Holistic Analysis of Infrastructure Deterioration and Rehabilitation Using System Dynamics
Bibliographic record
Abstract
Rehabilitation programs are essential for efficiently managing large networks of infrastructure assets and sustaining their safety and operability. While numerous studies in the literature have focused on various aspects of infrastructure rehabilitation, limited efforts have investigated the overall dynamics of the process. The research presented in this paper, therefore, takes a holistic view to investigate the dynamics that affect rehabilitation decisions and the long-term performance of an infrastructure network. First, the interactions among the main parameters related to asset deterioration, rehabilitation actions, and cost accumulation have been analyzed using causal loop diagrams (CLDs). Afterward, a system dynamics (SD) model has been developed based on the CLDs and the underlying mathematical relations among the various parameters. The SD model was then tested on a network of 1,000 assets over a 50-year plan, considering a range of rehabilitation policies regarding budgets, possible rehabilitation actions, and fund allocation options. The model proved to be a practical and effective tool for quick assessment of the long-term impact of rehabilitation policies on infrastructure performance and costs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".