Long-term condition evaluation for stay cable systems using dead load–induced cable forces
Bibliographic record
Abstract
To ensure the safety of cable-stayed bridges, a long-term condition evaluation method has been proposed based on dead load–induced cable forces. To extract dead load–induced cable forces under random vehicle loadings, a novel approach is first developed by integrating influence lines with monitoring data. Then, based on the extracted dead load–induced cable forces, the evaluation algorithm for stay cable systems is presented. In the assessment algorithm, uniform and non-uniform characteristics are taken into account. Finally, the Third Nanjing Yangtze River Bridge, a typical large span cable-stayed bridge, is used to illustrate the effectiveness of the proposed methodology. As a result, the maximum relative error in extraction of dead load–induced cable forces accounts for 4.78% within the studied five stay cables. The precision of the extraction method is acceptable for practical applications since the relative error is less than 5%. Moreover, the bridge is continuously assessed using the dead load–induced cable forces for 5 years. Eliminating the influence of vehicle loadings, the condition of the bridge gradually degrades with time but still remains in good condition. The study not only provides a long-term condition evaluation method for stay cable systems but a dead load–induced extraction approach under random vehicle loadings, which will help bridge owners know well the condition of bridges to make appropriate maintenance decisions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".