Optimum analysis of pavement maintenance using multi-objective genetic algorithms
Road network expansion in Egypt is considered as a vital issue for the development of the country. This is done while upgrading current road networks to increase the safety and efficiency. A pavement management system (PMS) is a set of tools or methods that assist decision makers in finding optimum...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2015-04-01
|
| Series: | HBRC Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1687404814000182 |
| _version_ | 1818877475968516096 |
|---|---|
| author | Amr A. Elhadidy Emad E. Elbeltagi Mohammad A. Ammar |
| author_facet | Amr A. Elhadidy Emad E. Elbeltagi Mohammad A. Ammar |
| author_sort | Amr A. Elhadidy |
| collection | DOAJ |
| description | Road network expansion in Egypt is considered as a vital issue for the development of the country. This is done while upgrading current road networks to increase the safety and efficiency. A pavement management system (PMS) is a set of tools or methods that assist decision makers in finding optimum strategies for providing and maintaining pavements in a serviceable condition over a given period of time. A multi-objective optimization problem for pavement maintenance and rehabilitation strategies on network level is discussed in this paper. A two-objective optimization model considers minimum action costs and maximum condition for used road network. In the proposed approach, Markov-chain models are used for predicting the performance of road pavement and to calculate the expected decline at different periods of time. A genetic-algorithm-based procedure is developed for solving the multi-objective optimization problem. The model searched for the optimum maintenance actions at adequate time to be implemented on an appropriate pavement. Based on the computing results, the Pareto optimal solutions of the two-objective optimization functions are obtained. From the optimal solutions represented by cost and condition, a decision maker can easily obtain the information of the maintenance and rehabilitation planning with minimum action costs and maximum condition. The developed model has been implemented on a network of roads and showed its ability to derive the optimal solution. |
| first_indexed | 2024-12-19T13:58:53Z |
| format | Article |
| id | doaj.art-22923b13cb74429dad0c9496cb0ff4a9 |
| institution | Directory Open Access Journal |
| issn | 1687-4048 |
| language | English |
| last_indexed | 2024-12-19T13:58:53Z |
| publishDate | 2015-04-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | HBRC Journal |
| spelling | doaj.art-22923b13cb74429dad0c9496cb0ff4a92022-12-21T20:18:31ZengTaylor & Francis GroupHBRC Journal1687-40482015-04-0111110711310.1016/j.hbrcj.2014.02.008Optimum analysis of pavement maintenance using multi-objective genetic algorithmsAmr A. Elhadidy0Emad E. Elbeltagi1Mohammad A. Ammar2Higher Misr Institute for Engineering and Technology, Mansoura, EgyptFaculty of Engineering, Mansoura University, 35516, EgyptFaculty of Engineering, Tanta University, EgyptRoad network expansion in Egypt is considered as a vital issue for the development of the country. This is done while upgrading current road networks to increase the safety and efficiency. A pavement management system (PMS) is a set of tools or methods that assist decision makers in finding optimum strategies for providing and maintaining pavements in a serviceable condition over a given period of time. A multi-objective optimization problem for pavement maintenance and rehabilitation strategies on network level is discussed in this paper. A two-objective optimization model considers minimum action costs and maximum condition for used road network. In the proposed approach, Markov-chain models are used for predicting the performance of road pavement and to calculate the expected decline at different periods of time. A genetic-algorithm-based procedure is developed for solving the multi-objective optimization problem. The model searched for the optimum maintenance actions at adequate time to be implemented on an appropriate pavement. Based on the computing results, the Pareto optimal solutions of the two-objective optimization functions are obtained. From the optimal solutions represented by cost and condition, a decision maker can easily obtain the information of the maintenance and rehabilitation planning with minimum action costs and maximum condition. The developed model has been implemented on a network of roads and showed its ability to derive the optimal solution.http://www.sciencedirect.com/science/article/pii/S1687404814000182Pavement maintenanceMulti-objective optimizationMarkov-chainGenetic algorithms |
| spellingShingle | Amr A. Elhadidy Emad E. Elbeltagi Mohammad A. Ammar Optimum analysis of pavement maintenance using multi-objective genetic algorithms HBRC Journal Pavement maintenance Multi-objective optimization Markov-chain Genetic algorithms |
| title | Optimum analysis of pavement maintenance using multi-objective genetic algorithms |
| title_full | Optimum analysis of pavement maintenance using multi-objective genetic algorithms |
| title_fullStr | Optimum analysis of pavement maintenance using multi-objective genetic algorithms |
| title_full_unstemmed | Optimum analysis of pavement maintenance using multi-objective genetic algorithms |
| title_short | Optimum analysis of pavement maintenance using multi-objective genetic algorithms |
| title_sort | optimum analysis of pavement maintenance using multi objective genetic algorithms |
| topic | Pavement maintenance Multi-objective optimization Markov-chain Genetic algorithms |
| url | http://www.sciencedirect.com/science/article/pii/S1687404814000182 |
| work_keys_str_mv | AT amraelhadidy optimumanalysisofpavementmaintenanceusingmultiobjectivegeneticalgorithms AT emadeelbeltagi optimumanalysisofpavementmaintenanceusingmultiobjectivegeneticalgorithms AT mohammadaammar optimumanalysisofpavementmaintenanceusingmultiobjectivegeneticalgorithms |