Prediction of Compaction Efforts of Fine-Grained Soils of Sarabi Dam Using Atrificial Neural Networks

One of the most important issues in the Reverse Analysis is analyzing the density resulting from the compaction of in fine soils. The conventional methods in d etermination of soil density are: sand cone, rubber balloon and nuclear density gauge. Trained neural network, as a suitable alternative for conventional methods based on models analyzed by those methods, is not only as accurate but it is also easier to calculate and implement. In the present article, a model based on multilayer perceptron of neural network is presented for prediction of the behavior of fine soils density in Sarabi Dam. The paper presents the implementation process and density of the soil layers. The input variables include 4 geotechnics and 4 implementation parameters. The geotechnic parameters consist of: optimum moisture content, maximum specific gravity, liquid and plasticity limit; implementation parameters consist of: the number of cross rollers, thickness of the layers and density and moisture of the soil obtained from the site. The model is based on multilayer neural network, using the error back propagation approach and it is capable of calculating the density. As a result, the maximum specific gravity laboratory, using the aforementioned geotechnic and implementa-tion parameters, is presented. The method compates the maximum specific gravity laboratory accurately at almost 100 percent.