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

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.