Bibliographic record
Abstract
Our understanding of how the bond between FRP and concrete performs under impact loading is severely limited. In this paper, bond performance of Sprayed FRP with concrete was studied under impact loading. A novel 550 mm × 150 mm × 150 mm notched specimen was developed and a 75 mm wide strip of Sprayed FRP was applied to strengthen the specimen. A novel test set-up designed to minimize the influence of specimen rebound after impact as well as eliminate the need to correct the load data for inertial effects was developed. FRP strains were measured using strain gauges carefully installed on the FRP, and the bond stress was calculated using the differential strain values. A kinematics analysis was also performed to translate the vertical deflections into strain values. Three types of surface preparation methods (water jetting, sand blasting, and jack hammering) were studied. Specimens are tested under four different strain rates (four different hammer heights of 250, 500, 750, and 1000 m) and the results were compared with quasi static values using loading rates of 0.005, 0.05, and 0.5 mm/min. Results showed that sand blasting is the most effective surface preparation method resulting in the highest bond strength values. Impact results were characterized using a Dynamic Improvement Factor (DIF). Results indicate that the FRP–concrete bond is highly strain sensitive, and in general the bond strength increases and the fracture energy decreases under higher rates of strain. Untreated specimens are shown to be more strain-rate sensitive than the surface treated ones.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".