Adaptive Optimization of Infrastructure Maintenance and Inspection Decisions under Performance Model Uncertainty

Infrastructure management systems assist agencies in making decisions regarding maintenance, repair, and reconstruction of the facilities under their jurisdiction. The objective in these decision-making tools is to minimize the total expected cost of managing a system of facilities over a given planning horizon. Recent optimization models account for the uncertainty in the selection of facility performance models through an adaptive control approach. In this paper, we extend the methodology to jointly determine when to inspect and what maintenance activity to perform, while taking into account uncertainty in measuring facility condition. A parametric study is performed to analyze the effect of the initial performance model uncertainty and bias on the expected total cost of managing a facility over a finite horizon. The parametric study shows that reducing model uncertainty leads, as expected, to lower costs. The results also indicate that reducing the initial variance in model uncertainty is more important than reducing the initial bias. In addition, our study shows that cost savings can result from relaxing the constraint of a fixed inspection schedule.