Ranking and prioritizing pavement infrastructure for maintenance and rehabilitation have become major undertakings for several departments of transportation around the globe. This is a complex decision-making problem because multiple and conflicting criteria can contribute to the assessment. Multi-criteria decision analysis (MCDA) techniques evaluate the trade-off between several quantitative and qualitative criteria and facilitate complex decision-making. This research introduces a framework based on MCDA to support pavement management decision making, while quantifying emerging sustainability-related factors such as safety, noise, and pollution in the decision-making process. The framework features include 1) identifying pavement management main decision elements: objectives, criteria, and attributes by detailing the problem with a five-level hierarchy structure; 2) employing combined analytic hierarchy process and multi-attribute utility theory to develop representative set of utility functions; and 3) ranking and prioritizing large networks of pavement sections while incorporating sustainability-related criteria. Data used to assess the decision criteria and develop the utility functions is extracted by means of a questionnaire survey completed by professionals working in the field of pavement management. The proposed method is applied to a case study consisting of ten pavement sections extracted from the long-term pavement performance database, wherein the sections are ranked based on their attributes. Sensitivity analysis is performed to evaluate the impact of the different criteria on the ranking process. The proposed method has shown potential in ranking pavement networks based on the identified criteria. Future work can test the performance of the proposed methodology with a full-scale pavement network and apply it to other civil infrastructure assets to evaluate its performance with different types of projects.
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
- Environmental Science(all)
- Strategy and Management
- Industrial and Manufacturing Engineering