Preparation and Performance Improvement Mechanism Investigation of High-Performance Cementitious Grout Material for Semi-Flexible Pavement
Bibliographic record
Abstract
Semi-flexible pavement material (SFPM) combines the advantages and avoids the disadvantages of asphalt concrete flexible pavement and cement concrete rigid pavement. However, due to the problem of interfacial strength of composite materials, SFPM is prone to cracking diseases, which limits the further application of SFPM. Hence, it is necessary to optimize the composition design of SFPM and improve its road performance. In this study, the effects of cationic emulsified asphalt, silane coupling agent and styrene-butadiene latex on the improvement of SFPM performance were compared and analyzed. The influence of modifier dosage and preparation parameters on the road performance of SFPM was investigated by an orthogonal experimental design combined with principal component analysis (PCA). The best modifier and the corresponding preparation process were selected. On this basis, the mechanism of SFPM road performance improvement was further analyzed by scanning electron microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) spectral analysis. The results show that adding modifiers can significantly enhance the road performance of SFPM. Compared to silane coupling agents and styrene-butadiene latex, cationic emulsified asphalt changes the internal structure of cement-based grouting material and increases the interfacial modulus of SFPM by 242%, allowing cationic emulsified asphalt-SFPM (C-SFPM) to exhibit better road performance. According to the results of the principal component analysis, C-SFPM has the best overall performance compared to other SFPMs. Therefore, cationic emulsified asphalt is the most effective modifier for SFPM. The optimal amount of cationic emulsified asphalt is 5%, and the best preparation process involves vibration at a frequency of 60 Hz for 10 min and 28 days of maintenance. The study provides a method and basis for improving the road performance of SFPM and a reference for designing the material composition of SFPM mixes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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".