Infrastructure rehabilitation planning : combined system dynamics and optimization methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To improve the performance of the increasingly deteriorating infrastructure, effective strategic policies must be combined with optimum tactical rehabilitation plans. In the literature, limited efforts have focused on strategic policy analysis and its integration with tactical/operational planning. This paper; therefore, presents a framework that combines the strategic and tactical dimensions of infrastructure rehabilitation. At the strategic level, the System Dynamics (SD) modeling technique has been used to simulate the long-term effect of different policy scenarios on physical performance and backlog accumulation. The optimum policies are then used as inputs to a detailed tactical planning model. The objective of such model is to provide detailed fund allocation plans for the assets that need rehabilitation on a yearly basis. The proposed tactical model deals with large number of asset components over a 5-year plan to determine the best possible combination of repair types and timings. The paper compares the processing time and solution quality of three models that use different optimization approaches: Genetic Algorithms (GA); mathematical mixed integer programming; and Microeconomic-based heuristics. The paper discusses the conceptual formulation of the proposed integrated framework, the developments made so far, present limitations, and future enhancements.
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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 | 0.000 | 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 it