System-Level Deterioration Model for Reinforced Concrete Bridge Decks
Bibliographic record
Abstract
Generally, in existing bridge management systems, the deterioration is modeled based on visual inspections where the corresponding condition states are assigned to individual elements. In this case, limited attention is given to the correlation between bridge elements from a structural perspective. In this process, the impact of the history of deterioration on the reliability of a structure is disregarded, as it may lead to inappropriate conclusions. The improved estimate of service life of a bridge deck may help decision makers enhance intervention planning and optimize life-cycle costs. The objective of this research is to evaluate the system reliability of conventional bridges that were designed based on existing codes. According to the methodology developed in this study, the predicted element-level structural conditions for different time intervals are applied in the nonlinear finite-element model of a bridge superstructure, and the system reliability indexes are estimated for different time intervals. This method has been applied in simply supported traditional RC bridge superstructures designed according to Canadian bridge design standards. Based on the reliability estimates, these conventional bridges designed based on the current codes are found to be in good condition during the initial stages of their service life, but their condition degrades faster once corrosion in steel reinforcements is initiated and spalling of concrete becomes evident. The system reliability deterioration model can be integrated into existing bridge management systems by replacing the existing condition index by the reliability index, or by adding it to the assessment process as an additional parameter.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".