Multiyear Maintenance and Rehabilitation Optimization for Large-Scale Infrastructure Networks: An Enhanced Genetic Algorithm Approach
Bibliographic record
Abstract
Multiyear network maintenance and rehabilitation optimization is a key, longstanding challenge for infrastructure asset management. Although genetic algorithms (GAs) have been widely used as the default optimization tool, successes were limited to small-scale networks. As the network size increases, the performance of conventional GAs quickly deteriorates because the traditional crossover and mutation operations disrupt promising solution compositions and drastically reduce the likelihood of obtaining a feasible solution. To address this gap, this paper introduced an enhanced GA that pivots on two innovations: a new crossover technique that swaps annual plans as a block of genes; and a novel mutation technique that incorporates linear programming (LP) to solve annual plans with a randomly perturbed budget profile. Both operations preserved the integrity of individual annual plans throughout the evolutionary process and enhanced local search capabilities. The hybrid LP-GA was tested with two practical case studies, one with a small-scale sewer network flushing program, and the other involving 13,610 pavement segments. Both case studies showed that the proposed algorithm quickly converged with 100% feasible solutions to optimum or near-to-optimum solutions. Through this work, we offered a sophisticated algorithmic tool for infrastructure planning, setting a stage for further advances in the domain.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".