IRJET - Rock Slope Assessment using Artificial Neural Networks

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 04 | Apr 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 559
Rock Slope Assessment using Artificial Neural Networks
Prashant K. Nayak1, S. Srinivas2, N. Rakesh2, G. Sanjeev Kumar2, B. Mahesh Babu2
1 Assistant Professor, Dept. of Mining Engineering, Godavari Institute of Engineering & Technology (Autonomous),
Rajahmundry, Andhra Pradesh, India.
2B. Tech Final Year Student, Dept. of Mining Engineering, Godavari Institute of Engineering & Technology
(Autonomous), Rajahmundry, Andhra Pradesh, India.
---------------------------------------------------------------------***----------------------------------------------------------------------
Abstract – In the risk analysis of slope stability, it is utmost necessary for a mining engineer to provide a reasonable factor of
safety, which gives not only the reliability but also the economic conditions. The stability of slopes in open pit mines is of great
concern because of the significant detrimental consequence’s instabilities can have. To ensure the safe and continuous economic
operation of the open pit mines, it is utmost necessary to systematically assessandmanageslopestabilityrisk. Forthispurpose, the
slope face of a study area is discretized into cells having homogenous aspect, slopeangle, rockproperties andjointsetorientations.
In this paper, an ANN based model is developed by which the objective function i.e. Probability of failure is assessed by the
combination of discontinuity parameters and slope geometry which defines the instability in rock slopes.
Key Words: Rock Slope Instability; Artificial Neural Networks; MATLAB.
1. INTRODUCTION
Reliable slopes are essential to the design of an open pit mine, at all scales and at every level of project development. The slope
design process, including how to gather reliable data, how to formulate the design, how to implementthedesign,howtoassess
the reliability of the outcome, and how to manage risk. If slope instabilities do develop, they must be manageable at all pit
scales, from the individual benches to the overall slopes. When managing the failures, it is essential that a degree of stability is
ensured to minimize risk (Read & Stacey, 2017).
Slope instability can be caused by failure occurring through weak intact rock or along pre-existing discontinuities in hardrock.
The type of rock discontinuities and its characteristics helps to determine their effect on rock mass properties. In rock mass,
joints are a source of weakness and can be the source of instability. The importantjoint characteristics arespacing, persistence,
joint roughness, aperture, and joint orientation (Gratchev, 2019).
To conduct stability analyses and develop optimum slope angles for input into pit design process, the proposed pit must be
divided into design sectors that are sections of the pit with similar geological and operational characteristics. This selection is
based on several criteria: the structural domain, the wall orientation, and operational considerations. Since a pit geometry is
required to define, design sectors, slope design are iterative with mine planning (Fleurisson & Cojean, 2014).
To ensure the safe and economic operation of these mines, it is necessary to systematically access and manage slope stability
risk. The methodology and factors that impact on rock mass slope stability risks are data collection, processing, reliability,and
the partitioning of data into domains. If all geotechnical inputs and factors impacting failure modes had been considered by
appropriate statistical methods, and consensus exists on the minimumvolumeofdislodgeddebris thatconstitutes a ‘true’slope
failure, then the statistical distribution of computing Factors ofSafety(FS)couldbea measureofstabilityrisks.FoSisdefinedas
the resisting shear strength divided by the activating force (Baczynski, 2016).
The risk is estimated as the product of probability of the potentially damaging event and its consequences. The specific failure
risk may be expressed as follows: R = H × E × (V × C); where H = Probability of a potentially damaging event of a given
magnitude; E = Set of elements at risk to the hazardous event; V = Vulnerability of the exposed element (s); and C = Cost. H and
V variables are (+) the numbers for measuring the probability aspectofthehazardand vulnerability (Wolf, et.al,2018).Inslope
design, the risks (R) associated with slope failure are defined and quantified as: R = PoF X Consequences of failure (Read &
Stacey, 2017). In this paper, ANN model is developed to assess the probability of failure by assessment of slope risk by the
combination of discontinuity parameters and slope geometry which defines the instability of the rock mass.

Table - 1: Controllable factors influencing the Stability of Rock Slopes
Parameter Effect
Excavation
Geometry
Slope Orientation Appropriate orientation w.r.t. the geology reduces instability.
