Abstract

Keeping asphalt-surfaced highways and roads in an acceptable condition is the major goal that departments of transportation and pavement engineers always strive to achieve. According to ASCE 2009 report card, an estimated spending of $186 billion is needed annually to substantially improve highways conditions. Hence, prediction models of current and future pavement condition should be rationalized and studied from cost effective perspective. In modeling the pavement condition, two major categories of models have been used: (1) deterministic and (2) stochastic. Existing models consider some factors that might be more critical than others, such as roughness measurements and distress information. They ignore other factors that could have a real effect on the accuracy of the pavement performance model(s), such as climate conditions. Therefore, the current research aims at developing a comprehensive condition-rating model that incorporates a wider range of possible factors significantly affecting flexible pavement performance. Data for this research were collected from the records of Nebraska Department of Roads (NDOR) called "Tab Files". In addition to a questionnaire that was designed and sent to pavement engineers and experts in North America. An integrated model was developed using Multi-Attribute Utility Theory (MAUT) and multiple regression analysis. Sensitivity analysis of the developed regression models is done using Monte-Carlo simulation to quickly identify the high-impact factors. Models' validation shows robust results with an average validity percent of 94% in which they can be utilized by Departments of Transportation (DOT) and/or Pavement Management Systems (PMS) as a useful tool for assessing and predicting pavement conditions