Several studies have implemented various pavement conditionprioritization techniques (which are summarized in Table 1), and inspected the efficiencyof time-cost optimization using genetic algorithms (GAs), fuzzy logicalcontrol, integer programming, and ant colony algorithms 11-14. GAs and Particle-SwarmOptimization (PSO) are excellent at pavement management analyses as well as managingM&R activities 15,16. Ouma et al. 17 explores a multi-attribute strategy todetermine pavement maintenance prioritization.

The study contrasts the use of afuzzy analytical hierarchy process (AHP) with fuzzy Technique for OrderPreference by Ideal Situation (TOPSIS). A case study was conducted in Kenya andit was observed that both fuzzy AHP and fuzzy TOSIS produced very similarmaintenance prioritization ranking. However, fuzzy TOPSIS results were moreaccurate as fuzzy AHP as fuzzy AHP had a tendency to overestimate the ranking. Gao et al. 18 discussed the use of a parametric method forpavement condition improvement and budget utilization simultaneously. Themethod is tested on a real-world case study using data from the DallasDistrict’s Pavement Management Information System.

The results proved that theparametric method can more efficiently solve the bi-objective pavementmaintenance and rehabilitation scheduling problem than other methods like theweighting method. The parametric can produce the complete set of Pareto-optimalsolutions at a more efficient computing time per solution. Babashamsi et al. 19 incorporates fuzzy analytic hierarchyprocess (AHP) with the VIKOR method (a multi-criteria decision-making methodbased on the ideal point technique) to determine the pavement prioritizationmaintenance for various real-world alternatives. Numerous pavement networkindices like the pavement condition index (PCI), traffic congestion, pavementwidth, improvement and maintenance costs, and the time required to operate wereconsidered. Fuzzy AHP was utilized to ascertain the weights of these indiceswhereas the VIKOR model helped prioritize the ranking of the alternatives’. Chang 20 successfully applied particle swarm optimization(PSO) method to prioritize 135pavement sections using eight pavement conditionparameters, which are standard deviation (SD) for smoothness, rutting, deflections, cracking, pothole, bleeding, patching, andshoving.


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