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