Material Characterization of GFRP Bars in Compression Using a New Test Method
Bibliographic record
Abstract
Abstract This article presents a new test method for determining the mechanical properties of glass fiber–reinforced polymer (GFRP) composite bars in compression, namely the compressive strength, compressive modulus of elasticity, ultimate crushing strain, and compressive stress-strain curves of the bars. The contribution of GFRP bars in compression is currently neglected by major design guidelines related to GFRP-reinforced concrete columns. However, the demand for using GFRP bars is increasing because multiple researchers have shown the effectiveness of the bars in concrete columns. Thus, the need for characterization of the mechanical properties of GFRP bars is increasing, while there is no standardized test method to evaluate the compressive properties of these bars. Therefore, in this article, a new test method is proposed for evaluating the compressive characteristics of GFRP bars. The proposed test method was examined through testing a total of 35 specimens. It was observed that the test method was able to evaluate the compressive characteristics of the GFRP bars successfully. Three different modes of compressive failure were observed, which were related to the crushing of GFRP bars in different locations in the bar, but no premature failure or bar buckling was observed. Moreover, a comparison between tensile and compression characteristics of the GFRP bars showed that the tensile test results are not sufficient to estimate the compressive characteristics, and performing a compression test is necessary.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".