Abstract
Artificial Neural Networks (ANNs) methods are highly regarded in abundant analysis and research in science and engineering. Neural networks (NNs) behave better in complex conditions; in particular NNs are often applied to the development of statistical models for intrinsically non-linear systems. In this study, applications of neural networks in designing of Asphaltic concrete mixture (determination of Marshall Quotient and Resilient modulus at 60 degrees c) were investigated. To determine these properties using neural networks, firstly; samples were collected from different regions in Makkah area during construction and secondly tested at Umm Al-Qura University laboratories for bitumen content, gradation of aggregate, Marshall Stability, Marshall Quotient and Resilient modulus determination. The NNs method is applied to the data set to determine the Marshall Quotient and Resilient modulus at 60oc. The NN results were compared to results those obtained from Marshall design method and Resilient modulus test. Experimental results demonstrate that the NN method is suitable to predict the Marshall Quotient and Resilient modulus for asphaltic concrete mixtures.