes1
Has abstractyes

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