Testing A Full-Scale Reinforced Concrete Bridge Deck with GFRP and Steel Reinforcement Using Cyclic Pulsating and Rolling Loads
Bibliographic record
Abstract
The repair and replacement of bridge decks represents a large portion of infrastructure spending. Gaining a better understanding of bridge deck behaviour through more representative vehicular loading can lead to a reduction in the costs, time and environmental impacts associated with construction. Past studies have most often used fixed pulsating loads to represent cyclic loading on a bridge deck. However, it has been shown that these types of loading do not induce the most critical conditions when compared with moving loads. This thesis is the first part of a long-term study intended to compare the loading effects of fixed pulsating cyclic loads against moving wheel loads, up to 3000 load cycles. The study also compares different types of deck reinforcement and construction methods, namely conventional steel reinforcing bar, glass fibre reinforced polymer (GFRP) rebar and GFRP stay-in-place (SIP) structural forms for rapid construction. A slab-on-girder reinforced concrete bridge deck with dimensions of 15.24 m x 3.89 m x 210 mm and a girder spacing of 3.05 m was designed according to the Canadian Highway Bridge Design Code (CHBDC) and constructed in the laboratory. Detailing and construction procedures have also been proposed for the new GFRP SIP structural form system. The deck was conceptually divided into four sections but all were monolithically cast. Sections 1 and 4, at either ends of the deck, were identical and reinforced with GFRP bars, Section 2 incorporated the novel GFRP SIP form system and Section 3 was reinforced with conventional steel rebar. Section 1 was subjected to pulsating loads and Sections 2-4 were subjected to moving wheel loads. Monotonic load tests were performed at various cycling intervals to establish stiffness degradation. After 3000 load cycles, the maximum values of reinforcement and concrete strain were well within their ultimate limits, signifying the deck was not at risk of failure. Section 4 experienced a larger reduction in stiffness than Section 1, suggesting that the moving loads were more damaging than the pulsating loads. It is important to note that observations could change at higher levels of cyclic loading, which is beyond the scope of this thesis.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".