Maintenance Optimization Model for Existing Reinforced Concrete Bridge Piers Based on an Integrated Life-Cycle Cost and Performance-Based Design Approach
Why this work is in the frame
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Bibliographic record
Abstract
Coastal bridges are vulnerable to diverse hazards, including earthquakes, corrosion, and climate change. Regular maintenance is necessary to mitigate such vulnerability and prolong the lifespan of bridges. However, the high number of deficient bridges, combined with the limited financial capacity of most developing countries, calls for a pragmatic framework to optimize the maintenance program of bridges and preserve public funds. This study provides an integrated life-cycle and performance-based design approach for existing bridges in multihazard environments, aiming to improve their fragility and minimize their life-cycle costs (LCC). A methodology to evaluate the current damage condition of an existing deficient reinforced concrete bridge pier is introduced. Future degradation of the concrete column is then anticipated, considering the impact of both corrosion and climate change. Then, the maintenance LCC indicators are defined, and the maintenance model is formulated. An optimization model using the binary linear programming approach is employed to minimize the operational LCC of the bridge while maintaining the probability of damage within a user-defined upper limit for each damage state. Finally, a case study is used to illustrate the proposed approach. With this optimization model, it was possible to decrease the future LCC of two existing bridge piers in two different environments, marine atmospheric and marine splash, by 38.4% and 35%, respectively. Overall, the results of this study indicate that reducing the maintenance interval between future activities is beneficial only up to a certain limit, which varies depending on the seismic risk and environmental exposure.
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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 | 0.000 |
| 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