Adhesion of Masonry Coating: Effect of Mortar Consistency and Type of Substrate
Bibliographic record
Abstract
The longevity of masonry mortar coatings is heavily influenced by their adhesion to the underlying substrate, a factor determined by multiple parameters including the nature and thickness of the mortars, as well as the substrate type and surface condition.This study explores the influence of cement mortar consistency (fluid, plastic, and firm) and thickness on adhesion to two distinct substrates -concrete and breeze block.Adhesion was evaluated using shear tests on 50×50 mm 2 specimens.An adhesive failure mode was observed at the interface between the substrates and the coating mortars.Among the tested conditions, plastic consistency mortar demonstrated superior adhesion to both substrates, with breeze block exhibiting a higher degree of mortar adhesion compared to concrete.This effect was particularly pronounced at early stages, with differences ranging from 35% to 257% at 7 days, reducing to 8% to 92% at 28 days.An increase in adhesion with thickness was observed for all mortar types across both substrates, with plastic consistency mortar displaying the most significant increase, exceeding 100%.The fluid and firm mortars showed comparatively smaller increases, ranging from 8% to 67%.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".