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S. Terzi¹, M. Saltan¹ and Ö. Terzi²

¹Civil Engineering Department, Suleyman Demirel University, Isparta, Turkey
²Construction Education Department, Suleyman Demirel University, Isparta, Turkey

doi:10.4203/ccp.109.28

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, "Modeling the Present Serviceability Ratio of Flexible Highway Pavements using a Wavelet-Neuro Approach", 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 28, 2015. doi:10.4203/ccp.109.28

Keywords: present serviceability ratio, pavement performance, pavement serviceability index, wavelet transform, artificial neural networks, performance estimation.

Summary

In this paper, wavelet-neuro (WN) models have been compared with artificial neural networks (ANN) models and the pavement serviceability index (PSI) equation for estimating the present serviceability ratio (PSR). The original experimental data obtained from ASHTO road tests including PSR, slope variance, rut depth, patches, cracking and longitudinal cracking are decomposed into sub-series components by using the discrete wavelet transform (DWT). Then, effective DWs have been used as input parameters in the ANN modeling. When the regression coefficient values of the WN and ANN models are examined, it has been shown that the WN model gave higher regression coefficient values than the ANN model.

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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 28 Modeling the Present Serviceability Ratio of Flexible Highway Pavements using a Wavelet-Neuro Approach S. Terzi¹, M. Saltan¹ and Ö. Terzi² ¹Civil Engineering Department, Suleyman Demirel University, Isparta, Turkey ²Construction Education Department, Suleyman Demirel University, Isparta, Turkey doi:10.4203/ccp.109.28 purchase the full-text of this paper Full Bibliographic Reference for this paper , "Modeling the Present Serviceability Ratio of Flexible Highway Pavements using a Wavelet-Neuro Approach", 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 28, 2015. doi:10.4203/ccp.109.28 Keywords: present serviceability ratio, pavement performance, pavement serviceability index, wavelet transform, artificial neural networks, performance estimation. Summary In this paper, wavelet-neuro (WN) models have been compared with artificial neural networks (ANN) models and the pavement serviceability index (PSI) equation for estimating the present serviceability ratio (PSR). The original experimental data obtained from ASHTO road tests including PSR, slope variance, rut depth, patches, cracking and longitudinal cracking are decomposed into sub-series components by using the discrete wavelet transform (DWT). Then, effective DWs have been used as input parameters in the ANN modeling. When the regression coefficient values of the WN and ANN models are examined, it has been shown that the WN model gave higher regression coefficient values than the ANN model. 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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