Economic optimization for the rehabilitation of co-located mixed assets
Why this work is in the frame
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Bibliographic record
Abstract
Managing the rehabilitation of co-located infrastructure assets (pavements, pipelines, culverts, etc.) has become a major challenge for municipalities due to the varying rehabilitation requirements of these assets and the need for better coordination of rehabilitation works. Yet, most of the existing fund-allocation methods are not structured to address co-located infrastructure rehabilitation work in a systematic manner. This paper, therefore, extends the enhanced benefit-cost analysis (EBCA) optimization method that was developed earlier for a single asset type, to the case of co-located assets. The extended EBCA approach arrives at near-optimum funding decisions by achieving an equilibrium state at which fair and equitable allocations are made among all asset categories. Using a real case study consisting of bridges and culverts co-located in the right of way of a pavement network along with two different implementation strategies, EBCA proved to be able to arrive at near-optimum fund-allocations supported with a credible economic justification.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 it