Pavement Network Maintenance Optimization Considering Multidimensional Condition Data

Abstract

Pavement management systems inventory historical and current conditions of roadway networks, predict the future conditions of such networks, and suggest schedules for maintenance, repair, and rehabilitation activities. Such systems typically rely on a composite condition index, a one-dimensional and often discrete measure of the overall structural health and/or serviceability of pavement. The index is used during deterioration modeling, user and agency cost estimation, and selection and scheduling of maintenance activities. Pavement can suffer from a large number of related but distinct distresses. Difficulties associated with unobserved heterogeneity have hampered efforts to accurately model deterioration through composite condition indexes. At the same time, optimization techniques used to generate recommended maintenance plans have been shown both to be sensitive to deterioration model specification and to become computationally intractable as condition data increase. This research describes how a large network of related sections of pavement, each one of which may be plagued by a number of different distresses, can be managed without reducing condition data to a composite index. The use of approximate dynamic programming mitigates the curse of dimensionality that has haunted distinct Markov decision problem formulations of the maintenance optimization problem and limited their complexity. A computational study illustrates how the proposed approach leads to more sophisticated maintenance decision rules, which can be used to ensure the suggestions of pavement management systems more closely match engineering best practices. The use of multidimensional condition data can also yield more accurate deterioration models and cost estimates. The techniques introduced in this paper in the context of pavement management could easily be applied within any infrastructure management system.