Effect of Fly Ash and Fine-Sand Addition on the Mechanical and Thermal Properties of Modified Adhesive
Bibliographic record
Abstract
This study presents an exploratory investigation into the mechanical and thermal properties of a modified adhesive (high adhesive, Sikadur®-330) when mixed with fly ash or a combination of fly ash and fine-sand at various ratios, as well as the adhesive's performance under high temperatures of 250℃.A series of physical tests, including compressive strength, consistency, modulus of rupture, density, and ultrasonic pulse velocity, were conducted alongside thermal assessments, such as final and initial shrinkage, linear expansion coefficient, and heat of reaction measurements.The results demonstrate that the incorporation of fly ash and fine-sand significantly enhances the adhesive's thermal properties by reducing both final and initial shrinkage, minimizing the linear expansion coefficient, and attenuating the heat of reaction.Furthermore, the mechanical properties of the adhesive were observed to improve upon exposure to high temperatures of 250℃.The addition of fine-sand and fly ash to the adhesive not only reduced costs but also led to a notable increase in the modulus of rupture and compressive strength.Consequently, the optimal ratio of adhesive, sand, and fly ash was determined to be 1:1:1 by weight, considering improvements in mechanical and thermal properties, cost reduction, and preserved workability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".