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Record W1510767594

SELF-CONSOLIDATING CONCRETE SOLVES CHALLENGING PLACEMENT PROBLEMS

2003· article· en· W1510767594 on OpenAlex
Michel Lessard, Brian Salazar, Caroline Talbot

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

VenueACI Concrete International · 2003
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsnot available
Fundersnot available
KeywordsSelf-consolidating concreteSlumpColumn (typography)Composite numberStructural engineeringMaterials scienceGeotechnical engineeringEngineeringCivil engineeringComposite materialCompressive strength
DOInot available

Abstract

fetched live from OpenAlex

This article describes how self-consolidating concrete (SCC) was used to fill 100-foot high steel composite columns at a new airport terminal in Toronto, Ontario. The concrete had to be pumped vertically from the bottom of the column. The concrete for the job had to be homogenous, with little tendency to bleed and segregate. Appropriate mix proportions were developed to achieve 30 MPa at 28 days strength and a slump flow between 650 and 750 mm. Sufficient time of plasticity was required to ensure ease of pumping. A test placement was conducted before the SCC was placed in the remaining 179 columns. Water-reducing admixtures were used to achieve and maintain the specified slump flow throughout the pumping process. This unique use of SCC provided a cost-efficient solution for a difficult project.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score1.000

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)0.0010.000

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 head0.012
GPT teacher head0.225
Teacher spread0.212 · 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