Multi-objective optimization model for automated risk-based inspection planning for concrete bridges
Bibliographic record
Abstract
This paper addresses the critical challenge of optimizing inspection planning for reinforced concrete bridges, considering budgetary and operational constraints. The developments presented here focus on bridge decks. This entails identifying which bridges require inspections, the optimal timing for these inspections, and the most effective non-destructive evaluation inspection methods to employ. A multi-objective optimization model is developed, leveraging the non-dominated sorting genetic algorithm-II and probabilistic modeling. The developed model strikes a balance between minimizing the structure risk of failure, maximizing inspection effectiveness, and optimizing direct costs and impact costs of inspections. The developed model is expected to provide transportation agencies and infrastructure managers with a robust decision-support tool for automated, efficient inspection planning for this class of bridges, that increases inspection effectiveness and enables condition- and risk-driven utilization of advanced non-destructive evaluation methods. The developments here lay the groundwork for integrating inspection outcomes from these methods in selecting subsequent intervention strategies.
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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".