In the current era, the highway environment is characterized by funding limitations, aging facilities, and increasing user expectations. In light of these trends, asset managers at most highway agencies have a fiduciary responsibility to apply management strategies and practices that not only are most cost-effective in a life-cycle context but also are in the best interests of taxpayers and highway users. To identify and implement effective rehabilitation strategies and practices, highway agencies need to verify information that was typically based on expert opinion, with reliable asset performance prediction tools and treatment service life estimates. This study provides an enhanced methodology to analyze pavement rehabilitation data and to develop estimates and ranges of treatment service lives. Pavement rehabilitation data typically consist of repeated measurements that form an unbalanced three-level nested structure, making analysis quite challenging. In this study, mixed linear modeling techniques are deployed to accommodate this structure and thus lead to less biased estimations compared with traditionally used approaches. The developed framework is applied to quantify the effectiveness of seven common rehabilitation treatments applied by the Indiana Department of Transportation in regard to their service lives and to validate the treatment service life values presented in the agency's current design manual.
