Bibliographic record
Abstract
Defiance, Ohio recently completed a project involving the structural rehabilitation of two bridges. To optimize the strength of the repaired structures, an innovative solution was developed involving post-tensioning externally bonded carbon fiber laminates. This article describes this pioneering project. One of the objectives of the repair was to restore the flexural capacity of the beams by reintroducing the compressive force lost to deterioration of prestressing strands. The key to this project was the installation of the post-tensioned carbon fiber plates. To begin the project, the beams were jacked up to replace deteriorated bearing pads. The next step was to repair the deteriorated concrete on the prestressed box beam. Existing asphalt pavement then was removed and the beams were post-tensioned using fiber-reinforced polymer laminates. To complete the project, the asphalt was returned with new drainage to keep stormwater from running over the repaired beams. A structural health monitoring system was installed to provide information on the longevity of these bridges and to determine if these techniques are appropriate for other bridges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.078 | 0.008 |
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".