Abstract
For the optimization analysis of pavement maintenance programs, combinatorial optimization is a pervasive problem. Genetic algorithms (GAs) are widely used to solve combinatorial optimization problems in pavement maintenance programs. However, owing to the stochastic search mechanisms underlying GAs, they are more likely to produce a relatively unsatisfactory solution due to premature convergence. Hence, a binary cuckoo search (BCS) algorithm was implemented to solve the optimization problem. To the best of our knowledge, this is the first time that a BCS algorithm has been applied to pavement maintenance management system. Three hypothetical cases are used to investigate and demonstrate the effectiveness of the BCS algorithm, in which uncertainty‐based performance degradation is considered. The results of a comparison between GA and BCS clearly justify the advantages of the search paths underlying the BCS in alleviating premature convergence. Therefore, the BCS algorithm can help decision makers to make more appropriate trade‐off decisions for pavement maintenance programs.
Highlights
Over the past 20 years, China’s highway construction industry has developed rapidly and has entered the maintenance and management mode. e Department of Transportation in China’s Shanxi Province (SXDOT) maintains a total of 5258 km freeway networks and established a pavement management system (PMS) in collaboration with Southeast University in 2018, so as to achieve the goal of reasonable use of pavement maintenance funds
To the best of our knowledge, cuckoo search (CS) theory has not been applied in the field of pavement maintenance management. erefore, the current study verifies two objectives: (i) if the binary cuckoo search (BCS) algorithm can be used to solve the optimization model in PMS and (ii) if the impact of BCS algorithm is better than Genetic algorithms (GAs) that is the most widely used method in asset management [1]
The BCS algorithm and GA were performed to solve the combinatorial optimization problems in a multiyear pavement maintenance program using three typical hypothetical cases. e pavement performance degradation model used in these cases has taken uncertainty into account, and this makes the examples more representative. e results in all these cases show that BCS is suitable for combinatorial optimization models in pavement maintenance management and better than GA in alleviating premature convergence. e advantage of BCS is important for large-scale optimization problems in pavement maintenance management
Summary
SXDOT and Southeast University collaborated to develop a report on the determination of the threeyear pavement maintenance funds for 2019–2021, in which a genetic algorithm (GA) was used to solve an optimization model, which is essentially a combinatorial optimization problem to gain an optimal fund allocation program. In order to improve the premature problem of GA in PMS, a binary cuckoo search (BCS) algorithm was established to solve combinatorial optimization in pavement maintenance management, according to the theory of cuckoo search (CS). Erefore, the current study verifies two objectives: (i) if the BCS algorithm can be used to solve the optimization model in PMS and (ii) if the impact of BCS algorithm is better than GA that is the most widely used method in asset management [1]. As mentioned above, it has not been used in the field of pavement maintenance management
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
