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Nonlinear Neural-Based Modeling of Soil Cohesion Intercept
Ali Mollahasani,Amir Hossein Alavi,Amir Hossein Gandomi,Azadeh Rashed 대한토목학회 2011 KSCE JOURNAL OF CIVIL ENGINEERING Vol.15 No.5
A new model was derived to estimate undrained cohesion intercept (c) of soil using Multilayer Perceptron (MLP) of artificial neural networks. The proposed model relates c to the basic soil physical properties including coarse and fine-grained contents, grains size characteristics, liquid limit, moisture content, and soil dry density. The experimental database used for developing the model was established upon a series of unconsolidated-undrained triaxial tests conducted in this study. A Nonlinear Least Squares Regression (NLSR) analysis was performed to benchmark the proposed model. The contributions of the parameters affecting c were evaluated through a sensitivity analysis. The results indicate that the developed model is effectively capable of estimating the c values for a number of soil samples. The MLP model provides a significantly better prediction performance than the regression model.