Fatigue Performance of 60-Year-Old Bridge Reinforced Concrete Girders Strengthened in Shear with CFRP Sheets
Bibliographic record
Abstract
Bridges situated in northern climate regions, which face severe environmental conditions and daily fatigue loading, are prone to accelerated deterioration and corrosion of their components. The application of carbon fiber–reinforced polymer (CFRP) sheets bonding to the surface of bridge elements has emerged as an attractive solution for enhancing bridge strength. Past studies and field implementations have effectively showcased the viability of this approach in strengthening bridges. An exceptional opportunity arises with the deconstruction of a bridge in Canada, providing a unique chance to assess and study the condition of reinforced concrete elements strengthened with CFRP. These elements have endured real service conditions, including fatigue loads and exposure to aggressive environmental factors. This paper presents the experimental results of a research program that aimed to investigate the residual fatigue life and capacity of 60-year-old reinforced concrete bridge girders, which were strengthened using CFRP sheets. The study focuses on assessing the performance of these girders under different test conditions, providing valuable insights into their remaining fatigue life and load-carrying capabilities. The two 60-year-old girders have been strengthened with CFRP for the last 10 years of the service life of the bridge. The two full-scale girders were tested under 2 million fatigue load cycles and then tested monotonically until failure at the structural lab of the University of Sherbrooke. The test results revealed that the CFRP-strengthening technique can extend the service life of the bridge element and enhance its shear capacity. The CFRP–concrete interface and CFRP sheets showed excellent bonding behavior, as no damage-debonding failure or tensile rupture occurred until the formation of the diagonal shear crack.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".