Rational Approach for Replacement of Conventional Reinforcement with Macro-Synthetic Fibers in Bridge Deck Overlays
Bibliographic record
Abstract
With the increasing use and acceptance of macro-synthetic fibers as a viable and economical reinforcing alternative to conventional steel reinforcement it is important to have a rational approach when designing fiber reinforced concrete mixtures to replace shrinkage and temperature reinforcement in concrete. In applications such as bridge decks and pavement overlays, steel reinforcement is typically specified based on the tensile capacity required to resist tensile forces generated by restrained shrinkage stresses. A rational approach to designing fiber reinforced concrete for this type of application is presented. The added value of increased durability, through reduced permeability and decreased shrinkage cracking is also discussed. Case studies of structures rehabilitated using fiber reinforced concrete overlays designed using this approach will be presented. These structures, located throughout the US and Canada, were each designed to provide fiber reinforced concrete with equivalent tensile capacity to the specified shrinkage and temperature cracking control reinforcement. Quality control testing procedures that were implemented on each structure to verify adequate postcrack tensile capacity will also be presented. In general, properly designed synthetic macro-fiber reinforced concrete has been shown to be an economical reinforcing alternative that increases speed of construction and decreases the susceptibility of concrete to shrinkage cracking without sacrificing aesthetics or ride quality.
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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".