Application of Neural Networks and Adaptive-Network-Based Fuzzy System in the Prediction of Optimum Bitumen Content for Asphaltic Concrete Mixtures

  • Moussa. S. Elbisy Department of Civil Engineering, Umm Al-Qura University, Makkah, Saudi Arabia
  • M. H. Alawi Department of Civil Engineering, Umm Al-Qura University, Makkah, Saudi Arabia
  • M. A. Saif Department of Civil Engineering, Umm Al-Qura University, Makkah, Saudi Arabia
Keywords: Bitumen content, artificial neural networks, Adaptive-Network-Based fuzzy System, prediction

Abstract

The objective of this study is to explore the applicability of artificial neural networks (ANNs) and Adaptive-Network-Based fuzzy System (ANFIS) for predicting the bitumen content (OBC) of asphaltic concrete mixtures based on the experimental data. Samples were collected from different regions in Makkah region in Saudi Arabia during construction and tested at laboratories of Umm Al-Qura University for bitumen content, gradation of aggregate determination. Asphaltic concrete mixtures data were used to test the performance of the ANNs and ANFIS models. Among the two ANN models (a feed-forward back propagation (BP) and a radial basis function (RBF)) employed for this investigation, the BP neural network was found to be superior to RBF network for prediction of the OBC of asphaltic concrete mixtures. For improving model prediction efficiency, optimization of network structure and spread are important for BP and RBF types of the network, respectively. A BPNN model having a structure 3-8-4-1 (three neurons in input and eight neurons in first hidden layers, four neurons in second hidden layer and one neuron in output layer) produced better prediction performance efficiencies with an accuracy of 96.37%. The BPNN (3-8-4-1) model was fairly close to the corresponding actual values of OBC with the average error of 1.1854% and 1.01% for trained and tested data respectively. The results of the testing of ANFIS were indicated almost same performance of the BPNN (3-8-4-1) model.

References

. J.F. Dias, L. Picado-Santos, and S. Capitão. "Mechanical performance of dry process fine crumb rubber asphalt mixtures placed on the Portuguese road network." Construction and Building Materials, vol. 73, pp. 247-254, 2014.

. A. Pasandín, and I. Pérez. "Overview of bituminous mixtures made with recycled concrete aggregates." Construction and Building Materials, vol.74, pp. 151-161, 2015

. L.Wang, , et al., "Advances in Pavement materials, design, characterisation, and simulation." Road Materials and Pavement Design, vol. 18(3): pp. 1-11, 2017.

. N. Baldo, E. Manthos, and M. Pasetto, "Analysis of the Mechanical Behaviour of Asphalt Concretes Using Artificial Neural Networks." Advances in Civil Engineering, 2018

. M. Alawi, and M. Rajab. "Determination of optimum bitumen content and Marshall stability using neural networks for asphaltic concrete mixtures." in Proceedings of the 9th WSEAS International Conference on Computers, World Scientific and Engineering Academy and Society (WSEAS), 2005.

. J.-S. Jang, "ANFIS: adaptive-network-based fuzzy inference system." IEEE transactions on systems, man, and cybernetics, vol. 23(3), pp. 665-685, 1993

. M.G. Schaap, and F.J. Leij, "Using neural networks to predict soil water retention and soil hydraulic conductivity." Soil and Tillage Research, vol. 47(1-2), pp. 37-42, 1998.

. S. Haykin, "Neural Networks, Comprehensive Foundation," Piscataway. NJ: IEEE Press, 1999.

. H.R. Maier, and G.C. Dandy, "The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study." Environmental Modelling & Software, vol. 13(2), pp. 193-209, 1998

. W. Dias, and S. Pooliyadda, "Neural networks for predicting properties of concretes with admixtures." Construction and Building Materials, vol. 15(7): pp. 371-379, 2001.

. J. Haddadnia, K. Faez, and M. Ahmadi, "A fuzzy hybrid learning algorithm for radial basis function neural network with application in human face recognition." Pattern Recognition, vol. 36(5): pp. 1187-1202, 2003.

. M.T. Hagan, H.B. Demuth, and M.H. Beale, "Neural network design," PWS Pub. Co., Boston, 1996.

. I. Flood, and N. Kartam, "Neural networks in civil engineering. I: Principles and understanding." Journal of computing in civil engineering, vol. 8(2): pp. 131-148, 1994.

Published
2020-01-27
Section
Articles