A pavement management system formulated according to the specific needs and resources of a highway agency should ensure satisfactory pavement performance with minimal maintenance cost. The success of a pavement management system depends on the pavement condition data, accuracy of the prediction of pavement performance, and project- and network-level rehabilitation planning. Of these, the most labor-intensive, time-consuming, and costly component is the collection of pavement condition data. The inventory data include roughness, skid resistance, and surface distresses. Although mechanical instruments are available for rapid collection of roughness and skid resistance data, the collection of distress data for low-volume rural roads is still manual and hence labor-intensive and time-consuming. Subjective rating techniques prove to be a solution to this problem, but their reliability is poor due to the subjectivity associated with them. Therefore, there is a need for a rapid and cost-effective yet reliable method for collecting pavement condition data. The Prime Minister's Gram Sadak Yojana is a project launched in India by the Government of India to provide all-weather road connectivity to rural habitations. The investment is around 600 billion rupees ($15 billion) during the period 2000-2007. As part of the project, road stretches are prioritized for maintenance and reconstruction by using the pavement condition index. The quantification of the severity and extent of data collected is affected by each panel member's perception and judgment. To deal effectively with the subjectivity associated with human judgment of distress severity and extent, mathematical techniques of fuzzy set were used. Fuzzy membership functions were formulated for severity, extent, and relative importance of each distress with respect to maintenance. Fuzzy condition indices were used to prioritize the pavement sections by suitable fuzzy ranking methods. The effectiveness of the method is established by ranking pavement sections.
Samenvatting