Evaluation of Dump Slope Stability of a Coal Mine Using Artificial Neuralnetwork 2168 9806 1000128

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Volume 4 Issue 1 1000128J Powder Metall Min

ISSN: 2168-9806 JPMM, an open access journal

Research Article Open Access

Manoj et al., J Powder Metall Min 2015, 4:1

http://dx.doi.org/10.4172/2168-9806.1000128

Research Article Open Access

Powder Metallurgy & Mining

Evaluation of Dump Slope Stability of a Coal Mine Using Artificial Neural

NetworkManoj K1*, Rahul2, Rai R2and Shrivastva BK2

1Department of Mining Engineering, College of Technology and Engineering, Maharana Pratap University of Agriculture and Technology, Udaipur 313001, India

2Department of Mining Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India

*Corresponding author:Manoj K, Department of Mining Engineering, College of

Technology and Engineering, Maharana Pratap University of Agriculture and Tech-

nology, Udaipur 313001, Rajasthan, India, Tel: +91 294 2471 379; Fax: +91 294 2471

056; E-mail: [email protected]

ReceivedJanuary 15, 2015; AcceptedMarch 30, 2015; PublishedApril 07, 2015

Citation:Manoj K, Rahul, Rai R, Shrivastva BK (2015) Evaluation of Dump Slope

Stability of a Coal Mine Using Articial Neural Network. J Powder Metall Min 4: 128.

doi:10.4172/2168-9806.1000128

Copyright: 2015 Manoj K, et al. This is an open-access article distributed underthe terms of the Creative Commons Attribution License, which permits unrestricted

use, distribution, and reproduction in any medium, provided the original author and

source are credited.

Keywords: Dump slope; Limit equilibrium; FLAC; Artificial neuralnetwork

Introduction

Since 1970s, the Indian mining industry has witnessed a seachange, particularly in Coal mining. Te production o coal increaserom a level o 75 Ms in 1973 has reached to over 530 Ms in 2011.Te increase in production is mainly contributed by the surace coalmine and today more than 88% o coal production is coming rom

surace mines. When the coal production is increased it has alsoincrease, the excavation o overburden and inter-burden materialcausing ormation o both large size spoil dumps both internal andexternal. Tese large sizes o dump have become more important in

view o restricted availability o land. Dumping o waste without anyprior analysis has caused the waste dump ailures [1]. Failure o dumpsis a complex problem it directly affects the resource recovery, minesaety and mining cost. It may also cause constant danger to the menand machinery deployed there with a potential to cause catastrophicloss o lie and property. An analysis o accidents in opencast minesrevealed that slope ailure and dump ailure have started assumingan upward trend in the recent times [2] So, detailed analysis o dumpstability is now getting prime importance throughout the world.

Stability o dump slopes is dependent on many parameters, like

geometry o dump slope which includes overall dump height, overall

slope angle, bench angle and number o benches; geology o dumping

site; hydrological conditions o the area; geotechnical properties o

dump like cohesion, density and angle o internal riction o dumping

material; method or construction o dump; nature o oundation o

dump; seismic activities and vegetation [3]. Tese are governing the

stability o dumps. Analysis or stability o dump is carried out by

using these actors only. Te majority o slope stability analyses are

perormed by using traditional limit equilibrium (LE) approaches

involving methods o slices that have remained essentially unchanged

or decades. Recently, the numerical modeling technique has been

developed to model strong discontinuity using standard constitutive

equations and is being applied or analyzing slope stability in solid

mechanics. It allows to model strong discontinuities which is imbeddedinto the element displacement field. However, it needs good knowledgeo finite element or finite difference, computer programs and to have

enough time to prepare input data and running computer programs[4,5]. So, there is a need o the hour or a suitable and substitute methodinstead o LE or numerical methods with quick and accurate results. Inthe present paper, artificial neural network (ANN) has been used toanalyze the stabilities o dump slopes. ANN is having high flexibilityin learning such type o complex problems. ANN has the advantage oLE and numerical methods together and also improves the deect andshort coming o LE and numerical methods [4]. Khandelwal et al. [6]analyzed the stability o coal mine waste dump using limit equilibriummethod. Rai et al. [7] used geo-grids to enhance the stability o wastedump and analyzed the results with numerical modeling. Tey oundthat stability o waste dump can be enhanced considerably using geo-grids at certain intervals. Khandelwal and Singh [8] predicted the actoro saety o dump slope using artificial neural network and ound verygood correlation. Here, an attempt has been made to evaluate andpredict the actor o saety o dump slope using the artificial neuralnetwork (ANN) technique.

