Optimal infrastructure management decisions under uncertainty

The planning of maintenance and rehabilitation activities for transportation facilities uses information on facility condition from two sources: measurement and forecasting. Both of these sources are characterized by the presence of significant uncertainties, which have important life-cycle cost implications. State-of-the-art decision-making models ignore the uncertainty either in one or both sources of information. This paper presents a methodology (the Latent Markov Decision Process) that explicitly recognizes the presence of random measurement errors in the measurement of facility condition. The methodology can also be used to quantify the "value of more precise information," which allows an agency to evaluate measurement technologies of different precisions and costs. A parametric study, which demonstrates such an evaluation in the case of highway pavements, is performed.