Comprehensive Investigation of Influential Mix-Design Factors on the Microsurfacing Mixture Performance
Bibliographic record
Abstract
This study aims to enhance the performance of the microsurfacing mixture and evaluate the parameters affecting this mixture. For these purposes, the effects of pure bitumen types for producing bitumen emulsion, types of aggregates, and the percentages of bitumen emulsion and emulsifier on the microsurfacing mixture are investigated. Three different types of pure bitumen with penetration grades of 40–50, 60–70, and 85–100 were used to make bitumen emulsion. In addition, limestone and siliceous aggregates, 0.9%, 1.2%, 1.5% emulsifier, and 9%, 10%, and 11% bitumen emulsion were used for test samples. The microsurfacing mixture was then evaluated by cohesion, wet track abrasion, loaded wheel, and mixing time tests. The results revealed that increasing the percentages of bitumen emulsion, emulsifier, limestone aggregates, and bitumen emulsion made from softer pure bitumen increased the microsurfacing mixture’s breaking time. Moreover, the test sample containing bitumen emulsion made from harder pure bitumen, limestone aggregates, and lower emulsifier percentages showed a better setting time, which is suitable for a quick traffic reopening system. In addition, using limestone aggregates, pure bitumen with a lower penetration grade and a higher emulsifier percentage declined the optimum bitumen emulsion percentage. It also enhanced the microsurfacing mixture’s resistance to abrasion, rutting, and moisture sensitivity. Overall, using limestone aggregates and bitumen emulsion made from harder pure bitumen improved the microsurfacing mixture performance, preventing some distresses, including rutting, stripping, bleeding, and aggregates’ polishing, which can lead to the longer service life of this mixture.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".