چكيده به لاتين
Roads are considered as one of the most extensive civil infrastructure and part of the national wealth of any country and due to the importance of the roads in all aspects of life in society, managing their maintenance and repair is very important. Two issues: the type of selected M & R option and the time of its application in the topic of M & R management are very important and applying an M & R option to a pavement section is only cost-effective within a range of conditions of that section or time. In this thesis, by combining prioritization tools, artificial intelligence, and optimization, a model has been presented to provide an algorithm for selecting the optimal M & R option in pavement sections in each period.
First, to distinguish pavement sections that have the same conditions and characteristics but are in branches with different characteristics and degrees of importance, the analytical hierarchy process (AHP) is used to prioritize the branches of the pavement network. In the next step, using the linear programming model, an attempt was made to maximize the possibility of selecting M & R options in pavement sections by considering specific constraints such as the existing budget, the power of the road trustee organization, which indicates the maximum number of sections that the organization can repair and maintain each year. A fuzzy inference system (FIS) has been used to obtain the possibility of selecting each M & R option in pavement sections so that for each of the options of crack filling, fog seal, slurry seal, chip seal, micro-surfacing, recycling, overlay, and reconstruction a fuzzy rule table was developed based on the research literature. Finally, after coding in MATLAB software, the model was used for a hypothetical pavement network. The mechanism used in this thesis presents an algorithm with a new objective function to select the optimal M & R option in pavement sections at any time interval. Based on the results, it can be said that the algorithm used in this thesis can provide different scenarios for selecting M & R options at one-year intervals to the road trustee organization by considering different parameters and indicators related to pavement branches and sections. This algorithm facilitates the process of selecting M & R options in different sections of the road network and provides a scientific method for managing road maintenance and repair.
