Optimization of Hot-Mix Asphalt Rolling Pattern Using Ground-Penetrating Radar and Markov Decision Processes

Abstract

Ground-penetrating radar (GPR) has been used for nondestructive evaluation of hot-mix asphalt (HMA) pavements including density prediction. HMA density is an acceptance quality characteristic (AQC) used across the U.S. AQCs are the basis for quality control and acceptance, and are used by agencies to determine contractors’ pay. Recently, roller-mounted GPR was introduced to monitor HMA density in real-time, enabling roller operators to make more informed decisions to avoid under- and over-compaction of HMA layers. In this study, a Markov decision process (MDP) is formulated to represent the rolling pattern optimization problem. This formulation accounts for GPR prediction error, density spatial variability, and uncertainty in density progression. The introduced MDP provides contractors and roller operators with a tool to minimize operational costs while achieving target density, thereby enhancing pavement service life and reducing maintenance activities. Data collected from several field projects were used in the MDP formulation. The benefits of using the developed MDP for compaction decisions were demonstrated using project data from Illinois, U.S. The analysis was conducted for an actual project scenario under Illinois’ quality control for performance (QCP) program and was extended to a hypothetical pay for performance (PFP) scenario to evaluate the generalizability of the approach under different risk levels. Compared with an experienced roller operator, MDP decisions reduced the construction time by 40.3% and 18.1% and increased the revenue by 9.7% and 50.2% for the QCP and PFP scenarios, respectively. Additional benefits in energy savings, reduced construction-related delays, and improved worker safety are expected.