Evaluation of a modification of current microsurfacing mix design procedures
Bibliographic record
Abstract
Although microsurfacing is widely used, current tests and mix design methods mostly rely on laboratory conditions and the correlation between laboratory results and field performance is poor. Therefore, there is a need to develop new mix design procedures, specifications, and guidelines for microsurfacing mixtures. The research described in this paper intended to suggest modifications to the actual International Slurry Seal Association (ISSA) mix design procedure for microsurfacing. The first part of study reports the findings of a detailed laboratory investigation concerning the effect of asphalt emulsion, added water content, and Portland cement on the design parameters and properties of microsurfacing mixtures. A multilevel factorial design is used to assess the effect of different mixture proportions on the test responses. For this, one aggregate type, one asphalt emulsion type or grade, and one aggregate gradation were used in the study. This part of study consisted mainly of establishing a method for preparing and testing microsurfacing mixture using four main mixture design tests proposed by the ISSA (TB 139, TB 113, TB 100, and TB 109). The results obtained with ISSA TB 109 and ISSA TB 100 mixture design tests were found highly variable and not precise enough to suggest optimum mix design. For the second part of this study, different tests were also studied to refine the current mix design procedure. The results have shown that ISSA TB 139 can be used to define the optimum water content at which samples should be tested, and that ISSA TB 147 mix design test should be used to define the optimum asphalt emulsion content.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".