Evolutionary Algorithms for Optimizing Bridge Deck Rehabilitation
Bibliographic record
Abstract
Bridges are vital links in infrastructure road networks and require frequent maintenance and repair to keep them functional throughout their service lives. However, with most existing bridges being old and the funds available for repair being limited, the prioritization of bridges for repair, the allocation of the limited funds, and the selection of appropriate repair methods become complex optimization decisions. This is still true even when considering only one bridge component (e.g., deck) within a large network of bridges. In this paper, an integrated bridge deck management system is formulated with detailed life cycle cost analysis. The system's implementation on a spreadsheet program is briefly highlighted. Five evolutionary algorithms namely; genetic algorithms, memetic algorithms, particle swarm, ant colony systems, and shuffled frog leaping are then introduced and applied to optimize maintenance and repair decisions for various problems with different numbers of bridges. Based on the results obtained, the benefits of both the model formulation and the use of evolutionary algorithms are discussed, and the most suitable algorithm is selected for the proposed bridge deck management system. This paper contributes not only to the development of advanced management systems that can be adapted to various infrastructure types, but also to the implementation of new techniques for large scale optimization.
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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".