Large-Scale Asset Renewal Optimization Using Genetic Algorithms plus Segmentation
Bibliographic record
Abstract
Civil infrastructure assets require continuous renewal actions to modernize inventory and sustain operability. However, allocating limited renewal funds among numerous asset components represents a complex optimization problem. Earlier efforts using genetic algorithms (GAs) optimized medium-sized problems, yet exhibited steep performance degradation as problem size increased. In this research, data compression is first used to cluster and abstract the large data of a network-level problem. Optimizing compressed models, however, did not result in high quality solutions. To address large size problems, a GA with segmentation approach was introduced. Segmentation breaks down a large-scale network-level problem into segments, allocates budget based on the relative criticality of the segment, and combines the results of all segment optimizations. The proposed GA with segmentation mechanism has been tested on different sized problems and was able to optimize very large problems with no performance degradation. The proposed GA with segmentation method is simple and logical; furthermore, it can be used on variety of asset types to improve fund allocation for infrastructure renewal.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".