Stability evaluation of dump slope using artificial neural network and multiple regression
- Bharati, , Ashutosh, Ray, Arunava, Khandelwal, Manoj, Rai, Rajesha, Jaiswal, , Ashok
- Authors: Bharati, , Ashutosh , Ray, Arunava , Khandelwal, Manoj , Rai, Rajesha , Jaiswal, , Ashok
- Date: 2022
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 38, no. (2022), p. 1835-1843
- Full Text:
- Reviewed:
- Description: The present paper focuses on designing an artificial neural network (ANN) model and a multiple regression analysis (MRA) that could be used to predict factor of safety of dragline dump slope. To implement these two models, the dataset was utilized from the numerical simulation results of dragline dump slopes, wherein 216 dragline dump slope models were simulated using a numerical modeling technique employed with the finite element method. The finite element model was incorporated a combination of three geometrical parameters, namely, coal-rib height (Crh), dragline dump slope height (Sh), and dragline dump slope angle (Sa) of the dump slope. The predicted results derived from the MRA and ANN models were compared with the results obtained from the numerical simulation of the dump slope models. Moreover, to compare the validity of both the models, various performance indicators, such as variance account for (VAF), determination coefficient (R2), root mean square error (RMSE), and residual error were calculated. Based on these performance indicators, the ANN model has shown a higher prediction accuracy than the MRA model. The study reveals that the ANN model developed in this research could be handy in designing the dragline dump slopes at the preliminary stage. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.
- Authors: Bharati, , Ashutosh , Ray, Arunava , Khandelwal, Manoj , Rai, Rajesha , Jaiswal, , Ashok
- Date: 2022
- Type: Text , Journal article
- Relation: Engineering with Computers Vol. 38, no. (2022), p. 1835-1843
- Full Text:
- Reviewed:
- Description: The present paper focuses on designing an artificial neural network (ANN) model and a multiple regression analysis (MRA) that could be used to predict factor of safety of dragline dump slope. To implement these two models, the dataset was utilized from the numerical simulation results of dragline dump slopes, wherein 216 dragline dump slope models were simulated using a numerical modeling technique employed with the finite element method. The finite element model was incorporated a combination of three geometrical parameters, namely, coal-rib height (Crh), dragline dump slope height (Sh), and dragline dump slope angle (Sa) of the dump slope. The predicted results derived from the MRA and ANN models were compared with the results obtained from the numerical simulation of the dump slope models. Moreover, to compare the validity of both the models, various performance indicators, such as variance account for (VAF), determination coefficient (R2), root mean square error (RMSE), and residual error were calculated. Based on these performance indicators, the ANN model has shown a higher prediction accuracy than the MRA model. The study reveals that the ANN model developed in this research could be handy in designing the dragline dump slopes at the preliminary stage. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.
Stability prediction of Himalayan residual soil slope using artificial neural network
- Ray, Arunava, Kumar, Vikash, Kumar, Amit, Rai, Rajesh, Khandelwal, Manoj, Singh, T.
- Authors: Ray, Arunava , Kumar, Vikash , Kumar, Amit , Rai, Rajesh , Khandelwal, Manoj , Singh, T.
- Date: 2020
- Type: Text , Journal article
- Relation: Natural Hazards Vol. 103, no. 3 (2020), p. 3523-3540
- Full Text:
- Reviewed:
- Description: In the past decade, advances in machine learning (ML) techniques have resulted in developing sophisticated models that are capable of modelling extremely complex multi-factorial problems like slope stability analysis. The literature review indicates that considerable works have been done in slope stability using ML, but none of them covers the analysis of residual soil slope. The present study aims to develop an artificial neural network (ANN) model that can be employed for evaluating the factor of safety of Shiwalik Slopes in the Himalayan Region. Data obtained from numerical analysis of a residual soil slope were used to develop two ANN models (ANN1 and ANN2 utilising eleven input parameters, and scaled-down number of parameters based on correlation coefficient, respectively). A four-layer, feed-forward back-propagation neural network having the optimum number of hidden neurons is developed based on trial-and-error method. The results derived from ANN models were compared with those achieved from numerical analysis. Additionally, several performance indices such as coefficient of determination (R2), root mean square error, variance account for, and residual error were employed to evaluate the predictive performance of the developed ANN models. Both the ANN models have shown good prediction performance; however, the overall performance of the ANN2 model is better than the ANN1 model. It is concluded that the ANN models are reliable, valid, and straightforward computational tools that can be employed for slope stability analysis during the preliminary stage of designing infrastructure projects in residual soil slope. © 2020, Springer Nature B.V.
- Authors: Ray, Arunava , Kumar, Vikash , Kumar, Amit , Rai, Rajesh , Khandelwal, Manoj , Singh, T.
- Date: 2020
- Type: Text , Journal article
- Relation: Natural Hazards Vol. 103, no. 3 (2020), p. 3523-3540
- Full Text:
- Reviewed:
- Description: In the past decade, advances in machine learning (ML) techniques have resulted in developing sophisticated models that are capable of modelling extremely complex multi-factorial problems like slope stability analysis. The literature review indicates that considerable works have been done in slope stability using ML, but none of them covers the analysis of residual soil slope. The present study aims to develop an artificial neural network (ANN) model that can be employed for evaluating the factor of safety of Shiwalik Slopes in the Himalayan Region. Data obtained from numerical analysis of a residual soil slope were used to develop two ANN models (ANN1 and ANN2 utilising eleven input parameters, and scaled-down number of parameters based on correlation coefficient, respectively). A four-layer, feed-forward back-propagation neural network having the optimum number of hidden neurons is developed based on trial-and-error method. The results derived from ANN models were compared with those achieved from numerical analysis. Additionally, several performance indices such as coefficient of determination (R2), root mean square error, variance account for, and residual error were employed to evaluate the predictive performance of the developed ANN models. Both the ANN models have shown good prediction performance; however, the overall performance of the ANN2 model is better than the ANN1 model. It is concluded that the ANN models are reliable, valid, and straightforward computational tools that can be employed for slope stability analysis during the preliminary stage of designing infrastructure projects in residual soil slope. © 2020, Springer Nature B.V.
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