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Record W2104321514 · doi:10.1139/l03-061

A capillary action test for the investigation of adhesion in repair technology

2003· article· en· W2104321514 on OpenAlexvenueno aff
Luc Courard, Robert Degeimbre

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2003
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsCapillary actionMaterials scienceWettingComposite materialPaint adhesion testingCementSubstrate (aquarium)AdhesionPlasticizerSlurryAbsorption of water

Abstract

fetched live from OpenAlex

When repair material is laid down on a concrete substrate, the porosity of concrete allows it to penetrate the substrate, increasing the probability of physical interactions, and subsequently increasing adhesion. Water absorption by immersion or usual capillary action tests do not provide enough information to understand what happens when contact occurs between the concrete substrate and the cement slurries used as a bonding layer. An adaptation of the capillary action test is presented; this is based on a continuous measurement of mass change of the sample. Major differences according to the type of surface preparation are pointed out: absorption rates as well as mass changes are greater for polished concrete substrates than for sandblasted ones. Centrifuged solutions of cement slurries modified with plasticizers and superplasticizers are used for water replacement in the adapted capillary action test. Results clearly show the influence of these admixtures: the greater the adhesion tension, the greater the wetting of the concrete substrate. Determination of these thermodynamic characteristics and performance of an adapted capillary action test provide a better understanding of the mechanisms of interface creation and promotion of adhesion.Key words: concrete, substrate, repair, capillary, absorption, impregnation, interface, centrifugation, superficial tension, pore radius.

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 score0.056
Threshold uncertainty score0.840

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 head0.018
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.

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

Citations30
Published2003
Admission routes1
Has abstractyes

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