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M.A. Saif, M.S. El-Bisy, M.H. Alawi, "Application of Soft Computing Techniques to Predict the Stability of Asphaltic Concrete Mixes ", 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 33, 2013. doi:10.4203/ccp.103.33

Keywords: asphaltic concrete mixes, support vector machines, structural risk minimization principle, back-propagation neural network, Marshall stability, Marshall flow.

Summary

In this paper, support vector machines (SVMs) and classical back propagation neural networks (BPNNs) were used to predict the stability of asphaltic concrete mixes. Samples of asphaltic concrete mixes were collected from different regions in the city of Makkah in Saudi Arabia during the construction of new roads. The samples were tested to determine bitumen content and gradation of aggregates. Marshall stability and Marshall flow were also determined. The results of these tests were used for the training of SVMs and BPNNs and for the prediction of the stability of the asphaltic concrete mixes. Comparisons between actual and predicted values for Marshall stability of asphaltic concrete mixes for trained and tested data by using BPNNs and SVMs were carried out. They demonstrate that the SVM is superior to the BPNN in predicting the stability of asphaltic concrete mixes. This paper shows that transportation and highway engineers can use the SVM model to predict the stability of asphaltic concrete mixes without conducting costly and time consuming experimental tests.

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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 33 Application of Soft Computing Techniques to Predict the Stability of Asphaltic Concrete Mixes M.A. Saif, M.S. El-Bisy and M.H. Alawi Department of Civil Engineering, College of Engineering and Islamic Architecture Umm Al-Qura University, Makkah, Saudi Arabia doi:10.4203/ccp.103.33 purchase the full-text of this paper Full Bibliographic Reference for this paper M.A. Saif, M.S. El-Bisy, M.H. Alawi, "Application of Soft Computing Techniques to Predict the Stability of Asphaltic Concrete Mixes ", 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 33, 2013. doi:10.4203/ccp.103.33 Keywords: asphaltic concrete mixes, support vector machines, structural risk minimization principle, back-propagation neural network, Marshall stability, Marshall flow. Summary In this paper, support vector machines (SVMs) and classical back propagation neural networks (BPNNs) were used to predict the stability of asphaltic concrete mixes. Samples of asphaltic concrete mixes were collected from different regions in the city of Makkah in Saudi Arabia during the construction of new roads. The samples were tested to determine bitumen content and gradation of aggregates. Marshall stability and Marshall flow were also determined. The results of these tests were used for the training of SVMs and BPNNs and for the prediction of the stability of the asphaltic concrete mixes. Comparisons between actual and predicted values for Marshall stability of asphaltic concrete mixes for trained and tested data by using BPNNs and SVMs were carried out. They demonstrate that the SVM is superior to the BPNN in predicting the stability of asphaltic concrete mixes. This paper shows that transportation and highway engineers can use the SVM model to predict the stability of asphaltic concrete mixes without conducting costly and time consuming experimental tests. 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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