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A.P. Chassiakos, M.I. Sotiropoulou, N. Moschonas, P. Iliakopoulos, "A Genetic Algorithm for Optimising Sustainability Performance of Road Pavements", in Y. Tsompanakis, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Fourth International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 29, 2015. doi:10.4203/ccp.109.29

Keywords: pavement, road infrastructure, environmental impact, social impact, sustainability, optimisation, genetic algorithm..

Summary

Road sustainability performance goals require that all adverse impacts on pavement deterioration are kept within tolerable limits. In this paper, an optimisation model is developed that aims to provide decision support to engineers in developing appropriate pavement maintenance strategies to ensure acceptable levels of safety and functionalism of the highway network in time as well as reduction of environmental impact. This model supersedes previous ones in considering the generalised cost, which includes the agency cost, the user cost, and the environmental impacts, as the main decision parameter. As a result of the problem size and complexity, the optimisation employs a genetic algorithm which can handle a large number of road sections, search a wide range of possible solutions, and reach a solution within reasonable computation time. The model has been evaluated with several test cases and Pareto curves have been developed. The results indicate that the proposed model can effectively assist pavement preservation and management decisions in highway networks.

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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: 109 PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING Edited by: Y. Tsompanakis, J. Kruis and B.H.V. Topping Paper 29 A Genetic Algorithm for Optimising Sustainability Performance of Road Pavements A.P. Chassiakos, M.I. Sotiropoulou, N. Moschonas and P. Iliakopoulos Department of Civil Engineering, University of Patras, Greece doi:10.4203/ccp.109.29 purchase the full-text of this paper Full Bibliographic Reference for this paper A.P. Chassiakos, M.I. Sotiropoulou, N. Moschonas, P. Iliakopoulos, "A Genetic Algorithm for Optimising Sustainability Performance of Road Pavements", in Y. Tsompanakis, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Fourth International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 29, 2015. doi:10.4203/ccp.109.29 Keywords: pavement, road infrastructure, environmental impact, social impact, sustainability, optimisation, genetic algorithm.. Summary Road sustainability performance goals require that all adverse impacts on pavement deterioration are kept within tolerable limits. In this paper, an optimisation model is developed that aims to provide decision support to engineers in developing appropriate pavement maintenance strategies to ensure acceptable levels of safety and functionalism of the highway network in time as well as reduction of environmental impact. This model supersedes previous ones in considering the generalised cost, which includes the agency cost, the user cost, and the environmental impacts, as the main decision parameter. As a result of the problem size and complexity, the optimisation employs a genetic algorithm which can handle a large number of road sections, search a wide range of possible solutions, and reach a solution within reasonable computation time. The model has been evaluated with several test cases and Pareto curves have been developed. The results indicate that the proposed model can effectively assist pavement preservation and management decisions in highway networks. 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 £50 +P&P)
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