Calibration and Application of Treatment Performance Models in a Pavement Management System in Tennessee

Abstract

The usage of locally calibrated performance models and other related parameters is necessary in order to utilize the pavement management system (PMS) for maintenance strategy decision-making. In this study, the performance models of typical asphalt resurfacing treatments used in Tennessee were calibrated for the PMS by investigating historical maintenance projects. The multiple regression method was employed to analyze the influence of pretreatment PSI, traffic level, overlay thickness, and milling depth on the post-treatment performance curves. Specific designs of asphalt overlays and performance classes were determined according to the regression analysis. The performance curves for each identified treatment method at different performance classes were established and calibrated for the PMS. Rehabilitation trigger values, typical treatments and decision trees for pavement with different functional classes were determined and designed by investigating the historical maintenance projects. Utilizing the calibrated models, pavement maintenance optimization analyses were performed on a network level. The study has found that pavement with lower pretreatment roughness, thick overlay, and deep milling deteriorated at slower rate. Pavement with high traffic levels tended to have lower post-treatment roughness. The distress condition decreases much faster than smoothness. The most cost-effective network level maintenance strategy can be determined by conducting a network optimization analysis with a sufficient high budget limit. It is recommended that an optimized maintenance budget allocation should include combining cost-effectiveness, rolling unused budget over fiscal years, and re-distributing budget among different maintenance regions.