Chemical resistance of carbon, basalt, and glass fibers used in FRP reinforcing bars
Bibliographic record
Abstract
One of the most important fields of research dealing with the use of carbon-, basalt-, and glass-fiber composites in the civil construction industry is their behavior under various chemical exposure conditions. Fiber-reinforced-polymer composites used as internal and external reinforcement in various structural applications can be subjected to widely differing pH situations. This study investigated the chemical durability of various carbon, basalt and glass fibers. The fibers were immersed in four types of solutions with acid, saline, alkaline, and deionized-water conditioning schemes. The fiber mass loss and surface damage along with changes due to chemical reactions were observed through weight-loss measurements and scanning electron microscopy. A criterion was developed to characterize the performance of fibers as very good, good, fair, and poor. This methodology can also be used by manufacturers as a quick quality-control tool for evaluating the chemical resistance of different fibers prior to large-volume production. The results reveal that the carbon fibers exhibited higher chemical resistance than the basalt and glass fibers based on weight loss and evidence of chemical reactions. Moreover, the determination of the fiber chemical composition before and after conditioning in acid and alkaline solutions clearly shows that the E-glass fibers, which are known to contain boron, were very sensitive to chemical corrosion. The ECR-glass fibers showed excellent chemical durability, even better than the basalt fibers.
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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 | 0.001 |
| 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".