Intelligent Estimation of Compressive Strength of the Pavement Layers Stabilized by the Combination of Bitumen Emulsion and Cement
Mehrdad Aryafar, Abdoul R. Ghotbi, Mehdi Aryafar and Amin Avaei
DOI : 10.3844/ajeassp.2008.389.392
American Journal of Engineering and Applied Sciences
Volume 1, Issue 4
The Application of the different types of additive materials such as lime, cement bitumen and the combination of them are considered as a main issue by the relating experts. In order to promote the bearing capacity of road, these materials, individually, or with the attendance of other materials add to sub base layers. During the recent years, road builders have been considering the application of the combination of bitumen emulsion and cement due to the emergence of the modern equipments and machineries in transportation engineering which have been led to the rapid construction of roads and a uniform combination with the suitable compactness properties in soil stabilization too. The compressive strength which can be determined by the Unconfined Compressive Strength (UCS) test is one of the most important factors to control the quality of the stabilized materials using bitumen emulsion and cement and also in order to design them much efficiently. Besides, it is necessary to use an analytical method because the laboratory tests are very expensive and in some cases are not available especially in the projects constructing in the remote areas and also the strong need for controlling the obtained results from the insitu tests. In this study, the application of the inelegant neural network is investigated to estimate the 28 days compressive strength of the samples built from the stabilized materials by the combination of bitumen emulsion and cement. The obtained results show that; artificial neural network is very capable in predicting the 28 days compressive strength.
© 2008 Mehrdad Aryafar, Abdoul R. Ghotbi, Mehdi Aryafar and Amin Avaei. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.