Development of pavement management system for low volume roads

Abstract

Low volume roads which are playing a pivotal role in community development, transport of people, goods, and services. Limited funding, inability to collect extensive data, subjective ad-hoc maintenance decision making has resulted in suboptimal maintenance level for these road networks. Therefore, there is a need to develop a cost-effective simplified approach for network-level decision-making to assist in pavement maintenance management. The research is focused on identifying a cost-effective method to collect the required data to assess the pavement condition. Also, it comprises by identifying the maintenance strategies and respective thresholds for low volume roads which can be used in the decision tree to support the decision-making process. Moreover, developing an analytical framework for optimize network level pavement condition, incorporating the decision tree to eliminate the limitations of subjective maintenance decision making is the main analysis approach in this study. Finally, to incorporate objective functions which cannot be accommodated in single objective analysis, a multi-objective optimization analysis scheme is developed. The applicability of smartphone-based roughness data was explored to assess the pavement condition by validating with the conventional class III roughness measurement equipment. It was found that the reliability of smartphone roughness was high with correlation of 0.84. In addition to that, roughness capability of forecast distress level, prediction of overall condition was evaluated. The multiple regression models shown that raveling, pothole, cracking, patching have a good relationship with roughness progression in wider roads while edge breaking, and edge gap have significant impact in narrow roads. In the analysis framework genetic algorithm-based optimization approach was found as the best evaluation tool compared with the engineer’s objective decision making and linear programming especially in limited budget conditions. Moreover, the use of socio-economic importance as a secondary objective was better option, which gives priority to roads having higher socio-economic importance.