Integration of stochastic approaches in the life cycle cost analysis of sewer pipe applications

Yongliang Jin

International Journal of Production Economics, 2016, vol. 179, issue C, 35-43

Abstract: The objective of this paper is to integrate stochastic approaches in the life cycle cost analysis of sewer pipes. It is generally accepted that costs are of the greatest interest to the decision maker. The selection procedure primarily depends on the material availability as well as empirical information. Afterward costs in the maintenance phase lack consideration, which is mainly constrained by data availability and analysis techniques. This study focuses on the rigid pipe and evaluates the comparable application of reinforced concrete pipe (RCP), non-reinforced concrete pipe (NRCP), and vitrified clay pipe (VCP) in sewer systems. The life cycle cost analysis is performed throughout the material manufacturing, construction and maintenance phases. Particularly, the Markov chain model is used to predict systems' conditions that are used as a guidance for management in the maintenance phase. Under a given strategy, the cost estimate is extended to the steady state, providing a new cost comparison perspective. In addition, methods to evaluate the model uncertainty are also presented. Applying the bootstrapping approach, the analysis reveals that the Markov chains model demonstrates decent robustness. In the case study of a 24-in. pipe application, results show that NRCP demands the least initial building cost, while RCP is more cost effective considering longer service life time. Cost comparison and specific examples are also presented to demonstrate practical applications. This integrated approach allows decision-makers and stakeholders to understand and compare economic costs during alternative selections in practice. The methodology developed may also be applied in the life cycle analysis of other critical infrastructure systems.

Keywords: Life cycle cost analysis; Markov chains model; Stochastic; Rigid pipe; Sewer system; Sensitivity analysis; Uncertainty (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:179:y:2016:i:c:p:35-43

DOI: 10.1016/j.ijpe.2016.05.011

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