Machine learning techniques applied to prediction of residual strength of clay

Abstract

Stability with first time or reactivated landslides depends upon the residual shear strength of soil. This paper describes prediction of the residual strength of soil based on index properties using two machine learning techniques. Different Artificial Neural Network (ANN) models and Support Vector Machine (SVM) techniques have been used. SVM aims at minimizing a bound on the generalization error of a model rather than at minimizing the error on the training data only. The ANN models along with their generalizations capabilities are presented here for comparisons. This study also highlights the capability of SVM model over ANN models for the prediction of the residual strength of soil. Based on different statistical parameters, the SVM model is found to be better than the developed ANN models. A model equation has been developed for prediction of the residual strength based on the SVM for practicing geotechnical engineers. Sensitivity analyses have been also performed to investigate the effects of different index properties on the residual strength of soil. Keywords

Fusing of optimized intelligence models by virtue of committee machine for estimation of the residual shear strength of clay

“…Referring to Das et al (2011), paper, RSS of clay can be determined as a function of parameters viz. LL, PI, CF, and DPI.…”

“…The great complexity and difficulties encountered in prediction of RSS of clay have motivated researchers to apply intelligent models for solving this problems. To date, a variety of intelligence based models are available in the literature for exploring complex nonlinear underlying relationships between the RSS of clay and clay index (Das and Basudhar 2008;Khan et al 2015;Das et al 2011). The basic idea of these models is to find out the relation between input/output through mimicking the neural behavior of brain.…”

“…They concluded that ANN can obtain adequate results in prediction of RSS of clay. In second group, two papers published recently for demonstrate the capability of intelligence models for estimation of RSS of clay (Khan et al 2015;Das et al 2011). These papers argue the capability of intelligence based model for identifying the functional dependency between RSS of clay and aforementioned four clay index.…”

Prediction of the residual strength of clay using functional networks

“…Hence, in the present study an attempt has been made to predict the residual friction of soil using FN based on a set of index properties including LL, PI, CF and DPI. The data set used for the study is the same as used by Das et al (2011). Functional Networks have not been applied to geotechnical engineering issues to the best of the knowledge of the authors.…”

“…Das and Basudhar (2008) used artificial neural network (ANN) modelling to predict the f r of clay, but their study was limited to tropical soil of a specific region only. Das et al (2011) provided an equation for the calculation of f r of soil based on their analysis of data using ANN and SVM. However, ANN has poor generalization, attributed to attainment of local minima during training and needs iterative learning steps to obtain better learning performances.…”