Slope Angle The lower slope angle may increase stability.
Berms Wide berms limit the size of potential failures and retain failedmaterial.
Excavation
Method
Mechanical Excavation Minimize disturbance of rock mass reduces instability.
Bulk Density Can produce induced instability.
Controlled Blasting Use to minimize induced stability.
Environment
Vegetation Roots dilate discontinuities, increases stress and weathering.
Proximal Engineering Position of other structures can influence stability.
Table - 2: Uncontrollable factors influencing the Stability of Rock Slopes
Parameter Effect
Meteorology
Rainfall Water pressures drive instability. Water increases weathering.
Temperature Temperature affects weathering.
Freeze/Thaw Ice wedging acts as driving force on failure masses.
Geology
&
Geotechnics
Intact Rock
Properties
Determines excavation methodandcaninducedinstabilityduetoexcavation
method.
Discontinuity
Properties
Shear strength, orientation, frequency, aperture, roughness, and infill
influence the scale and the likelihood of instabilities.
In-situ Stress Increase in stress deceases overall stability.
Earthquakes Increases the destabilizing forces on the slope.
Pre-existing Slides Can be reactivated due to excavate and may have post-peak shear strength.
Topography Pre-existing Relief Determines the geometry of the design.
Table - 3: FoS and PoF acceptance criteria values (Read & Stacey, 2009).
Slope scale Consequence of
failure
Acceptance criteria
FoS (Min) (Static) FoS (Min) (Dynamic) PoF (Max) P [FoS ≤ 1]
Bench Low-high 1.1 NA 25-50%
Inter-ramp
Low 1.15-1.2 1.0 25%
Medium 1.2 1.0 20%
High 1.2-1.3 1.1 10%
Overall
Low 1.2-1.3 1.0 15-20%
Medium 1.3 1.05 5-10%
High 1.3-1.5 1.1 ≤5%
Fig - 1 - Correlation between velocity of slope movement and movement classification and sensitivity
to environmental factors (Vaziri, et. al, 2010).

Fig - 2 - Risk Acceptability Criteria (Steffen et al., 2006).
2. Rock Slope Assessment - Case Study
2.1 Project Background
The SRP OC - II Expansion Project is proposed to operate in the Srirampur Area where one opencast mine is already in
operation under the name of SRP OC - II Project. The proposed project is expansion of SRP OC - II Project. SRP OC - IIExpansion
Project is proposed with method of mining/technology (i.e. Opencast with Shovel Dumper combination technology), the
Hazards were identified based on the previous experience of the SRP OC - II Project with the following criteria. The following
are the possible hazards identified for the proposed project basing on the
Tasks/Activities/Workplaces involved: For Pit Slope Stability - The ultimate working depth of the proposedquarryisbetween
120 m to 350 m. There may be chances of slope failure, where the depth is more.
Identified Hazards Mechanism Control Action
Slope Stability
Failure of Pit Slope when
the depth is more and
intercepted by a number
of faults.
The overall pit slope varies from 400 to
420. This has been done to ensure safe pit
slope for the prevalent strata conditions.
For Slope stability, care is taken while
forming the batter on the east side of the
quarry fault zone by pre-split blasting.
The movement of the
slope shall be observedby
installing subsidence
movement pillars.
The surveyor should
ensure frequently.
The derived values are likely to be valid for the entire quarry. The slope stability analysis was done based on the data of rock
mass discontinuity parameters and geometric parameters. It may however be prudent, from time to time, to re-examine the
local changes in the different geotechnical parameters.

3. METHODOLOGY
3.1 Derivation of Factor of Safety (FoS) Index
The FoS Parameter is derived from a standard set of calculations which use the parameter values as input. The logic of the
derivation of the FoS Parameter is that Primary parameter values are derived related to the potential foreachtypeoffailureon
a slope. Each type of failure is assumed to act independently, andtheythereforefollowseparatecalculationpaths.Calculationof
the FoS Index involves multiplying the Primary parameters by successive Secondary parameters and adding other relevant
Primary parameters.
3.2 Input Parameters and Data Collection
Input Parameters for calculation of FoS Index are failure specific and a complete set of parameters are required for each
potential failure observed on the rock slope and the remainingparameters areslopeangle,slopeheight,andgroundwaterlevel.
