Pavement rehabilitation and maintenance prioritization of urban roads using fuzzy logic

Moazami, Danial; Behbahani, Hamid; Muniandy, Ratnasamy · 2011 · Crossref

DOI: 10.1016/j.eswa.2011.04.079

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Summary

This paper addresses the critical challenge of prioritizing pavement rehabilitation and maintenance projects for urban roads under constrained budgetary conditions. The authors identify that selecting which projects to fund requires a comprehensive model that accounts for multiple influencing factors, including pavement condition index, traffic volume, road width, and the associated costs of rehabilitation and maintenance. The primary motivation for this research is the difficulty in defining a traditional mathematical model that effectively integrates all these diverse variables. Consequently, the study employs fuzzy logic as an advanced modeling technique to facilitate more precise decision-making outcomes compared to alternative methods like the Analytical Hierarchy Process. The methodology involves the development and testing of a fuzzy logic model designed to prioritize road maintenance projects. The researchers utilized MATLAB software and coded M-files to implement the model. A key component of the experimental design was the evaluation of different inference engines to determine which provided the most logical and effective separation for this specific application. The study tested three distinct inference engines: the Product engine, the Dienes–Rescher engine, and the Lukasiewicz engine. To validate the model, the authors conducted a case study focusing on streets located in District No. 6 of the Tehran municipality. This real-world application involved executing the preferred mathematical model on 131 specific road sections to assess its practical utility in prioritization. The main finding of the research is the identification of the Product inference engine as the superior choice for this application. After testing the three engines, the authors determined that the Product engine provided the most logical separation for prioritizing pavement maintenance tasks. The model was successfully applied to the 131 sections in the Tehran case study, demonstrating its capability to handle the complex interplay of factors such as pavement condition, traffic volume, road width, and maintenance costs. The study confirms that fuzzy logic offers a more precise approach to decision-making in this context than traditional methods, allowing for better handling of the uncertainties and multiple variables inherent in urban road maintenance planning. The significance of this work lies in its contribution to the field of civil engineering and urban infrastructure management. By establishing a robust fuzzy logic model with the Product inference engine, the paper provides a practical tool for municipalities and transportation agencies to optimize limited budgets. The ability to accurately prioritize maintenance projects based on a holistic set of criteria ensures that resources are allocated to the most critical road sections, thereby improving overall pavement performance and serviceability. This approach enhances the efficiency of maintenance planning processes, offering a structured and data-driven method for addressing the inevitable trade-offs required in urban road management.

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