New life-cycle costing approach for infrastructure rehabilitation

Purpose - Several rehabilitation planning methods are reported in the literature for public infrastructures, such as bridges, pavements, sewers, etc. These methods, however, are limited to specific types of infrastructures. The purpose of the present research is to develop a novel and generic method for Maintenance and Rehabilitation Planning for Public Infrastructure (Mamp;RPPI), which aims at determining the optimal rehabilitation profile over a desired analysis period. Design/methodology/approach - The Mamp;RPPI method is based on life-cycle costing (LCC) with probabilistic and continuous rating approach for condition states. The Mamp;RPPI 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. It also uses genetic algorithm (GA) in conjunction with Markov chains in order to find the optimal rehabilitation profile. A case study is presented with a comparison between the traditional Markov decision process (MDP) and the newly developed method. Findings - The new method, which generates lower LCC, is found practical in providing a complete Mamp;R plan over a required study period, compared to a stationary decision policy with the traditional MDP. In addition, GA is found useful in the optimization process and overcomes the computational difficulties for large combinatorial problems. Research limitations/implications - The implementation of the developed models is limited to only four alternatives/actions. However, the developed models and framework are superior for MDP. Practical implications - The developed methodology and model play essential roles in the decision-making process. Originality/value - The new method is beneficial to researchers and practitioners. It is developed for a single facility; however, it provides a major step towards a broader infrastructure management system and capital budgeting problems.