Use of fibre reinforced polymer reinforcement integrated with fibre optic sensors for concrete bridge deck slab construction
Bibliographic record
Abstract
The use of corrosion free fibre reinforced polymer (FRP) composites as reinforcement to concrete is currently being seen as a promising option to generate durable concrete structures. However, there exists very little credible information about its field application and performance. This paper describes the Joffre Bridge project, in Sherbrooke (Québec, Canada), over the St-François River, where Carbon Fibre Reinforced Polymer (CFRP) was used as reinforcement for a portion of the concrete deck slab. The bridge consists of five longitudinal spans with lengths varying from 26 to 37 m. Each span has a concrete deck supported by five steel girders at 3.7 m. A part of the concrete deck slab (7.3 × 11.5 m) and a portion of the traffic barrier and the sidewalk were reinforced with Carbon (CFRP) and Glass Fibre Reinforced Polymer (GFRP) reinforcement. The bridge was extensively instrumented with many different types of gauges, including integrated fibre optic sensors (FOS) into FRP reinforcement. The performance of the bridge had been assessed under static and dynamic loading using calibrated heavy trucks. Moreover, structural design and construction details of the bridge and instrumentation were performed. The results from calibrated field tests on the bridge are presented in this paper.Key words: concrete bridge deck, FRP reinforcement, fibre optic sensors (FOS), field calibrated tests, performance monitoring.
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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 | 0.000 | 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 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".