Experimental study on the strength of developed concrete bridge barrier‐deck slab connections reinforced with glass fiber reinforced polymer bars
Bibliographic record
Abstract
Abstract The study introduces glass fiber‐reinforced polymer (GFRP) bars as a substitute for traditional steel reinforcement in TL‐5 concrete bridge barriers, highlighting their resistance to corrosion, long‐lasting durability, and superior tensile strength. To qualify the GFRP‐reinforced barrier‐deck slab system for use in bridges, the barrier should be designed for vehicle impact to (i) determine the amount of vertical and horizontal bars in the barrier wall and (ii) determine the bar anchorage details at the barrier‐deck slab connection. The GFRP bar detailing incorporated the recently developed high‐modulus GFRP bars with 180° hooks and bent bars. This research examined different GFRP bars' anchorage configurations in the barrier wall/concrete deck slabs using GFRP bars with 180° hooks in different orientations and the GFRP bent bars. As such, the experimental program consisted of seven full‐scale barrier walls with varying profiles of anchorage to ensure that the resistance of the anchorage at the barrier‐deck connection is greater than the factored design load specified in the Canadian Highway Bridge Design Code (CHBDC). Experimental results exceeded the factored applied moments per CHBDC for the design of the barrier‐deck connection well, regardless of the origination of the hook bars and/or the use of bent bars.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".