Performance evaluation of advanced pavement materials by Bayesian Markov Mixture Hazard model

Optimized maintenance and management strategies are often emphasized in infrastructure management. Although such strategies serve to facilitate decision making, it is important to recognize that revolutions in asset management can come from improvements to hardware performance rather than managerial techniques. Underlying the managerial solutions, this study focused on the utility of various pavement materials and a special layer to support a performance-oriented asset management plan. This study compared the life expectancy and uncertainties of Stone Mastic Asphalt (SMA), Polymer Modified Asphalt (PMA), Rut-resistant Asphalt (RRA), Porous Asphalt (PA), and conventional hot-mix asphalt (HMA), which are widely introduced in field maintenance works. Problems associated with insufficient data due to short elapsed time and insufficient time-series performance data were mitigated by employing an advanced statistical method, the Markov mixture hazard model applying hierarchical Bayesian estimation. Empirical studies were conducted using historical performance data covering a period of 5 years (2002-2007) from 150 special monitoring sections in the K-Network. The results provide useful information for developing improved specifications for maintenance design and performance-based contracts that may lead to radical reform of infrastructure asset management. The Markov mixture hazard model with hierarchical Bayesian estimation is also a powerful tool for solving critical limitations in the post-evaluation of short-term projects.