A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Integrated airport pavement management using a hybrid approach of Markov Chain and supervised multi-objective genetic algorithms
An airport pavement management system is of great significance in terms of allocating budget and resources as well as preserving the level of serviceability and safety. In order to provide a more accurate, reliable, and applicable plan for airport pavement management, the combination and interrelation of four crucial affecting factors should be considered: pavement age, traffic load frequency, weather conditions, as well as maintenance actions effects. These factors have not been comprehensively studied enough neither within the deterioration modelling, which is the core of airport pavement management, nor through the decision-making procedure. The Markov Chain method is used for deterioration modelling. The data required to build up Markov Chain deterioration models are collected through questionnaires filled by experts. Having built up deterioration models, the second version of Non-Dominated Sorting Genetic Algorithms (NSGA-II) is applied to solve pavement management problems, i.e. providing a pavement maintenance activity plan over the planning horizon through a supervised manner considering both pavement conditions and monetary resources as objective functions. Finally, the proposed plan maintains a minimum acceptable level of airport pavement conditions over the planning horizon minimizing budget.
Integrated airport pavement management using a hybrid approach of Markov Chain and supervised multi-objective genetic algorithms
An airport pavement management system is of great significance in terms of allocating budget and resources as well as preserving the level of serviceability and safety. In order to provide a more accurate, reliable, and applicable plan for airport pavement management, the combination and interrelation of four crucial affecting factors should be considered: pavement age, traffic load frequency, weather conditions, as well as maintenance actions effects. These factors have not been comprehensively studied enough neither within the deterioration modelling, which is the core of airport pavement management, nor through the decision-making procedure. The Markov Chain method is used for deterioration modelling. The data required to build up Markov Chain deterioration models are collected through questionnaires filled by experts. Having built up deterioration models, the second version of Non-Dominated Sorting Genetic Algorithms (NSGA-II) is applied to solve pavement management problems, i.e. providing a pavement maintenance activity plan over the planning horizon through a supervised manner considering both pavement conditions and monetary resources as objective functions. Finally, the proposed plan maintains a minimum acceptable level of airport pavement conditions over the planning horizon minimizing budget.
Integrated airport pavement management using a hybrid approach of Markov Chain and supervised multi-objective genetic algorithms
Ansarilari, Zahra (author) / Golroo, Amir (author)
International Journal of Pavement Engineering ; 21 ; 1864-1873
2020-12-05
10 pages
Article (Journal)
Electronic Resource
Unknown
Optimum analysis of pavement maintenance using multi-objective genetic algorithms
| DOAJ | 2015
Optimum analysis of pavement maintenance using multi-objective genetic algorithms
| Taylor & Francis Verlag | 2015
| Wiley | 2015
Arizona Airport Pavement Management
| ASCE | 2013
Pavement Performance Modelling Using Markov Chain
| Springer Verlag | 2012