Study of long‐term skid and wear resistance of waterborne epoxy resin‐<scp>SBR</scp>compound modified emulsified asphalt microsurfacing
Bibliographic record
Abstract
Abstract As an eco‐friendly technology for pavement preventive maintenance, microsurfacing (MS) has the potential properties of cost‐saving and excellent performance benefits in repairing distress. MS also improves the surface function, especially skid resistance, of old pavements that have gradually decayed under the coupled effects of long‐term environment and loads. Reasonable assessments of the long‐term skid and wear resistance of the MS can provide support for accurate decision‐making on preventive maintenance, which in turn reduces maintenance costs and saves maintenance costs, and ensures traffic safety. To evaluate the long‐term skid and wear resistance of MS in different natural and traffic environments, this paper investigated the change of British pendulum number (BPN), rutting depth, and mass loss rate of waterborne epoxy resin‐SBR compound emulsified asphalt micro‐surfacing (WER‐SBR MEAMS) under different environmental factors and traffic conditions based on the self‐developed accelerated loading abrasion instrument. The factors considered in the experiment include temperature, rainfall, UV radiation, speed, and load. The results showed that temperature and vehicle speed had a detrimental effect on the durability of WER‐SBR MEAMS, which was more pronounced after 10,000 wear loading cycles. Reducing immersion time and slowing down the aging rate can improve the durability of WER‐SBR MEAMS. Further, the influence degrees of different factors on the skid and wear resistance of MS were evaluated using the gray correlation method. From the results of the gray correlation analysis, the correlation coefficients between external variables and BPN, rutting depth, and mass loss rate are all greater than 0.8, which indicate that all the variables involved have relatively obvious and regular effects on the skid and wear resistance of WER‐SBR MEAMS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".