Artificial Neural Network (ANN)

Artificial neural network is a branch o the artificial intelligence,which also includes case-based reasoning, expert systems, and geneticalgorithms. Te classical statistics, uzzy logic and chaos theory arealso considered to be related fields. ANN is an inormation processing

system simulating the structure and unctions o the human brain. It is ahighly interconnected structure that consists o many simple processingelements (called neurons) capable o perorming massively parallelcomputation or data processing and knowledge representation. Te

Abstract

In the present paper, articial neural network has been used to calculate the factor of safety of dump slope of

a coal mine. The input parameters are dump slope, geotechnical properties of dump and hydrological condition

of dump slope used to evaluate the stability of dump slope. A three-layer, feed-forward back-propagation neural

network having optimum hidden neurons is used to model. 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 by Articial Neural Network (ANN) and numerical modeling (Finite element tool

and Finite difference). ANN technique is considered to be one of the most intelligent tools for simulating complex

problems. This technique has the ability of generalizing a solution from the pattern presented to it during training.Results were compared between factor of safety calculated from the numerical modeling tool and predicted value of

factor of safety by using ANN.
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Citation:Manoj K, Rahul, Rai R, Shrivastva BK (2015) Evaluation of Dump Slope Stability of a Coal Mine Using Articial Neural Network . J Powder

Metall Min 4: 128. doi:10.4172/2168-9806.1000128

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neural network is first trained by processing a large number o inputpatterns and the corresponding output. Te neural network is able torecognize similarities when presented with a new input pattern afer

proper training and predicting the output pattern.

Network Training

A network first needs to be trained beore interpreting newinormation. A number o algorithms are available or training oneural networks but the back-propagation algorithm is the most

versatile and robust technique. It provides the most efficient learningprocedure or multilayer neural networks. Te eed orward back-propagation neural network (BPNN) always consists o at least threelayers: input layer, hidden layer and output layer. Each layer consistso a number o elementary processing units called neurons and eachneuron is connected to the next layer through weights, i. e. neurons inthe input layer will send their output as input to neurons in the hidden

layer and similar is the connection between hidden and output layer.Number o hidden layers and neurons in the hidden layer is changedaccording to the problem to be solved. Te number o input andoutput neuron is the same as the number o input and output variables.During training o the network, data is processed through the inputlayer to hidden layer, until it reaches the output layer (orward pass).In this layer, the output is compared to the measured values (the trueoutput). Te difference or error between both is propagated backthrough the network (backward pass) updating the individual weightso the connections and the biases o the individual neurons. Te inputand output data are mostly represented as vectors called training pairs.Te process as mentioned above is repeated or all the training pairs inthe data set, until the network error converges to a threshold defined bya corresponding unction; usually the root mean squared error (RMS)

or summed squared error (SSE) (Figure 1). In the training process, theinterconnections between the neurons are initially assigned specificweights. Te network would be able to perorm a particular unction byadjusting the initial weights [9]. A single neuron containing multipleinputs (x

1,,x

n) and a single output (y) is shown in Figure 2. Te ANN

network can be defined by three undamental components: transerunction, network architecture, and learning law. Tere are differenttypes o transer unction unctions (nonlinear and/or linear) arepurelin, logsig, and hardlim. Tere are many types o network used insimulation o various problems by ANN, eed orward neural network,redial basis unction network, and recurrent network are generallyused so ormulate the problems by ANN. Basically the neural networkarchitecture include number o hidden layer, number o hidden nodes,

number o output nodes and activation unction. Te learning law canbe categorized in three distinct sorts supervised learning or associativelearning, unsupervised learning or sel-organization and reinorcementLearning. However selection o the unction is perormed according tothe problem to be solved. Each input is weighted with an appropriatew. Te sum o the weighted inputs and the bias orms the input to thetranser unction .

