Bibliographic record
Abstract
The need for rapid construction or replacement of highway bridge decks can be addressed by precast concrete elements reinforced with Glass Fiber Reinforced Polymer (GFRP) bars with cast-in-place joints made using Ultra-High Performance Concrete (UHPC). This thesis investigates the bond between GFRP bars and UHPC and splice length optimization to obtain narrow joints and simplified bar geometries. Multiple linear regression analyses of existing bond data indicate that the bar’s Young’s Modulus and embedded length are the most significant parameters that influence the average bond strength of sand-coated GFRP bars in UHPC: increasing either decreases the average bond strength. Linear-elastic uncracked Finite Element analysis of pull-out specimens indicates that reinforcing bars with low Young’s Moduli have highly non-uniform bond distributions along their length and so exhibit high peak bond stresses and low average bond strengths. The higher average bond strengths observed for High Modulus (HM) GFRP bars compared to Low Modulus (LM) GFRP bars is likely because the HM GFRP bars have lower interlaminar shear strength. A methodology for GFRP reinforcement design that synthesizes provisions from the Flexural Design Method in the Canadian Highway Bridge Design Code including an additional new step to determine bar splice lengths in UHPC was developed. Splice lengths and bond resistance factors for HM GFRP bars in UHPC are determined by reliability analysis to resist either bar stresses due to the factored applied moments or the mean ultimate tensile strength of the bar. A significant reduction in splice length can be achieved if splices are designed to resist the bar stresses at factored applied moments. A new resistance factor of 0.5 for bond of GFRP bars in UHPC is also recommended.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".