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G. Di Mino, M.R. De Blasiis, F. Di Noto, S. Noto, "An Advanced Pavement Management System based on a Genetic Algorithm for a Motorway Network", in Y. Tsompanakis, (Editor), "Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 26, 2013. doi:10.4203/ccp.103.26

Keywords: pavement management system, genetic algorithm, multi-objective optimization.

Summary

Maintenance and improvement, through the rehabilitation, of the road infrastructure is a strategic and priority objective for road agencies, nevertheless the economic resources required are often inadequate.

Within road management, the pavement management system (PMS) plays an essential role because of both the money needed and the performance that should be provided in terms of safety, ride quality and transport cost.

The PMS is based on searching for a balanced solution between the lowest cost and the increased level of performance (i.e. pavement condition).

In this paper a PMS multi-objective optimization method, was proposed, using a genetic algorithm (GA) to identify the best solution considering different rehabilitation strategies. The multi-objective optimization GA permits a set of optimal solutions (the Pareto solution set) that takes into account all the considered constraints.

Finally on the basis of a specific criteria the best solution was selected in relation to the ranking of the priorities of the agency. A detailed numerical study was conducted on the Italian A18 motorway and the results showed that the proposed model PMS-GA is a suitable support to the decision making process.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 103 PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING Edited by: Y. Tsompanakis Paper 26 An Advanced Pavement Management System based on a Genetic Algorithm for a Motorway Network G. Di Mino¹, M.R. De Blasiis², F. Di Noto³ and S. Noto¹ ¹University of Palermo, Italy ²University of Roma3, Italy ³SPEA Ingegneria Europea, Italy doi:10.4203/ccp.103.26 purchase the full-text of this paper Full Bibliographic Reference for this paper G. Di Mino, M.R. De Blasiis, F. Di Noto, S. Noto, "An Advanced Pavement Management System based on a Genetic Algorithm for a Motorway Network", in Y. Tsompanakis, (Editor), "Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 26, 2013. doi:10.4203/ccp.103.26 Keywords: pavement management system, genetic algorithm, multi-objective optimization. Summary Maintenance and improvement, through the rehabilitation, of the road infrastructure is a strategic and priority objective for road agencies, nevertheless the economic resources required are often inadequate. Within road management, the pavement management system (PMS) plays an essential role because of both the money needed and the performance that should be provided in terms of safety, ride quality and transport cost. The PMS is based on searching for a balanced solution between the lowest cost and the increased level of performance (i.e. pavement condition). In this paper a PMS multi-objective optimization method, was proposed, using a genetic algorithm (GA) to identify the best solution considering different rehabilitation strategies. The multi-objective optimization GA permits a set of optimal solutions (the Pareto solution set) that takes into account all the considered constraints. Finally on the basis of a specific criteria the best solution was selected in relation to the ranking of the priorities of the agency. A detailed numerical study was conducted on the Italian A18 motorway and the results showed that the proposed model PMS-GA is a suitable support to the decision making process. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £42 +P&P)
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