The relevant parameters are as follows:
1. Potential failure plane discontinuities: Dip and azimuth, Join Spacing, Trace Length, Aperture, Block Sizes, Weathering
adjacent to potential failure, Rock Strength and Ground water Conditions.
2. Potential failure dimension: Height, Width, and Depth (Depth for Toppling only; can be calculated for Plane and Wedge).
Fig - 3a - Discontinuity Dip, Azimuth & Trace Length. Fig - 3b - Resolution of the fracture frequency for one set.
Geotechnical Parameters - Initial Parameter Indices from Discontinuity - Slope Geometry Relationships
Plane
failure
Criteria Plane Orientation Slope Orientation Initial
Parameter
FoS
Parameter
Dip azm
The
plane
must
dip
>30
0
at
an
azimuth
of
slope
of
a
slope
azm
+/-
20
0
&
plane
dip
<
slope
dip.
30-45 +/- 20 <45 0.5 Stable
30-45 +/- 20 45-60 1.0 Stable
30-45 +/- 20 60-70 1.0 Stable
30-45 +/- 20 70-90 1.0 Stable
45-70 +/- 20 <45 0 Stable
45-70 +/- 20 45-60 0.3 Stable
45-70 +/- 20 60-70 0.8 Stable
45-70 +/- 20 70-90 1.0 Stable
70-90 +/- 20 <45 0 Stable
70-90 +/- 20 45-60 0 Stable
70-90 +/- 20 60-70 0 Stable
70-90 +/- 20 70-90 0.5 Stable

Wedge
failure
Criteria
Wedge Plane Orientation Slope
Orientation
Initial
Parameter
FoS
Parameter
Set 1 Set 2
Dip azm Dip azm Dip
Intersection
must
be
formed
from
planes
from
separate
sets.
Intersection
must
dip
>30
0
and
daylight
on
the
slope.
30-45 +20-90 30-45 -20-90 <45 0.09 Fail
30-45 +20-90 30-45 -20-90 45-60 0.16 Fail
30-45 +20-90 30-45 -20-90 60-70 0.16 Fail
30-45 +20-90 30-45 -20-90 70-90 0.16 Fail
30-45 +20-90 45-70 -20-90 <45 0.18 Fail
30-45 +20-90 45-70 -20-90 45-60 0.37 Fail
30-45 +20-90 45-70 -20-90 60-70 0.37 Fail
30-45 +20-90 45-70 -20-90 70-90 0.37 Fail
30-45 +20-90 70-90 -20-90 <45 0.25 Fail
30-45 +20-90 70-90 -20-90 45-60 0.5 Fail
30-45 +20-90 70-90 -20-90 60-70 0.5 Fail
30-45 +20-90 70-90 -20-90 70-90 0.5 Fail
45-70 +20-90 45-70 -20-90 <45 0.15 Stable
45-70 +20-90 45-70 -20-90 45-60 0.45 Stable
45-70 +20-90 45-70 -20-90 60-70 0.67 Stable
45-70 +20-90 45-70 -20-90 70-90 0.77 Stable
45-70 +20-90 70-90 -20-90 <45 0.16 Stable
45-70 +20-90 70-90 -20-90 45-60 0.49 Stable
45-70 +20-90 70-90 -20-90 60-70 0.70 Stable
45-70 +20-90 70-90 -20-90 70-90 0.87 Stable
70-90 +20-90 70-90 -20-90 <45 0.12 Stable
70-90 +20-90 70-90 -20-90 45-60 0.30 Stable
70-90 +20-90 70-90 -20-90 60-70 0.57 Stable
70-90 +20-90 70-90 -20-90 70-90 0.86 Stable
Toppling
Plane
Criteria
Toppling Plane Orientation
Initial
Parameter
Mode
Set 1 Set 2 Set 3
Dip azm Dip azm Dip azm
Intersection
must
dip
>60
0
toward
(slope
azm
+
180)
+/-
20
&
plane
must
dip
<30
0
toward
slope
azm
+/-
20.
