Integration of stochastic deterioration models with multicriteria decision theory for optimizing maintenance of bridge decks
Bibliographic record
Abstract
This paper presents a new approach to optimizing the maintenance of concrete bridge decks. This approach combines a stochastic deterioration model and a multiobjective optimization model. The stochastic deterioration model is based on the first-order Markov chain, which predicts the probabilistic time variation of bridge deck conditions. The multiobjective optimization model takes into account two important and conflicting criteria: the minimization of maintenance costs and the maximization of the network condition. This approach achieves the best compromise between these competing criteria while considering the uncertainty in bridge deck deterioration. The feasibility and capability of the proposed approach are demonstrated with field data for a sample network of bridge decks obtained from the Ministère des Transports du Québec database. This example illustrates the effectiveness of the proposed approach in determining the optimal set of maintenance alternatives for reinforced concrete bridge decks when two or more relevant optimization criteria are taken into consideration.Key words: concrete bridge deck, maintenance management, multicriteria optimization, Markov chain, deterioration model.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".