Carbonization Law of Fly Ash Concrete under Freeze-Thaw Cycles Based on Image-Pro Plus
Bibliographic record
Abstract
To understand the influence of freeze-thaw on the carbonization performance of concrete in severe cold areas, this paper conducted experiments to explore the carbonization law of fly ash concrete under freeze-thaw cycles. First, carbonization tests were conducted under different freeze-thaw cycles and fly ash contents; then PS (Photoshop) and IPP (Image-Pro Plus) were adopted to measure the carbonized area and calculate the ratio of carbonized area (RCA). The experimental results showed that, when the fly ash content was between 10% and 30%, RCA increased slowly; when the fly ash content was 20%, the convergence point showed up; when the fly ash content was 0, the air-entrained fly ash concrete had the best resistance to carbonation. With the help of PS and IPP, this paper calculated the RCA more accurately and found that, the freeze-thaw cycles can aggravate carbonization, and there is a linear relationship between carbonization depth and RCA. The research findings in this paper can provide a reference for the durability evaluation and design of concrete structures in severe cold areas.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".