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Improvement in Prediction of Slope Stability & Relative Importance Factors Using ANN

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Evaluation of slope stability using conventional limit equilibrium methods is very time consuming and repetitive, while the use of simplified approaches like regression analysis does not provide accurate estimation due to complexity and nonlinearities involved in the process. In such cases Artificial Neural Networks (ANNs) provide a better alternative. By proper training, an ANN with desirable transfer function and suitable number of hidden layers is able to well predict the nonlinearities and can provide accurate estimation of slope stability. However, performance of ANN in the past studies on slope stability prediction is found to be poor, while the prediction of relative importance of various slope contributing factors is not reliable. This is primarily due to the use of limited number of real field data cases and/or synthetic data covering limited parametric variations, in the training process. In the present study, an ANN has been trained using extensive synthetic dataset consisting of 15,000 cases covering wide range of soil properties & slope geometry, and then applied to the real field slopes to test its accuracy. The ANN presented here is showing significant improvement in assessing the Factor of Stability of slopes as compared to the ANN used in previous studies. The present ANN is also able to provide accurate estimation of Factor of Safety of real slopes comparable to any conventional limit equilibrium methods. Thus, ANN can be used for the estimation of Factor of Safety of real slopes, especially where it is required to estimate stability conditions rapidly such as landslide early warning, post-earthquake landslide activity, etc. Further, reliable estimates of relative importance of various contributing factors to slope stability have also been obtained, which have several applications.

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Authors are thankful to Mrs Anjana Kukunuri for providing valuable inputs & suggestions in the work carried out as well as in the preparation of the manuscript.

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Correspondence to Balendra M. Marrapu or Ravi S. Jakka.

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Appendix: Calculation of Relative Importance Factors

Appendix: Calculation of Relative Importance Factors

The residual interconnection weights between the neurons obtained after successful training of ANN with the huge data (Sect. 5) is provided in the following Table

Table 9 Final residual interconnection weights between the neurons

9. This information is used further to calculate importance factors of various parameters contributing slope stability based on the method suggested by Garson (1991).

The absolute value of interconnection weights between each hidden neuron and input neurons is multiplied by the corresponding absolute value of interconnection weight between the hidden and output neuron. These connection weights products are given in the Table

Table 10 Connection weights products


Each connection weight product of hidden neuron is normalized with respect to sum of the all connection weight products of the corresponding hidden neuron. The normalized connection weight products are given in Table

Table 11 Normalized connection weight products


The relative importance of each input parameter is obtained by adding the normalized connection weight products of that particular input neuron to all the hidden neurons (See Table

Table 12 Estimated relative importance factors of input parameters

12). These importance factors are further expressed in percentages.

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Marrapu, B.M., Kukunuri, A. & Jakka, R.S. Improvement in Prediction of Slope Stability & Relative Importance Factors Using ANN. Geotech Geol Eng 39, 5879–5894 (2021).

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