American J. of Engineering and Applied Sciences 1 (4): 389-392, 2008 ISSN 1941-7020 © 2008 Science Publications Corresponding Author: Ghotbi, A., Faculty of Civil Engineering, Shahid Bahonar University, 22 Bahman Blvd, Kerman, Iran Tel/Fax: +98(21)-33939450 389 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 Department of Civil Engineering, Shahid Bahonar University, 22 Bahman Blvd, Kerman, Kerman, Iran Abstract: 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. Key words: Artificial neural network, compressive strength, network teaching, unconfined compressive strength (UCS) INTRODUCTION The knowledge of artificial network has had a considerable progress during the recent 15 years. The basic researches were begun in the mid 19 century by Ivan Pavlov and consequently were peruse by the scientists such as, Warren Mc Culloch and Walter Pitts in 1943, Donald Hebb in 1949, Frank Rosenblatt in 1958 and Bernurd Widrow, Marvin Minsky, Seymour Papert in the mid decade of 60B.C and finally by John Hopfield, David Rummelhart and James Mcland by 1982 to 1985 up to now [1,2] . Using the interpretation of the empirical data, artificial neural network conducts the knowledge or the rule hidden behind the data to network structure and despite the mathematical models, there is no need to determine a mathematical relation between the input and the output values. So that in cases which it is too difficult to imply a complicated relation between the variables especially in the physical terms, artificial neural network is very capable. On the other hand, in the mathematical functions, the incorrect input values or the incomplete ones cause a very significant error in the output values while neural network presents a better output results close to the exact results [3] . Considering that the laboratory tests are so expensive especially in the field of transportation projects and due to the shortage of the required models for estimating the strength properties of the materials stabilized by the combination of bitumen emulsion and cement, it is so necessary to do further researches to achieve the mentioned goals. In order to access this propose, using 97 tests done on the constructed samples with different percentages of cement and bitumen emulsion, the compressive strength is stimulated and is modeled by artificial neural network. The presented model can be effective for estimating the compressive strength and can decrease the costs of the laboratory tests. MATERIALS AND METHODS Artificial neural network: The main body of each artificial neural network includes some joins and the