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Record W2171360776 · doi:10.1139/l99-091

Use of condensed silica fume for making high-strength, self-consolidating concrete

2000· article· en· W2171360776 on OpenAlexvenueno aff
A Kwan

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2000
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
FundersCroucher Foundation
KeywordsSilica fumeSuperplasticizerMaterials scienceCompressive strengthComposite materialConsolidation (business)CompactionSelf-consolidating concrete

Abstract

fetched live from OpenAlex

A high concrete strength can be achieved by lowering the water/binder ratio and a high workability by adding a higher dosage of superplasticizer. However, a high-performance concrete with both high strength and high workability cannot be produced by just these means because lowering the water/binder ratio leads to lower workability and there is a limit to the increase in workability that can be attained by adding superplasticizer. To produce a high-strength, high-workability concrete, the concrete strength needs to be increased without lowering the water/binder ratio. This can be done by adding condensed silica fume. In this study, a series of trial mixing aimed at developing high-strength, self-consolidating concrete (mean cube strength >80 MPa and needs no compaction for consolidation) was carried out. Several mixes suitable for making such high-performance concrete have been developed and it was found that the addition of condensed silica fume may, under favourable conditions, increase not only the strength but also the workability of the concrete mix. Based on the trial mix results, charts for the design of high-strength, high-workability concrete mixes made of the studied constituents are presented.Key words: condensed silica fume, high-strength concrete, self-consolidating concrete.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
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 head0.017
GPT teacher head0.216
Teacher spread0.199 · 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.

Study designSimulation or modeling
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

Citations62
Published2000
Admission routes1
Has abstractyes

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