Advanced ANN Prediction of Moisture Damage in CNT Modified Asphalt Binder

Document Type : Regular Article

Author

Assistant Professor, Department of Civil Engineering, University of Bahrain, Bahrain

Abstract

Moisture penetration causes many direct and indirect distresses in flexible asphalt pavement. Due to damage in asphalt concrete and binder by moisture are the prime concern of failure for flexible pavement worldwide. The causes and prediction are investigated in this study. The asphalt binder was modified with carbon nanotubes (CNT) with very small percentages. The modified binder was simulated with moisture damage with AASHTO T-283 methods. In this study, polymer and carbon nanotubes (CNT) have been added to liquid asphalt binder to examine whether the resulting modified binder has improved moisture damage resistance. Using laboratory tested data, an artificial intelligence modeling technique has been utilized to determine the moisture damage behavior of the modified binder. Multi-Layer Perceptron (MLP) provides the best prediction for wet and dry samples AFM readings with R2 values respectively 0.6407 and 0.8371.

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References

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  • Receive Date: 22 April 2017
  • Revise Date: 25 May 2017
  • Accept Date: 22 June 2017

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