Determination of Mechanical Properties of FRP Materials Using the DIC Method
Bibliographic record
Abstract
ABSTRACT The mechanical properties of fiber reinforced polymer (FRP) materials have been of great interest in recent years as the applications of these materials in civil engineering and other industries have expanded. However, the utilization of FRP products, especially for new materials, has been limited because of a lack of complete knowledge about their properties. The primary objective of this study is to determine the various mechanical properties of five commonly used FRP materials. The study found that carbon FRP and high-strength glass FRP would be the best suitable materials for rehabilitation of structural elements when an increase in strength and stiffness is the primary objective. However, basalt FRP and E-glass FRP would be a far better choice for rehabilitation when ductility is the primary objective. The other objective of this study was to evaluate the application of a new digital technology, known as digital image correlation (DIC) technique, for the determination of mechanical properties of FRP materials. In this study, the strain data obtained from the DIC method were validated with strain data obtained from conventional methods (strain gage). It was found that DIC is a reliable and accurate noncontact testing method that can be successfully used for determining mechanical properties of various FRP materials.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".