Abstract
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.
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Mollahasani, A., Alavi, A.H., Gandomi, A.H. et al. Nonlinear neural-based modeling of soil cohesion intercept. KSCE J Civ Eng 15, 831–840 (2011). https://doi.org/10.1007/s12205-011-1154-4
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DOI: https://doi.org/10.1007/s12205-011-1154-4