A Neuro-Fuzzy and Neural Network Approach for Rutting Potential Prediction of Asphalt Mixture Based on Creep Test

  • Israa Saeed Jawad Al-Haydari Highway and Transportation Eng. Dep., Mustansiriyah University, Baghdad - IRAQ

Abstract

This study implements the soft computing techniques such as Artificial Neural Network (ANN) and an adaptive Neuro-Fuzzy (ANFIS) approach. Thus to model the rutting prediction with the aid of experimental uniaxial creep test results for asphalt mixtures. Marshall samples, having Maximum Nominal Size of 12.5 mm, have been selected from previous studies. These samples have been prepared and tested under different conditions. They were also subjected to different loading stress (0.034, 0.069, 0.103) MPa, and tested at various temperature (10, 20, 40, and 55) °C. The modeling analysis revealed that both approaches are powerful tools for modeling creep behavior of pavement mixture in terms of Root Mean Square Error and Correlation Coefficient. The best results are obtained with the ANFIS model.

Published
2018-05-02
How to Cite
Al-Haydari, I. (2018). A Neuro-Fuzzy and Neural Network Approach for Rutting Potential Prediction of Asphalt Mixture Based on Creep Test. Al-Nahrain Journal for Engineering Sciences, 21(2), 275-284. https://doi.org/10.29194/NJES21020275