1

n

i

i

ia x w b

=

= +

A large number o hidden layer nodes have large number oassociated undetermined parameters, and i the number o trainingpairs is small, the network will then tend to memorize rather thangeneralize. Seibi and Al-Alawi [10] pointed out that an over determinednetwork should be used in order to have a good approximation over theregion o interest. Neural networks may be used as a direct substituteor auto correlation, multi variable regression, linear regression, andother statistical analysis and techniques. Neural networks, with theunremarkable ability to derive a general solution rom complicated orimprecise data, and detect trends that are too.

Numerical Modeling of Dump Slope

A number o different numerical methods exist e. g. BoundaryElement Methods (BEM), Finite Element methods (FEM) and FiniteDifference Methods (FDM). In boundary element methods, only theboundaries o a problem need to be discretized into elements [11].For finite element and finite difference methods, the entire problem

domain must be discretized into elements. Another class o methodsis the Discrete Element Method (DEM). While BEM, FEM and FDMall are continuum methods, DEM is a discontinuum method in whichdiscontinuities present in the rock mass are modeled explicitly. Incontinuum models, the displacement field will always be continuous.No actual ailure surace discontinuity is ormed, but the location othe ailure surace can be judged by the concentration o shear strainin the model [12,13]. Shear Strength reduction (SSR) technique hasbeen used in numerical modeling to calculate the actor o saety. Teactor o saety o a slope is the ratio o actual soil shear strength tothe minimum shear strength required to prevent ailure or the actorby which soil shear strength must be reduced to bring a slope to the

verge o ailure [14]. Te material shear strengths are progressivelyreduced until collapse occurs. Figure 3 shows the discretized model o

dump slope by finite element tool. Factor o saety o each simulatedmodel is calculated. Selection o parameters is very important step orurther analysis by ANN. Graphs are plotted between actor o saety

Figure 1:A view of articial neural network model.

Figure 2: Graphical representation of a single neuron.


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and varying parameter and based on the variation the parameters have

been selected or ANN analysis [15].

Input and Output Parameters

Initial various numbers o numerical models have been preparedand solved. Te actor o saety is calculated by SR. Ten the selectedparameters have been used as input to developed ANN models. In thisstudy, ANN is used to find out the actor o saety o coal mine dumpslope. Here, 520 dump slope models are used to develop ANN. Terange o overall dump height, overall slope angle, number o benches,hydrological condition, cohesion o dump material, and internal angleo riction o dump material are taken as input parameters and actoro saety is considered as an output parameter or the development oANN model (able 1) [16,17].

Network Architecture

Feed orward back-propagation neural network architecture isadopted here due to its appropriateness or this type o problem. Teobjective o the present investigation is to predict actor o saety o coalmine dump slope rom relevant parameters. It is difficult to determineall the relevant parameters, which have direct influence on the actoro saety o dump slope. However, the influencing parameters are notindependent and some o them are strongly correlated. Hence, it wasnot possible to use all the variables as input parameters. A three-layereed-orward back-propagation neural network was developed topredict the actor o saety. Te input layer has 6 input neurons andthe output layer has one neuron, whereas the hidden layer has 7 hiddenneurons (Figure 4). raining o the network is carried out using 80 % casesand testing and evaluation o the network is perormed using 20% differentcases. All data type which is used here to build ANN network are numeric.angential and exponential unctions are used as transer unction.

