Evaluating Impact of Pavement Condition Sampling Advances on Life-Cycle Management

Over the past two decades, several new nondestructive technologies have been developed and applied in collecting raw condition data and processing them to produce useful condition input to infrastructure inspection, maintenance, and rehabilitation (IM&R) decision making aimed at minimizing expected total life-cycle cost. Such advances initially motivated the quantification of condition measurement uncertainty and the incorporation of this uncertainty in decision making. Following this development, the spatial variation of condition has been quantified and has led to the recent extension of decision-making methods to take into account sampling uncertainty and determine the optimal sample size, along with the other IM&R activities. In this paper, the evaluation of the contributions of the condition sampling–related advances to improved decision making is presented. An evaluation methodology is developed and subsequently applied to a realistic example facility. The methodology is based on comparing decision-making frameworks that reflect the advances of interest with those that do not. The basic idea behind comparing any two frameworks is to use each to produce optimal IM&R policies that are based on the specific assumptions they reflect and then to simulate these optimal policies within the framework reflecting the truth with regard to capturing the most realistic assumptions. The results of the application of this evaluation methodology indicate that the magnitudes of the value of the condition-sampling advances of interest are found to be appreciable in both expected total life-cycle cost and IM&R agency cost.