30-45 +20-90 30-45 -20-90 <30 +/- 20 0
ψ
p
<
Ф
p
–
Stable
X
/
Y
<
tan
ψ
p
–
Topple
30-45 +20-90 30-45 >90 <30 +/- 20 0
30-45 >90 30-45 >90 <30 +/- 20 0
30-45 +20-90 45-70 -20-90 <30 +/- 20 0
30-45 +20-90 45-70 >90 <30 +/- 20 0
30-45 >90 45-70 -20-90 <30 +/- 20 0
30-45 >90 45-70 >90 <30 +/- 20 0
30-45 +20-90 70-90 -20-90 <30 +/- 20 0
30-45 +20-90 70-90 >90 <30 +/- 20 0
30-45 >90 70-90 -20-90 <30 +/- 20 0
30-45 >90 70-90 >90 <30 +/- 20 0
45-70 +20-90 45-70 -20-90 <30 +/- 20 0
45-70 +20-90 45-70 >90 <30 +/- 20 0
45-70 >90 45-70 >90 <30 +/- 20 0.06
45-70 +20-90 70-90 -20-90 <30 +/- 20 0
45-70 +20-90 70-90 >90 <30 +/- 20 0
45-70 >90 70-90 -20-90 <30 +/- 20 0.12
45-70 >90 70-90 >90 <30 +/- 20 0.24
70-90 +20-90 70-90 -20-90 <30 +/- 20 0.04
70-90 >90 70-90 -20-90 <30 +/- 20 0.27
70-90 >90 70-90 >90 <30 +/- 20 0.27

Factors for Observed Failure Condition
Multiplicative Parameters
Parameter Parameter Value Parameter Value
Plane Failure Observed 1 Not Observed 0.5 Multiplicative
Wedge Failure Observed 1 Not Observed 0.5 Multiplicative
Toppling
Failure
Observed 2 Not Observed 0.5
Multiplicative
Additive Parameters
Parameter Parameter Value Type
Plane Failure 1 Initial
Wedge Failure 1 Initial
Toppling Failure 2 Initial
Discontinuity Size and Spacing Factors
Criteria Description Joint spacing (m) Parameter Value Type
Indices
of
each
type
of
failure
are
multiplied
by
the
principle
spacing
factors
and
persistence
factors
for
each
relevant
joint
set.
Spacing
Factor
Extremely close spacing <0.02 9 Multiplicative
Very close spacing 0.02–0.06 6.5 Multiplicative
Close spacing 0.06–0.2 2.25 Multiplicative
Moderate spacing 0.2–0.6 1 Multiplicative
Wide spacing 0.6–2 0.35 Multiplicative
Very wide spacing 2–6 0.11 Multiplicative
Extremely wide spacing >6 0 Multiplicative
Persistence
Factor
Description Trace length (m) Parameter Value Type
Very low persistence < 1 0.25 Multiplicative
Low persistence 1–3 1 Multiplicative
Medium persistence 3–10 4 Multiplicative
High persistence 10–20 16 Multiplicative
Very high persistence > 20 56 Multiplicative
Aperture
Factor
Description Aperture (mm) Parameter Value Type
Very tight < 0.1 0 Multiplicative
Tight 0.1–0.25 0.11 Multiplicative
Partly open 0.25–0.5 0.35 Multiplicative
Open 0.5–2.5 1 Multiplicative
Moderately wide 2.5–10 1.2 Multiplicative
Wide > 10 1.3 Multiplicative
Very wide 10–100 >1.4 Multiplicative
Extremely wide 100–1000 >1.4 Multiplicative
Cavernous > 1000 >1.4 Multiplicative
Block
sizes
Factor
Description Jv (joints/m3) Parameter Value Type
Very large blocks < 1 0 Multiplicative
Large blocks 1–3 0.25 Multiplicative
Medium-sized blocks 3–10 1 Multiplicative
Small blocks 10–30 4 Multiplicative
Very small blocks 30–60 16 Multiplicative
Crushed rock > 60 56 Multiplicative

Weathering Strength and Water Factors
Weathering
Factor
Option Parameter Value Type
Fresh 1 Multiplicative
Slight 1 Multiplicative
Moderate 1.2 Multiplicative
Highly 1.5 Multiplicative
Complete 2 Multiplicative
Residual 2.5 Multiplicative
Strength
Factor
Weak 2 Multiplicative
Moderate strong 1.5 Multiplicative
Strong 1 Multiplicative
Very strong 1
GW
Factor
None 1 Multiplicative
Minor 1.1 Multiplicative
Moderate 1.2 Multiplicative
Extreme 1.3 Multiplicative
Geometric Parameters
Factor Range Parameter Value Type
Slope Angle
30-450 0 Additive
45-600 0.5 Additive
60-700 1 Additive
70-900 1.5 Additive
Slope Height
3-6m 0 Additive
6-12m 0.5 Additive
12-20m 1 Additive
>20m 1.5 Additive
4. BUILDING NEURAL NETWORK MODEL
The neural network design process has 7 steps: (1) Collect data; (2) Create the network; (3) Configure the network; (4)
Initialize the weights and biases; (5) Train the network; (6) Validate the network (post-training analysis); and (7) Use the
network. An ANN is a group of interconnected artificial neurons, interacting with one another in a concerted manner. Feed
forward networks have one-way connections, from the input to the output layer. Here, the neurons arearrangedintheformof
layers. Neurons in one layer get inputs from the previous layer and feed their outputs to the next layer. The last layer is called
the output layer. Layers between the input and output layers are called hidden layers and are termed multi-layerednetworks.
