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Dry Density Based on Soil Index Properties by Using Expert System

Dry density modeling is a valuable issue. Artificial neural networks (ANNs) have been used in many problems in geotechnical engineering and have demonstrated great success. In this paper, the ANN model is proposed to predict the dry density of the soil. The developed model is managed by the Matlab Neural Network Interface (R2016a). To create the ANN model, liquid limit, plastic limit, plasticity index, moisture content, specific gravity, finer accuracy than sieve 200, total suspended solids, organic and SO3 were selected and used as input parameters. There are (9, 6,5 and 3) nodes, (10) nodes and (1) node used for input, hidden layers and output layers, respectively. The value of dry density obtained from three sources was sympathetic. The first source is the experimental results of 99 soil samples conducted in Al-Najaf Institution laboratory for this study. The second source was to propose the expected dry density using multiple linear regression analysis (MLRA) on the samples used in the first source; The results show, that the prediction of the use of ANNs was closely consistent with the experimental data. Correlation coefficient (R2) and mean square error (MSE) were 0.97368 and 3.19474 10-3, respectively. The observed results of the proposed system were very comparable with those obtained from empirical analysis and the prediction obtained from multiple linear regression analysis, where the advanced ANN approach is applicable.