Freeze-Thaw Performance Characterization and Leachability of Potassium-Based Geopolymer Concrete
Bibliographic record
Abstract
It is well known that concrete is one of the most widely used construction materials in the world, and cement as its key constituent is partly responsible for global Carbon Dioxide (CO2) emission. Due to these reasons, high strength concrete with lower CO2 emission, and concrete with lower reliance on natural resources is increasingly popular. Geopolymer Concrete (GPC), due to its capability to minimize the consumption of natural resources, has attracted the attention of researchers. In cold regions, frost action is one of the primary GPC deterioration mechanisms requiring huge expenditures for repair and maintenance. In this regard, two types of GPC (fly-ash based GPC and bottom-ash based GPC) were exposed to the harsh freeze-thaw conditions using a standard test method. The dynamic elastic modulus of both types of GPC was determined using a Non-Destructive Test (NDT) method called Resonant Frequency Test (RFT). The results of RFT after exposing to 300 freeze-thaw cycles showed that bottom-ash based GPC has better freeze-thaw resistance than fly-ash based GPC. Moreover, in this study, the leachability of bottom-ash based GPC was also investigated to trace the heavy metals (including Si, Al, Na, Cr, Cu, Hg) using Toxicity Characteristic Leaching Procedure (TCLP) test. The results of the TCLP test showed that all of the heavy metals could be effectively immobilized into the geopolymer paste.
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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 | 0.000 | 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".