A Variable Selection for Asphalt Pavement Performance Based on RBF Neural Network

Peng Tian; Gao Feng Zhan; Lei Nai

doi:10.4028/www.scientific.net/AMM.744-746.1222

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 744-746A Variable Selection for Asphalt Pavement...

A Variable Selection for Asphalt Pavement Performance Based on RBF Neural Network

Abstract:

By combining RBF neural network with MIV algorithm, the main influencing factors of asphalt mixture pavement performance will be selected. First, the MIV values will be calculated by MIV method. Selection of variables is based on the size of MIV. There are 8 variables selected form 12 variables. Then, a new RBF neural network will be found by the data which have great impact to the output result. The comparison between the two RBF simulate results will prove that the method of MIV is feasible in variable selection. By the MIV method, the simulate results of RBF will be calculated faster and more accurately.

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Periodical:

Applied Mechanics and Materials (Volumes 744-746)

Pages:

1222-1225

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.744-746.1222

Citation:

Cite this paper

Online since:

March 2015

Authors:

Peng Tian*, Gao Feng Zhan, Lei Nai

Keywords:

Asphalt Mixture, Influencing Factor, MIV, Pavement Performance, RBF

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* - Corresponding Author

References

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