LIfe cycle cost for rehabilitation of public infrastructures : application to Montreal metro system.
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
According to the National Guide to Sustainable Municipal Infrastructure (InfraGuide), Canadian municipalities spend $12 to $15 billion annually on infrastructure; however, this does not seem to be enough to maintain ageing infrastructures and rehabilitate them to higher safety standards. The solution according to InfraGuide is "to change the way we plan, design, and manage infrastructures" . Several rehabilitation planning methods are reported in the literature for public infrastructures, such as bridges, pavements, sewers, or others. These methods, however, are limited to specific types of infrastructure. In this research, a novel method for Maintenance and Rehabilitation Planning for Public Infrastructure (M&RPPI) is developed. One that is generic for any type of public infrastructure. The method aims at determining the optimal rehabilitation profile over a desired analysis period. Specifically, it will determine the best type of rehabilitation intervention, and its optimal timing. The M&RPPI method is based on life-cycle costing (LCC) with probabilistic and continuous rating approach for condition states. The M&RPPI also uses a new approach of " dynamic " Markov chain to represent the deterioration mechanism of an infrastructure and the impact of rehabilitation interventions on such infrastructure. As an optimization technique, genetic algorithm (GA) is used in conjunction with Markov chains in order to find the optimal or quasi-optimal rehabilitation profile. The way GA communicates with the transition probability matrices (TPM) is described. In addition, a new directed-GA approach was developed in order to guide the optimization process toward the final solution. Finally a computer program using Excel and VBA macros is developed in order to prove workability of the developed method. The developed M&RPPI methodology is applied to the deterioration problem of Montreal Metro system. In order to validate the performance of the proposed methodology, three different types of analysis are performed using: (1) traditional Markov decision process (MDP) that uses a discrete rating scale, (2) continuous rating method, and (3) the proposed M&RPPI method with GA optimization technique. Results show the benefits of using continuous rating in contrast with discrete method. They also demonstrate the superiority of GA compared to other optimization methods. In addition, the proposed M&RPPI method provides a complete M&R Plan over a required study period, not only a stationary decision policy. Finally, the M&RPPI is a major step towards a broader infrastructure management system, addressing network-level problems
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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