Effect of Different Constituent Fiber, Resin, and Sizing \nCombinations on Alkaline Resistance of Basalt, Carbon, \nand Glass FRP Bars
Bibliographic record
Abstract
When used as an internal reinforcement, fiber-reinforced-polymer (FRP) composite bars are exposed to a highly alkaline (pH > 12.5) concrete environment. This study evaluated the durability of 24 types of FRP bars in a simulated alkaline concrete environment, specifically with respect to reinforcing-fiber type (carbon, basalt, and glass), fiber sizing, resin chemistry, and manufacturer. A total of 10 types of glass fibers, including two types of E-glass fibers and eight types of electrical corrosion resistance (ECR)-glass fibers, four types of basalt fibers, two types of carbon fibers, six types of resin systems based on vinyl ester, polyurethane, and epoxy resins, and five types of proprietary fiber sizings were used in manufacturing the bars. The study focused on assessing the tensile, transverse-shear, and interlaminar-shear properties of FRP bars subjected to 3 months of accelerated alkaline conditioning at 60°C, as per Canadian Standards Association (CSA) and American Society for Testing and Materials (ASTM) standards. The strength retention and failure of these bars were evaluated as a measurement of the durability and long-term performance of the FRP bars currently available on the market and for quality control by manufacturers. Statistical analysis using independent samples t-test and one-way analysis of variance revealed that the manufacturing parameters have a significant effect on the mechanical characteristics and alkaline resistance of FRP bars. In particular, the results show that the FRP bars manufactured with the same parameters and fiber types but by different fiber manufacturers with different fiber sizings and resin systems produced bars with totally different strength properties and durability performance in an alkaline environment. Vinyl ester resin and silane-sized fiber was the most compatible resin system and produced a more durable glass-FRP bar, while the epoxy resin yielded more durable basalt- and carbon-FRP bars. This paper also describes a procedure for and a criterion of the optimum manufacturing parameters to achieve specific mechanical properties and durability performance with FRP bars.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".