Condition evaluation of suspension bridges for maintenance, repair and rehabilitation: a comprehensive framework
Bibliographic record
Abstract
To indicate health status of bridges and help stakeholders make decision on maintenance, a comprehensive framework has been proposed to evaluate structural efficiency of suspension bridges using analytic hierarchy process. First, the analytical hierarchy model (i.e. hierarchical network together with data aggregation algorithms) has been constructed using multi-source data, including visual inspection, non-destructive testing and structural health monitoring information. Age-dependent variable weight theory is developed to account for the service history of elements ensuring the alignment of variation trend of index weights with the objective law in bridge maintenance and management activities. To overcome the limitations of factor-based variable weight model for weight adjustment, the factor- and age-based variable weight model has been adopted for data aggregation. Finally, four cases are used to test the effectiveness of the three models (i.e. constant weight model, factor-based variable weight model and factor- and age-based variable weight model). By comparing the performance of the three models, the recommended maintenance strategy derived from factor- and age-based variable weight model aligns more with the actual strategy than the other two. The factor- and age-based variable weight model outperforms both the factor-based variable weight model and constant weight model in helping bridge owners make 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".