Optimal infrastructure condition sampling over space and time for maintenance decision-making under uncertainty

Infrastructure management is the process through which inspection, maintenance, and rehabilitation (IM&R) decisions are made to minimize the total life-cycle cost. Measurement, forecasting, and spatial sampling are three main sources of errors introducing uncertainty into the process. The first two uncertainties are captured in the infrastructure management literature. However, the third one has not been recognized and quantified. This paper presents a methodology where the spatial sampling uncertainty in question is captured and the sample size is incorporated as a decision variable in an optimization framework. An illustrative realistic example is presented to demonstrate an application of the developed framework. The results indicate that by not addressing the sampling uncertainty and decisions, the optimum IM&R decisions would not be achieved, and consequently, marked unnecessary overspending could take place.