Adjacency Modeling for Coordination of Investments in Infrastructure Asset Management
Bibliographic record
Abstract
Departments of transportation and municipalities are expected to implement infrastructure management systems powered by analytical tools. The tools perform long-term strategic analysis capable of identifying alternatives that achieve the most cost-effective solution and that provide sustainability to networks of infrastructure assets. However, results from such analyses reflect uncoordinated programs of works represented by actions scattered across time and space. The implementation of strategic analysis results as they emerge from life-cycle optimization bring about many small contracts, which translate into constant disruption of services for users and higher costs to the government. In addition, uncoordinated actions may result in utility cuts or premature damage to recently rehabilitated assets. This paper adapts classical time–space adjacency modeling to translate results from strategic analysis into coordinated tactical and operational plans addressing the aforementioned drawbacks. A case study of Kindersley, Saskatchewan, Canada, is used to illustrate the proposed approach for coordinating the program of works of pavements, sanitary and storm sewers, and water mains for one of the scenarios of the original strategic analysis. The approach can incorporate time and space considerations among neighboring assets for selected compatible actions (investments) guided by a heuristic simulation that follows the guiding objectives of the original optimization. The results from coordinated actions are compared with results from classical life-cycle optimization to determine the degree of optimality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".