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Record W2127087131 · doi:10.1139/l09-083

Mix-design method of self-compacting concretes for pre-cast industry

2009· article· en· W2127087131 on OpenAlexvenueno aff
Jiyang Shen, I. Yurtdas, Cheikhna Diagana, A. Li

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsSuperplasticizerPrecast concreteCompressive strengthMaterials scienceStructural engineeringComposite materialComputer scienceCivil engineeringEngineering

Abstract

fetched live from OpenAlex

Some mix-design methods for self-compacting concrete (SCC) have been proposed since the 1990s, but these methods do not address all practical needs. This paper proposes a method that enables the composition of SCC to be designed for a given strength (in this study, from 10 to 50 MPa at age 1 day or from 30 to 70 MPa at age 28 days). In this method, the compressive strength of SCC at an early age is considered an important parameter to answer the needs of the precast industry. The proposed mix-design method is based on optimizing the packing density of aggregates and ensuring the necessary quantity of paste to fill the voids between aggregates and to provide good fluidity of the SCC. The compressive strength of SCC can be estimated according to the Bolomey formula, and the quantity of superplasticizer can be determined by the proposed method.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.999
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.024
GPT teacher head0.267
Teacher spread0.243 · 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 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

Citations6
Published2009
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Civil EngineeringSame topicConcrete and Cement Materials ResearchFrench-language works237,207