State of practice on the use of tack coats for micro-surfacing applications: A survey
Bibliographic record
Abstract
This paper presents the results of the first comprehensive national survey on the state of practice on the use of tack coats for micro-surfacing applications and factors that affect the bond strength of micro-surfacing mixes. Surveys were sent to all state and provincial transportation agencies in the United States (US) and Canada. The survey questions were developed to collect information on the state of the practice related to: use of tack coat for micro-surfacing applications, types of tack coat materials, dilution rates of tack coat materials, residual application rates, determination of rate for different types of surfaces, methods used for tack coat distribution, and micro-surfacing failures related to tack coat application. The key findings of the survey are summarized. The results indicated that 11 of the transportation agencies that responded to the survey are currently using tack coat on all surfaces with micro-surfacing. In addition, ten agencies indicated using tack coat on some but not all surfaces with micro-surfacing, such as concrete surfaces or surfaces that are heavily raveled or oxidized. Finally, 18 agencies indicated that they do not use tack with micro-surfacing application. The survey results indicated that there is no consensus among responding transportation agencies about the importance of using tack coat for micro-surfacing applications. Most agencies that use tack coat with micro-surfacing believe that it is critical for providing adequate bonding with the underlying surface, while the majority of the agencies that do not use tack coat believe that it is not needed, as adequate bonding can be provided by the emulsion in the micro-surfacing mix. However, some agencies that stopped using tack coat with micro-surfacing (such as Indiana DOT and Michigan DOT) have noted some debonding issues and decided to or are considering adding a requirement for tack coat usage. The survey results also indicated that none of the responding agencies are performing field tests to evaluate the bonding strength of micro-surfacing mixes. Furthermore, four agencies indicated that they do perform tests to evaluate bonding strength between asphalt layers. The tests used included either a pull-off test on milled surfaces (Kansas) or a direct shear bond test (Tennessee, Texas, and West Virginia).
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".