A Deterioration Model for Concrete Bridge Deck Using System Reliability Analysis
Bibliographic record
Abstract
Generally, in existing bridge management systems, deterioration is modeled based on visual inspections in which corresponding condition states are assigned to individual elements. Therefore, limited attention is given to the correlation between bridge elements from structural perspective. In this process, the impact of history of deterioration on the reliability of a structure is disregarded which may lead to inappropriate decisions. Improved estimate of service life of a bridge deck may help decision makers enhance the intervention planning and optimize life cycle costs. A reliability-based deterioration model is potentially an appropriate replacement for the existing procedures. The predicted element-level structural conditions for different time intervals are implemented to the non-linear Finite Element model of a bridge structure and the system reliability indices are estimated for different time intervals. The resulting degradation curve could be calibrated and updated based on the outcomes of the visual inspections. The aim of this research is to evaluate the system reliability of conventional bridges which have been designed based on the existing codes. The developed method utilizes the reliability theory and establishes a deterioration model for such bridges based on their failure mechanisms. This method has been applied to a simply supported concrete bridge superstructure designed according to the Canadian Highway Bridge Design Code (CHBDC). Based on the reliability estimates, the bridge is found to be in a good condition during the initial stages of its service life. However, its condition degrades faster once corrosion in steel reinforcements is initiated and spalling of concrete occurs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".