Artificial Neural Network (ANN) and Regression Tree (CART) applications for the indirect estimation of unsaturated soil shear strength parameters

doi:10.1007/s11707-014-0416-0

Front. Earth Sci.

2014, Vol. 8

Issue (3) : 439-456 https://doi.org/10.1007/s11707-014-0416-0

RESEARCH ARTICLE

Artificial Neural Network (ANN) and Regression Tree (CART) applications for the indirect estimation of unsaturated soil shear strength parameters

D.P. KANUNGO(

),Shaifaly SHARMA,Anindya PAIN

Geotechnical Engineering Group, CSIR – Central Building Research Institute (CBRI), Roorkee 247667, India

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Abstract

The shear strength parameters of soil (cohesion and angle of internal friction) are quite essential in solving many civil engineering problems. In order to determine these parameters, laboratory tests are used. The main objective of this work is to evaluate the potential of Artificial Neural Network (ANN) and Regression Tree (CART) techniques for the indirect estimation of these parameters. Four different models, considering different combinations of 6 inputs, such as gravel %, sand %, silt %, clay %, dry density, and plasticity index, were investigated to evaluate the degree of their effects on the prediction of shear parameters. A performance evaluation was carried out using Correlation Coefficient and Root Mean Squared Error measures. It was observed that for the prediction of friction angle, the performance of both the techniques is about the same. However, for the prediction of cohesion, the ANN technique performs better than the CART technique. It was further observed that the model considering all of the 6 input soil parameters is the most appropriate model for the prediction of shear parameters. Also, connection weight and bias analyses of the best neural network (i.e., 6/2/2) were attempted using Connection Weight, Garson, and proposed Weight-bias approaches to characterize the influence of input variables on shear strength parameters. It was observed that the Connection Weight Approach provides the best overall methodology for accurately quantifying variable importance, and should be favored over the other approaches examined in this study.

Keywords cohesion friction angle Artificial Neural Network Regression Tree Connection Weight Weight-bias Approach

Corresponding Author(s): D.P. KANUNGO

Issue Date: 04 July 2014

Cite this article:

D.P. KANUNGO,Shaifaly SHARMA,Anindya PAIN. Artificial Neural Network (ANN) and Regression Tree (CART) applications for the indirect estimation of unsaturated soil shear strength parameters[J]. Front. Earth Sci., 2014, 8(3): 439-456.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-014-0416-0
https://academic.hep.com.cn/fesci/EN/Y2014/V8/I3/439

Tab.1 Basic descriptive statistics for different parameters of soil samples

Tab.2 Correlation matrix and significance levels for the considered data set

Fig.1 A schematic description of the relationship between the input and output vectors of one neuron.

Fig.2 Neural network processing as implemented using coding developed in MATLAB Software.

Tab.3 Combined accuracies in terms of R and RMSE’s for both the shear parameters (c and ? ) for all 4 models for some selected neural networks

Fig.3 Correlation coefficients as obtained for Model II for the 5/16/2 neural network: (a) training and (b) testing.

Fig.4 Correlation coefficients as obtained for Model IV for the 6/2/2 neural network: (a) training and (b) testing.

Tab.4 Individual accuracies in terms of R and RMSE values for both of the shear parameters (c and ?) for all the 4 models for some selected neural networks

Fig.5 ANN training and testing accuracies in terms of correlation coefficients observed across different neural networks for the case of c for Model IV.

Fig.6 ANN training and testing accuracies in terms of correlation coefficients observed across different neural networks for the case of angle of internal friction (? ) for Model IV.

Tab.5 Connection weights and biases for cohesion (c) and friction angle (?) in case of a 6/2/2 neural network

Tab.6 Weights and ranks (in brackets) corresponding to all of the 6 input variables for the prediction of both c and ? as obtained using Connection weight approach, Garson’s approach and Weight-bias approach

Fig.7 Variation in RMSE and R values across different regression trees with varying splitmins for Model IV for: (a) cohesion and (b) friction angle.

Tab.7 Training and testing accuracies for prediction of c using Regression tree technique

Tab.8 Training and testing accuracies for prediction of ? using Regression tree technique

Fig.8 Most appropriate regression tree in the case of Model IV for predicting (a) cohesion and (b) angle of internal friction.

Fig.9 Experimentally determined and predicted values for the testing dataset in case of Model IV using regression tree technique: (a) cohesion and (b) angle of friction.

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