Maintenance Cost Optimization for Bridge Structures Using System Reliability Analysis and Genetic Algorithms
Bibliographic record
Abstract
Maintenance cost optimization and performance prediction of bridge structures have become important challenges in bridge management systems. The performance of bridge structures should be carefully monitored, especially in severe climatic conditions. The objective of this study is to develop a rational method that predicts the most cost-effective intervention schedule for bridges, where the structural safety is maintained with the minimum possible lifecycle cost. The framework functions through (1) a central database that contains the asset inventory along with the maintenance actions list, (2) a biquadratic system reliability–based deterioration model, (3) an intervention effect model that simulates the effect of undertaking various intervention scenarios on the bridge superstructure performance, (4) a financial model that computes the lifecycle costs throughout the planning horizon, and (5) an optimization model that utilizes a genetic algorithms engine to compare the different intervention scenarios and selects the most cost-effective one. This method is applied to a simply supported bridge superstructure case study, designed in accordance with Canadian highway bridge design standards. The results indicate that undertaking less costly minor repair actions may considerably reduce the lifecycle costs as a result of decreasing the number of costly major interventions. The optimum scenario resulted in an equivalent uniform annual cost of US$8,277 per year, which shows 4.5 times cost saving as compared with the conventional scenario where only major repairs are performed. This innovative combination of reliability analysis, nonlinear finite-element modeling, and genetic algorithms optimization supports asset managers in long-term planning and ensures undertaking rational and objective decisions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".