Reliability-Centric Maintenance Planning for Bridge Infrastructure: A Novel Method Based on Improved Electric Fish Optimization
Bibliographic record
Abstract
Bridge infrastructure provides an important effect on contemporary transportation networks, and its upkeep is significant for ensuring public safety and reducing economic impacts. Nevertheless, the aging and degradation of bridge structures present considerable challenges for asset managers, who must navigate the necessity of maintenance against constrained financial resources. Conventional maintenance approaches typically emphasize reactive repairs, which can result in elevated lifecycle expenses and risk structural integrity. This paper introduces an innovative framework aimed at optimizing bridge maintenance expenditures while maintaining structural safety. The proposed methodology incorporates a reliability-based deterioration model, an intervention effect model, a financial model, and an optimization model empowered by an Improved Electric Fish Optimization (IEFO) algorithm. The framework is demonstrated through a case study of a reinforced bridge framework designed according to the standards of Canadian highway bridge design. The findings illustrate that the proposed methodology can substantially lower lifecycle costs by investigating the most economical maintenance strategies, including minor repairs that can postpone the necessity for expensive major interventions. The optimal scenario identified by the IEFO algorithm yielded lower equivalent uniform annual costs in comparison with the traditional scenario focused solely on major repairs. This research advances the field of data-driven maintenance planning for bridge infrastructure, empowering asset managers to make well-informed decisions that effectively balance cost and safety considerations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".