A capillary action test for the investigation of adhesion in repair technology
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".