Camber, prestress loss, and determination of distribution factors in pretensioned concrete girder bridges
Bibliographic record
Abstract
This research investigates prestress losses and camber of Prestressed Concrete bridge beams (PC Beams), and the performance and load testing of the SH 4 Bridge over the North Canadian River in Canadian County, OK. The bridge consists of 15 spans; each span is nominally 100 ft. in length. Each span was also designed and built with unique reinforcement details. An instrument-based structural monitoring program was implemented to measure strains and temperatures within hardened concrete. Materials testing was also performed. Camber measurements were taken at several intervals during the fabrication of the PC Beams and during the construction of the SH 4 Bridge and continuing through service. Several prestress loss analysis methods were performed and compared to gathered strain data. The purpose was to compare how different strand layouts and the inclusion of mild steel reinforcement in the bottom flanges of the girders affected long-term cambers and prestress losses. Static load testing was also performed on this bridge to determine the live load distribution factors of the girders. The research investigated whether distribution factor methods (DF’s) are accurate, and whether overestimating DF’s for external girders leads to unnecessary levels of prestressing reinforcement. Excess reinforcement can result in increasing camber and increasing prestress losses, and adversely affect cracking in end regions and the constructability of the bridge. Results from the research were compared to analytical methods prescribed in AASHTO codes. A finite element model was also made to recreate the load test and determine the effectiveness of the parapets and diaphragms in distributing live load.
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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 | 0.002 |
| 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 | 0.000 | 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 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".