Nonlinear neural-based modeling of soil cohesion intercept

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.

Explicit formulation of bearing capacity of shallow foundations on rock masses using artificial neural networks: application and supplementary studies

“…This activity is then changed by a function (Transfer Function) to produce the output of the layer (y j ) or input of next layer. For nonlinear problems, the sigmoid functions (Hyperbolic tangent sigmoid or log-sigmoid) are usually selected as the transfer function (Alavi et al 2010;Mollahasani et al 2011). This process is typically shown by Eq.…”

“…According to a universal approximation theorem (Cybenko 1989), a single hidden layer network is sufficient for the traditional MLP to uniformly approximate any continuous and nonlinear function. Choosing the number of the hidden layers, hidden nodes, learning rate, epochs, and activation function type plays an important role in the model construction (Alavi et al 2010;Mollahasani et al 2011). Hence, several MLP network models with different settings for the mentioned characters were trained to reach the optimal configurations with the desired precision (Eberhart and Dobbins 1990).…”

“…This approach has been implemented by several researchers (Das and Basudhar 2008;Alavi et al 2010;Mollahasani et al 2011). Figure 6 shows the procedure of this algorithm (Alavi et al 2010).…”

“…Artificial neural network (ANN) is a well-known branch of soft computing (Alavi et al 2010). This technique has been successfully employed to solve problems in civil engineering field (e.g., Kayadelen et al 2009;Günaydın 2009;Kolay et al 2010;Das et al 2010;Yilmaz 2010a, b; Akgun and Türk 2010; Kaunda et al 2010;Das et al 2011a, b, c;Mert et al 2011;Mollahasani et al 2011;Yilmaz et al 2012;Sattari et al 2012;Tasdemir et al 2013;Seker 2012, 2013;Isik and Ozden 2013;Alkhasawneh et al 2014;Wu et al 2013;Maiti and Tiwari 2014;Park et al 2013;Ceryan et al 2013;Manouchehrian et al 2014). Besides, ANN has been used to predict the bearing capacity of shallow foundations resting on soil layers (Soleimanbeigi and Hataf 2005;Padmini et al 2008;Kuo et al 2009;Kalinli et al 2011).…”

Prediction of compression index using artificial neural network

“…Recently ANNs have been employed to model complex relationships between input and output datasets in geotechnical engineering (Sinan, 2009;Ozer et al, 2008;Park et al, 2009;Cho, 2009;Park, 2010;Park and Cho, 2010;Park and Lee, 2011;Park and Kim, 2010;Mollahasani et al, 2011;Goktepe et al, 2010).…”