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Application of geogrids in waste dump stability : A numerical modeling approach

- Rai, Rajesh, Khandelwal, Manoj, Jaiswal, Ashok

  • Authors: Rai, Rajesh , Khandelwal, Manoj , Jaiswal, Ashok
  • Date: 2012
  • Type: Text , Journal article
  • Relation: Environmental Earth Sciences Vol. 66, no. 5 (2012), p. 1459-1465
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  • Description: Geosynthetic is widely used to reinforce the weak rock mass, mine waste dump, soil slopes road cut slopes, etc. The present paper discusses the effect of geogrids on the stability of mine waste dump. The stability of mine waste dump has been done by Fast Langrage Analysis of Continua (FLAC) slope software, which is based on finite difference method. Reinforcement by geogrids mainly depends on the tensile strength, aperture size of geogrids, and particle size distribution of dump rock mass. Different permutations and combinations of spacing between two geogrid sheets have been taken into consideration to study the stability of mine waste dump. The factor of safety is calculated to quantify the effect of geogrids on waste dump slope. It has been observed from numerical modeling that the maximum slope angle is 45° at a height of 10 m. The scope of increasing slope angle from 45 to 60° is evaluated using geogrids. It has been found from the study that the factor of safety increases as the spacing between geogrids decreases. Maximum strain is also plotted of each case to identify the slip circle. The positions of geogrids modify the probable slip circle or failure plane of mine waste dump. Using ten geogrids at a spacing of 1 m, the slope angle can be increased up to 60° with factor of safety of 1.4. © 2011 Springer-Verlag.

Evaluation of dump slope stability of a coal mine using artificial neural network

- Rahul, Manoj, Rai, Rajesh, Shrivastva, B.K.

  • Authors: Rahul, Manoj , Rai, Rajesh , Shrivastva, B.K.
  • Date: 2015
  • Type: Text , Journal article
  • Relation: Geomechanics and Geophysics for Geo-Energy and Geo-Resources Vol. 1, no. 3-4 (2015), p. 69-77
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  • Description: Dump slope stability is critical in the design and operation of any open pit mines having significant impacts on safety and on the economics of an open pit project. The issues related to the stability of dumps is catching attention worldwide from quite some time, which is quite important for the safe working in and around monstrous structures as well as restricted availability of land. In the present paper, artificial neural network (ANN) has been used to calculate the factor of safety of dump slope of a coal mine. A three-layer, feed-forward back-propagation neural network having optimum hidden neurons is used to model. The input parameters are dump slope geometry, geotechnical properties and hydrological conditions have been used to evaluate the stability of dump slope. Six input parameters and one output parameters have been trained using various algorithms. New dump slope data sets have been used for the validation and comparison of the factor of safety of dump slope. Factor of safety has also been determined using numerical modeling technique. Results were compared between factor of safety, calculated from the numerical modeling tool and predicted value of factor of safety by ANN. It was found a very closer agreement between the predicted and calculated values of factor of safety for the dump slope with ANN over numerical modelling.

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
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  • 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.

Stability prediction of a natural and man-made slope using various machine learning algorithms

- Karir, Dhruva, Ray, Arunava, Kumar Bharati, Ashutosh, Chaturvedi, Utkarsh, Rai, Rajesh, Khandelwal, Manoj

  • Authors: Karir, Dhruva , Ray, Arunava , Kumar Bharati, Ashutosh , Chaturvedi, Utkarsh , Rai, Rajesh , Khandelwal, Manoj
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Transportation Geotechnics Vol. 34, no. (2022), p.
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  • Description: In this paper, an attempt has been made to implement various machine learning techniques to predict the factor of safety of a natural residual soil slope and a man-made overburden mine dump slope using several physical and geometrical parameters of the respective slopes. As the stability predictions of a slope, whether natural or man-made, is very complex and time-consuming, several machine learning-based algorithms like Support Vector Regressor, Artificial Neural Network, Random Forest, Gradient Boosting and Extreme Gradient Boost were selected for modelling. The results derived from the models were compared with those achieved from numerical analysis. Moreover, various performance indices such as coefficient of determination, variance account for, root mean square error, learning rate and residual error were employed to evaluate the predictive performance of the developed models. The results indicate an excellent prediction performance and ease of interpretation of tree-based algorithms like Random Forest, Gradient Boosting and Extreme Gradient Boost than linear models like Support Vector Regressor and Neural Network-based algorithm for both the slope types. The Support Vector Regressor has the least while Extreme Gradient Boost has the highest predictive performance. Also, it was observed that the efficiency of various machine learning models to predict the factor of safety was found to be superior in the case of man-made dump slope than natural residual soil slope. © 2022 Elsevier Ltd
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