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P. Georgiou, C. Plati, A. Loizos, "Pavement Roughness Progression Modeling using Artificial Neural Networks and Multivariate Adaptive Regression Splines", 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 32, 2015. doi:10.4203/ccp.109.32

Keywords: pavement, roughness, international roughness index, modeling, artificial neural networks, multivariate adaptive regression splines.

Summary

Pavement performance models are imperative for a complete pavement management system (PMS), providing the possibility of analysing, designing, planning, projecting, ranking and optimising the choice of alternatives, allocating costs and apportioning funds. Roughness can serve as an important indicator in determining the road surface's serviceability and thus the assessment of roughness progression over time is considered of outmost importance. In this paper, two models, based on artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS) techniques are developed to predict roughness in terms of international roughness index (IRI), analyzing a series of field data collected from a Greek motorway. The results indicate that both ANN- and MARS-based models seem to be capable of predicting pavement surface roughness; hence they can be useful in support of decision making concerning maintenance priorities and strategies. The recommendation for which of the two techniques should be selected for the purpose of the analysis is mainly dependent on the analyser's skills, since the MARS one seems to require less expertise.

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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 32 Pavement Roughness Progression Modeling using Artificial Neural Networks and Multivariate Adaptive Regression Splines P. Georgiou, C. Plati and A. Loizos Laboratory of Pavement Engineering, National Technical University of Athens, Greece doi:10.4203/ccp.109.32 purchase the full-text of this paper Full Bibliographic Reference for this paper P. Georgiou, C. Plati, A. Loizos, "Pavement Roughness Progression Modeling using Artificial Neural Networks and Multivariate Adaptive Regression Splines", 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 32, 2015. doi:10.4203/ccp.109.32 Keywords: pavement, roughness, international roughness index, modeling, artificial neural networks, multivariate adaptive regression splines. Summary Pavement performance models are imperative for a complete pavement management system (PMS), providing the possibility of analysing, designing, planning, projecting, ranking and optimising the choice of alternatives, allocating costs and apportioning funds. Roughness can serve as an important indicator in determining the road surface's serviceability and thus the assessment of roughness progression over time is considered of outmost importance. In this paper, two models, based on artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS) techniques are developed to predict roughness in terms of international roughness index (IRI), analyzing a series of field data collected from a Greek motorway. The results indicate that both ANN- and MARS-based models seem to be capable of predicting pavement surface roughness; hence they can be useful in support of decision making concerning maintenance priorities and strategies. The recommendation for which of the two techniques should be selected for the purpose of the analysis is mainly dependent on the analyser's skills, since the MARS one seems to require less expertise. 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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