Pavement maintenance scheduling using genetic algorithms

Yang, Chao; Remenyte-Prescott, Rasa; Andrews, John D.

Abstract

This paper presents a new pavement management system (PMS) to achieve the optimal pavement maintenance and rehabilitation (M&R) strategy for a highway network using genetic algorithms (GAs). Optimal M&R strategy is a set of pavement activities that both minimise the maintenance cost of a highway network and maximise the pavement condition of the road sections on the network during a certain planning period. NSGA-II, a multi-objective GA, is employed to perform pavement maintenance optimisation because of its robust search capabilities and constraint handling method that deal with the multi-objective and multi-constrained optimisation problems. In the proposed approach, both deterministic and probabilistic pavement age gain models are utilised for evaluating the evolution of pavement condition over time because of their simplicity of application. The proposed PMS is applied to a case study network that consists of different kinds of road sections. The results obtained indicate that the model is a valuable toolbox for pavement engineers.

Citation

Yang, C., Remenyte-Prescott, R., & Andrews, J. D. (2015). Pavement maintenance scheduling using genetic algorithms. International Journal of Performability Engineering, 11(2),

• Journal Article Type: Article
• Acceptance Date: Sep 26, 2014
• Publication Date: Mar 1, 2015
• Deposit Date: Jul 22, 2016
• Publicly Available Date: Jul 22, 2016
• Journal: International Journal of Performability Engineering
• Electronic ISSN: 0973-1318
• Peer Reviewed: Peer Reviewed
• Volume: 11
• Issue: 2
• Keywords: Pavement Management System, Maintenance and Rehabilitation Strategy, Genetic Algorithms, NSGA-II, Pavement Age Gain Model
• Public URL: https://nottingham-repository.worktribe.com/output/744142
• Publisher URL: http://www.ijpe-online.com/march-2015-p3-pavement-maintenance-scheduling-using-genetic-algorithms.html#axzz4F32mnhrC
• Additional Information: When referring to this paper, credit and acknowledgement must be given to the permission granted by RAMS Consultants.
• Contract Date: Jul 22, 2016