Scheduling Inspection and Renewal of Large Infrastructure Assets

Abstract

A decision framework is introduced to assist municipal engineers and planners to optimize decisions regarding the renewal of large infrastructure assets such as water transmission pipes, trunk sewers, or other assets with high costs of failure, inspection, and condition assessment. The proposed decision framework identifies a need for immediate intervention or, alternatively, enables optimization of the scheduling of the next inspection and condition assessment. The deterioration of the asset is modeled as a semi-Markov process and is thereby discretized into condition states. The waiting times in each state are assumed to be random variables with “known” probability distributions. If pertinent data are scarce (as is typical in most municipalities) these probability distributions can be initially derived based on expert opinion. These distributions will then be continually updated as observed deterioration data are collected over time. Monte Carlo simulation is used to calculate the distributions of the cumulative waiting times. Conditional survival probabilities are used to compile age-dependent transition probability matrices in the various states. The expected discounted total cost associated with an asset (including cost of intervention, inspection, and failure) is computed as a function of time. The time to schedule the next inspection/condition assessment is when the total expected discounted cost is minimum. Immediate intervention should be planned if the time of minimum cost is less than a threshold period (2 to 3 years) away. A computer program is prepared for demonstration and proof of concept. The decision framework lends itself to a computer application fairly easily. Although usable in its current form, this paper identifies some issues that require as yet unavailable data as well as more research in order to develop the framework into a comprehensive application tool.