Results and Discussion

By considering six input parameters the various models are ormed

and simulated using numerical modeling tools. Factor o saety o eachsimulated model is calculated based on input parameters. Figure 5shows the simulated model and model with probable plane o ailurerespectively by finite element method. In this study, ANN is used tofind out the actor o saety o coal mine dump slope. Here, 520 dumpslope models are used to develop ANN. Te range o overall dumpheight, overall slope angle, number o benches, hydrological condition,cohesion o dump material, and internal angle o riction o dumpmaterial are taken as input parameters and actor o saety is consideredas input parameter or development o ANN model. Te range o inputand output parameters are given in able 1. o test and validate theANN model, data sets are chosen, which is not used while training thenetwork. Te results are presented in this section to demonstrate theperormance o the networks. est error between the predicted and

observed values is taken as the measure o perormance. o reach anappropriate architecture, three layer back propagation artificial neuralnetwork is selected with one hidden layer. Te network with the lowesterror is selected as desired network. Results o some models are in theable 2. Te prediction model 6-6-1 with training algorithm BFGS119 is considered as the optimum model. For evaluation o a model, acomparison between predicted and measured values o actor o saetycan be ulfilled. For this purpose, graphs are plotted between actor o

Figure 3: Descritized model of dump slope by using FEM.

Name of parameter Range

INPUT PARAMETERS

1 Overall height (In meter) 40 meter-200 meter

2 Overall slope angle 17-39

3 Number of benches 2-7

4 Groundwater condition 0-50% of overall dump height

DUMP MATERIAL QUALITY

5 Cohesion 10K Pa-70 Kpa

6 Internal angle of friction 10-45

OUTPUT PARAMETER

7 Factor of safety 0.5-2.8

Table 1:Show the input and output parameters with their range for the present

study.

Figure 4:Network architecture.

Figure 5:Maximum shear stress and FOS diagram by using FEM.


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saety (output) versus actor o saety (target) or training, testing andvalidation data sets as shown in Figures 6-8.

Conclusions

Predicted actor o saety value is almost overlapped the measuredactor o saety, so ANN can be used or the calculation o actoro saety. Considering the complexity o the relationship amongthe inputs and outputs, the results obtained by ANN are highlyencouraging and satisactory. ANN can learn new patterns that are

not previously available in the training dataset. ANN can also updateknowledge over time as long as more training data sets are presented,and can process inormation in parallel way. Hence, the technique

proves to be economical and easier in comparison to tedious expensive

experimental work.References

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Summary of active networks

Index Model Training error Test error Prediction error Training algorithm Hidden activation Output activation

1 MLP 6-3-1 0.000467 0.000176 0.000337 BFGS 108 Exponential Exponential

2 MLP 6-5-1 0.000297 0.000130 0.000225 BFGS 131 Tanh Tanh

3 MLP 6-6-1 0.000220 0.000114 0.000170 BFGS 119 Tanh Tanh

4 MLP 6-9-1 0.000235 0.000140 0.000170 BFGS 118 Exponential Sine

5 MLP 6-7-1 0.000453 0.000171 0.000389 BFGS 68 Tanh Exponential

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Figure 6:Targeted and output factor of safety by ANN (For training data).

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Figure 8:Targeted and output factor of safety by ANN (For validation data).
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Citation: Manoj K, Rahul, Rai R, Shrivastva BK (2015) Evaluation of Dump

Slope Stability of a Coal Mine Using Articial Neural Network. J Powder Metall

Min 4: 128. doi:10.4172/2168-9806.1000128
http://dx.doi.org/10.4172/2168-9806.1000128http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1623280http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1623280http://link.springer.com/article/10.1007%2Fs12665-011-1385-1http://link.springer.com/article/10.1007%2Fs12665-011-1385-1http://link.springer.com/article/10.1007%2Fs12665-011-1385-1http://link.springer.com/article/10.1007%2Fs12665-011-1385-1http://www.sciencedirect.com/science/article/pii/S0013794496000768http://www.sciencedirect.com/science/article/pii/S0013794496000768http://dx.doi.org/10.4172/2168-9806.1000128http://dx.doi.org/10.4172/2168-9806.1000128http://www.sciencedirect.com/science/article/pii/S0013794496000768http://www.sciencedirect.com/science/article/pii/S0013794496000768http://link.springer.com/article/10.1007%2Fs12665-011-1385-1http://link.springer.com/article/10.1007%2Fs12665-011-1385-1http://link.springer.com/article/10.1007%2Fs12665-011-1385-1http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1623280http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=1623280http://dx.doi.org/10.4172/2168-9806.1000128

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Evaluation of Dump Slope Stability of a Coal Mine Using Artificial Neuralnetwork 2168 9806 1000128