Current and Future Pavement Maintenance Prioritization Based on Rapid Visual Condition Evaluation

Abstract

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 cost. Since the collection of detailed pavement condition data is extremely costly and time-consuming, innovative approaches for rapid data collection is in increasing demand among highway agencies with limited PMS budgets. A time-saving and effective data collection approach based on subjective judgment is introduced by the writers for rating predominant distress types found in asphaltic pavements. Inclusion of both severity and extent ratings of distresses is expected to provide a strong basis for eventual maintenance cost computations. The mathematical techniques of fuzzy sets are used to deal with the subjectivity associated with human judgment of distress severity and extent. In addition, the relative importance of each distress type with respect to maintenance is also utilized in the determination of the combined condition index. Several fuzzy aggregation and ranking approaches are explored and the one with the highest computational efficiency is employed for ranking pavement sections with respect to rehabilitation needs. Finally, a fuzzy pavement condition forecasting model is also developed by incorporating subjective probability assessments regarding pavement condition deterioration rates, in the Markov transition process. Specific transition probability matrices for different distress types are used in this approach to overcome the deficiencies of the traditional PCI approach. The potential applicability of the methodology is tested on the major pavement network of Sri Lanka and its effectiveness and the execution ease are demonstrated.