Current and future pavement maintenance prioritization based on rapid visual condition evaluation

Satisfactory maintenance of its highway network is essential for any nation 's economic growth. A pavement management system (PMS) formulated according to specific needs and resources of a particular highway maintenance agency would assure satisfactory pavement performance with minimal maintenance co st. Since the collection of detailed pavement condition data is extremely c ostly and time-consuming, innovative approaches for rapid data collection i s in increasing demand among highway agencies with limited PMS budgets. A t ime-saving and effective data collection approach based on subjective judgm ent is introduced by the writers for rating predominant distress types foun d in asphaltic pavements. Inclusion of both severity and extent ratings of distresses is expected to provide a strong basis for eventual maintenance c ost computations. The mathematical techniques of fuzzy sets are used to dea l with the subjectivity associated with human judgment of distress severity and extent. In addition, the relative importance of each distress type wit h respect to maintenance is also utilized in the determination of the combi ned condition index. Several fuzzy aggregation and ranking approaches are e xplored and the one with the highest computational efficiency is employed f or ranking pavement sections with respect to rehabilitation needs. Finally, a fuzzy pavement condition forecasting model is also developed by incorpor ating subjective probability assessments regarding pavement condition deter ioration rates, in the Markov transition process. Specific transition proba bility matrices for different distress types are used in this approach to o vercome the deficiencies of the traditional PCI approach. The potential app licability of the methodology is tested on the major pavement network of Sr i Lanka and its effectiveness and the execution ease are demonstrated.