Damage prediction and long-term cost performance analysis of glass fiber recycled concrete under freeze-thaw cycles
Bibliographic record
Abstract
This paper establishes a freeze-thaw cycle damage model by analyzing the changes in mass, relative dynamic elastic modulus and compressive strength of glass fibers (0 %, 0.5 %, 1.0 %, and 1.5 %) recycled concrete after the freeze-thaw cycle (0, 50, 100, and 150) tests. Meanwhile, the antifreeze life of concrete is predicted based on the Weibull distribution model. The study show that glass fiber can reduce the deterioration of recycled concrete specimen surfaces result from frozen-thaw environment. After 150 freeze-thaw cycles, the specimens with 0.5 %, 1.0 %, and 1.5 % glass fiber content showed a reduction in mass loss of 0.405 %, 1.100 %, and 0.725 %, and an increase in compressive strength of 8.19 %, 21.35 %, and 17.79 %, respectively, when compared with the specimens without glass fiber. Fiber can provide tension when recycled concrete is compressed, thus improving compressive strength, and the optimum glass fiber content is 1.0 %. After 150 freeze-thaw cycles, the freeze-thaw damage of recycled concrete specimens with 1.0 % glass fiber content was the smallest. Compared with that before freeze-thaw, the mass of the specimens only decreased by 2.128 %, and the compressive strength decreased by 35.2 %. Finally, the long-term cost-effectiveness of Recycled Aggregate Concrete (RAC) is analyzed based on the predicted life, and the performance optimization and economic benefits are comprehensively considered. Therefore, the appropriate volumetric admixture of glass fiber can be selected according to the actual situation in different regions, considering the cost-effectiveness of glass fiber recycled concrete to provide suggestions for related research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".