Bridge Deck Management System with Integrated Life-Cycle Cost Optimization
Bibliographic record
Abstract
Bridge management systems can be classified as one of two types: network level or project level. The former type is concerned with the prioritization of bridges for inclusion in an upcoming maintenance, repair, and rehabilitation program, and the latter focuses on the repairs that suit the components of a selected bridge. Even though these types are interrelated, most bridge management research treats them as separate aspects. A comprehensive framework is presented for a bridge deck management system that aims at integrating project-and network-level decisions into a unified model to optimize costs at both levels. The novelty of the proposed approach stems from three main aspects: incorporating project-level repair options along with their performance improvements and cost implications; incorporating many flexible and practical features such as variable yearly budget limits, variable yearly discount rates, and optional methods for handling project-level repairs (e.g., single or multiple visits); and using a powerful genetic algorithm-based optimization to consider both project- and network-level variables into bridge life-cycle cost optimization. The proposed model and its implementation are described, and an example application is presented. Although this research focuses on bridge decks, details on future improvements to incorporate all bridge components are outlined.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".