Using Advanced Inspection Technologies to Support Investments in Maintenance and Repair of Transportation Infrastructure Facilities

Abstract

We present a statistically rigorous and computationally efficient framework that can exploit the extensive capabilities of advanced inspection technologies to support investment decisions in maintenance and repair of transportation infrastructure facilities. The framework consists of two components: a state-estimation problem that involves processing arrays of condition data and using them to develop condition forecasts; and an optimization problem whose solution yields maintenance and repair policies. Through a computational study, we illustrate how the framework can be used to quantify the effect of uncertainties both in the deterioration process and in the data collection process on the optimal life-cycle costs of managing infrastructure facilities. We also show how the framework can be used to quantify the benefits associated with combining inspection technologies to monitor infrastructure facilities, and therefore can serve as a tool to develop deployment strategies for these technologies.