Role of Fly Ash in the Repair Interface between Magnesium Phosphate Cement and Cement Concrete
Bibliographic record
Abstract
This paper examines a rapid repair material for cement concrete pavement based on magnesium phosphate cement (MPC) blended with fly ash (FA). Three kinds of stress forms for rapid repair of Portland cement concrete pavement (PCCP) were evaluated and the role of FA in the repair interface was studied. The optimal mixture ratio of FA-MPC mortar was determined, and the compressive and flexural strength tested. The composite specimens were then designed to test the interfacial bonding, tensile, and plain shear strength. Finally, the multi-scale mechanism of FA in the interface was analyzed by a pull-off adhesion test, nanoindentation test, scanning electron microscope (SEM)/energy dispersive spectrometer (EDS), Raman spectrum, and molecular dynamics simulation. The optimal content of FA in MPC mortar was 20%. The laminated beam structure had the maximum strength, and the interface load bearing capacity was the lowest. For further multi-scale analysis, the results of the pull-off adhesion test showed the adhesion strength of interface was increased by the curing age and loading rate. Adhesion capacity of basalt aggregate and FA-MPC was stronger than that of Portland cement mortar and FA-MPC. The elastic modulus of MPC-aggregate interface was more than that of MPC-OPC mortar. SEM/EDS and Raman spectrum results showed that the MPC blended with FA had cracks and defects at the interface formed with cement mortar. Less FA formed near the interface. The good adhesion ability of FA and basalt aggregate was verified by molecular dynamics simulation. By contrast, poor adhesion strength with cement mortar was confirmed.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".