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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 105
PROCEEDINGS OF THE NINTH INTERNATIONAL CONFERENCE ON ENGINEERING COMPUTATIONAL TECHNOLOGY
Edited by:
Paper 88

Prediction of Asphaltic Concrete Stability by using Support Vector Machines

M.A. Saif and M.S. Al-Bisy

Civil Engineering Department, Umm Alqura University, Makkah, Saudi Arabia

Full Bibliographic Reference for this paper
M.A. Saif, M.S. Al-Bisy, "Prediction of Asphaltic Concrete Stability by using Support Vector Machines", in , (Editors), "Proceedings of the Ninth International Conference on Engineering Computational Technology", Civil-Comp Press, Stirlingshire, UK, Paper 88, 2014. doi:10.4203/ccp.105.88
Keywords: prediction, genetic algorithm, support vector machines, asphaltic concrete, Marshall stability.

Summary
The objective of the study, presented in this paper, is to explore the applicability of a support vector machine approach with different kernel functions for predicting the stability of asphaltic concrete mixes. The results of this approach are compared with back propagation and cascade correlation neural network models. All methods used the following classes of input data, which can be easily measured in the laboratory: the percentage of course aggregate, which is basalt type (igneous rock), percentage of fine aggregate, (natural sand), percentage of filler, (ordinary Portland cement type 1), and the percentage of optimum bitumen content. A genetic algorithm is used to determine optimal values of the free support vector machine parameters for different kernel functions. Among the models, the excellent performance of the support vector machine with a radial-basis-kernel-based model demonstrated the potential to function as a useful tool for the estimation of the stability of asphaltic concrete mixes to assess the maximum obtainable prediction accuracy. In conclusion, the support vector machine (radial basis function kernel) model has the highest accuracy and better generalization performance than the cascade correlation neural network and back propagation neural network models. The results obtained in this investigation demonstrate that the support vector machine (radial basis function kernel) model is a promising alternative to neural networks for the stability of asphaltic concrete mixes forecasting.

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