Strengthening of dapped timber beams using glass fibre reinforced polymer bars
Bibliographic record
Abstract
An economical rehabilitation scheme to strengthen creosote-treated dapped timber stringers in both flexure and shear is proposed. An experimental program was conducted to test stringers under monotonic load in three-point bending load configuration. Eight control beams with no reinforcement, 12 reinforced for flexure only, and 6 reinforced for flexure and shear were tested. Glass fibre reinforced polymer (GFRP) dowel bars were placed at an angle of 60° from the horizontal to reinforce for shear and to bridge the dapped end. Test results from previous studies by Gentile et al. (C. Gentile, D. Svecova, and S.H. Rizkalla. ASCE Journal of Composites for Construction, 6(1): 11–20, 2002.) and Svecova and Eden (D. Svecova and R.J. Eden. Canadian Journal of Civil Engineering, 31: 45–55, 2004) are combined with the results of this investigation for a total sample size of 54 beams. Large sample sizes are essential to study the performance of timber beams strengthened using GFRP bars in various schemes. An overall increase of 70% in the 10th percentile ultimate strength was obtained for stringers reinforced for both flexure and shear. Ductility was increased with the addition of the GFRP reinforcement, but the modulus of elasticity appeared to be unaffected.Key words: timber, bridge, glass fibre reinforced polymer, rehabilitation, modulus of rupture, analysis.
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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) | 0.001 | 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".