Extending Service Life of Stormwater Drainage Pipes with Proactive Maintenance Tools

Abstract

Aging infrastructure is a significant concern to many system operators who might struggle to operate, maintain, and improve systems and infrastructure assets due to spatially buried assets over a large area, uncertainty about their condition, and lack of a comprehensive planning. These challenges often lead to a reactive approach to maintenance, causing emergency situations due to unexpected asset failures. This study proposes a suite of risk-based asset management plan tools to extend the service life of a drainage pipe network with a length of 580 km managed by a city council in Australia. The proposed management plan tools are based on a reactive–proactive approach to manage risk in which proactive condition monitoring is applied to critical and important pipes for detecting failure conditions in a timely manner. A sample of critical closed-circuit television (CCTV) inspected pipes selected from pipe network is used to predict the current and future condition of the pipe network using a Markov deterioration model. The predicted condition is used to support various implementation tasks of an asset management plan and accounting requirements. The key findings of the case study include a calibrated Markov deterioration curve showing slow rate of deterioration for a drainage network, average service life estimation (120–180 years) for drainage pipes, lowest-cost inspection interval determination (i.e., 7–14 years) for detecting poor condition, and estimated annual inspection and replacement cost for a drainage network. A significant number of local governments and city councils around the world manage drainage pipe networks as high capital asset value. Extending the service life of drainage pipes with reduced life-cycle cost and managed failure risk is a challenge and an essential task for asset management. This study introduces a proactive–reactive asset management strategy and a suite of decision support tools to assist in achieving such challenging tasks. For example, instead of using the common design service life of 80–100 years for asset renewal planning, this study demonstrates, through a case study, that service life can be extended to 120–180 years, resulting in significant savings of capital investment. To control failure risk and reduce life-cycle cost during the extended service life, a suite of decision support models including an optimal inspection model, a failure risk model, and others can be used to justify annual maintenance funding, timely detecting poor condition, and suggesting appropriate maintenance and rehabilitation planning. The results show that the inspection interval can be 7–14 years, and the Markov deterioration model predicted that the current network has 7% of pipe in a failure condition, which will increase by 1.67% over the next 10 years, which can justify renewal funding.