Design, Construction, and Performance of Continuously Reinforced Concrete Pavement Reinforced with GFRP Bars: Case Study
Bibliographic record
Abstract
The application of deicing salt on roads during the winter is one of the main reasons for steel corrosion in reinforced-concrete pavements in cold-weather regions such as Canada and the Northern United States. Steel corrosion creates internal stresses in the concrete that cause the concrete to burst. This reduces the service life of pavements and increases maintenance costs. This study presents a long-term field test of a continuously reinforced-concrete pavement (CRCP) reinforced with glass fiber-reinforced polymer (GFRP) bars located on Highway 40 West (Montreal, Quebec). The design procedures, construction details, performance, and monitoring results for a 306-m-long section of GFRP-CRCP are presented. Three different types of fiber-optic sensors were used to monitor the pavement behavior and to evaluate the long-term performance of this type of CRCP. The field inspection ran for 6 years after the time of construction, and the data covering 30 months were analyzed. The concrete crack width, concrete crack spacing and rate, concrete temperature, concrete strain, and GFRP-bar strain behavior were recorded and investigated. The GFRP-CRCP and a 94-m-long stretch of steel-CRCP on that highway were compared in terms of crack width, spacing, and rate. Site inspection showed that neither type of pavement exceeded the crack-width limit of 1.0 mm set by the available design standard for pavement structures. The crack rate of the CRCP reinforced with GFRP bars was generally lower than that with steel bars. Moreover, the field test results after 6 years under actual service conditions revealed that GFRP-CRCP provides very competitive performance in comparison to steel-CRCP. Lastly, design equations were developed and proposed to determine the longitudinal-reinforcement ratio for the GFRP-CRCP based on the available design standard.
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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".