Long-term condition evaluation for stay cable systems using dead load–induced cable forces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it