Structural Qualification of a Developed GFRP-Reinforced Concrete Bridge Barrier using Ultimate Load Testing
Bibliographic record
Abstract
Corrosion of reinforcing steel bars is the main factor affecting durability and service life of steel-reinforced bridge barriers in North America. The use of glass fiber reinforcing polymer (GFRP) bars as non-corrosive material has emerged as an innovative solution to corrosion related problems. A recent cost-effective design of PL-3 bridge barrier was developed at Ryerson University incorporating high-modulus GFRP bars with headed ends. An experimental program was conducted to investigate the load carrying capacity of the developed barrier wall. A 40-m long barrier was constructed and tested at four different locations to investigate its structural behavior, crack pattern and ultimate strength when subjected to the equivalent static loading simulating vehicle impact. Experimental results were compared with the design values specified in the Canadian Highway Bridge Design Code. Experimental findings showed a large margin of safety for the proposed GFRP-reinforced barriers. The failure pattern due to transversal loading the longitudinal barrier over 2400 mm length was initiated by a trapezoidal flexural crack pattern at front face of the barrier, followed by punching shear failure at the transverse load location. Comparison between the available punching shear equations in the literature and the punching shear failure developed in the barrier wall was conducted.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".