Multi-objective optimization for maintaining low-noise pavement network system in Hong Kong

Low noise, as one of the emerging pavement functions, has received growing interest recently, but was rarely considered in pavement management system. To fill this gap, this study aims to develop a multi-objective optimization (MOO) decision-support system for maintaining the low-noise pavement network system. Three objectives were considered: (1) maximizing the average Close Proximity (CPX) level reduction, (2) minimizing the maintenance costs, and (3) minimizing the greenhouse gas emissions generated from the maintenance. The non-dominated sorting genetic algorithm II (NSGA-II) was employed to search for the optimal intervention strategies. The proposed model was implemented in a case study in Hong Kong to demonstrate its capability. The optimization strategies developed in this study could provide more informative reference for the decision-makers. The best-compromised strategy could be determined by trading off different solution sets subjected to the specific social situations, budget limitations and policy restrictions.