Inspection and Maintenance of Bridge Stay Cable Systems
Bibliographic record
Abstract
In this synthesis effort, a worldwide search of information on inspection, repair, testing, and design of stay cable, was undertaken. On-line sources of information as well as engineering databases were examined. Contacts were made with a number of knowledgeable individuals for information. A questionnaire was prepared and distributed to all state and provincial departments of transportation in the United States and Canada. Completed questionnaires were received from 75% (27 of 36) of all known U.S. cable-stayed bridges and 81% (13 of 16) known cable-stayed bridges in Canada. Based on this information, various methods, approaches, and practices are explained in detail and their strengths and weaknesses identified. Specific approaches to inspection and repair are presented and discussed. Challenges in the inspection and maintenance of cable-stayed bridges are significant. The main tension elements (MTEs) within a cable bundle are, in most cases, hidden from the view of inspectors. Access to cables for visual inspections or nondestructive testing is generally dif- ficult and, in the case of the anchorage zones, nearly impossible. Those who are responsible for the inspection and maintenance of stay cables are faced with challenges for which proven and accepted methodologies and tools are limited and, in many cases, very costly. There are 36 cable-stayed bridges in the United States and 16 such bridges in Canada. As of 2005, the average age of cable-stayed bridges in the United States was 11.4 years. As these bridges age, the need for effective inspection and maintenance methods and tools becomes more acute.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".