Comparative Analysis of Life-Cycle Costing for Rehabilitating Infrastructure Systems

Abstract

Metro systems usually offer an attractive alternative for mass transit transportation system in most large cities. Such infrastructures require proper maintenance and rehabilitation (M&R) programs to maintain them within an acceptable level of operational and safety performance. The Markov decision process (MDP) has been widely used to find the optimal M&R decision policy for situations that deal with uncertainties. A drawback of the traditional MDP approach is that it uses discrete number of states in the analysis as well as a stationary transition probability matrix (TPM). Also the MDP is based on the Markovian or “memory-less” property, which is not necessarily the fact for all aging infrastructures. This research presents a case study on a deteriorating slab in a Montreal metro. The traditional MDP is employed with linear programming to determine the optimal rehabilitation profile. Three different methods are employed for calculating life-cycle cost: (1) the average expected discount cost per time period that is normally used with the traditional MDP; (2) continuous rating approach; and (3) dynamic or time-dependent TPM. Results revealed that the continuous rating approach provides lower values compared to the traditional approach. Dynamic TPM reflects better the infrastructure behavior but necessitate additional data gathering. This research mainly benefits metro management agencies and enhances the MDP practice by overcoming some downsides of the traditional methodology.