The number of hidden layers and neurons in the hidden layer is usually defied by trial and error method. ANN study’s input,
output relationships by suitably adjusting the synaptic weights in a process known as training.
In supervised learning, target values or desired responses are known and are given to ANN during training so that ANN can
adjust its weights to try to match its output to the target values. Before the learning algorithms are applied to update the
weights, all the weights are initialized randomly (Haykin, 1999). The network usingthissetofinputsproducesitsownoutputs.
These are compared with the target outputs and the difference between them, called the error, is used for modifying the
weights. The architecture of MLP is a multi-layered feed-forward neural network, in which nonlinear elements (neurons) are
arranged in successive layers and the information flows unidirectionally, that is from the input layer to the output layer
through hidden layers. MLP is trained by using supervised algorithms known as the back-propagation algorithm.

The backpropagation (BP) algorithm allows experimental acquisition of input/output mapping knowledge within multilayer
networks. There are basically two passes through the differentlayersofthe network:a feed-forwardpassanda backwardpass.
In the forward pass, an input pattern is submitted and propagated through the network, layer by layer. A set of outputs is
produced as the actual response of the network. During the forward pass, the synaptic weights are all fixed, and in the
backward pass, the synaptic weights are all adjusted, depending on the error between the actual output and the targetoutput.
The process is continued until all the input patterns from the training set are learned with an acceptable overall error. The
error is cumulative and computed over the entire training set. This computation is calledthetraining epoch.During thetesting
phase, the trained network it operates in a feed-forward manner (Haykin 1999).
The performance of the back-propagation algorithm depends on following:
1. Initial weights - The network weights are initialized to small random values. The initialization strongly affects the final
solution.
2. The transfer function of the Nodes - For calculating the value of δ in the backward pass, the requirement is that the
activation function should be differentiable.
3. Learning rate - The effectiveness and convergence of back propagation algorithm depend significantly on the value of the
learning rate η. By trial and error, the value of the learning rate provides an optimum solution. The value is lesser than 1.
4. Momentum coefficient - The momentum term is generally used to accelerate the convergence of the error BP algorithm.
This involves the use of momentum coefficient α. This is a simple method of increasing the rate of learning and yet avoids
the danger of instability. The value chosen is generally lesser than 1.
5. Number of hidden neurons - The optimal number of hidden nodes in any network for solving any given problem is
determined by trial and error. Hidden units play a critical role in the operation ofmultilayerperceptronwithBPalgorithm
learning as they act as feature detectors.
5. Supervised Learning - Using Neural Network Fitting Tools
In this work, the ANN model was developed by using MATLAB R2016b software for windows. Data for functional fitting
problems are set up in a neural network by organizing the data into two matrices, the input matrix X and the target matrix
T. Input ‘data’ is a 350 x 7 matrix, representing static data of 350 samples of 7 elements. Target ‘data’ is a 350 x 1 matrix,
representing static data of 350 samples of 1elements. Then divides input vectors and target vectors into three sets as follows:
(a) 60% is used for training; (b) 20% are used to validate that the network is generalizing and to stop training over fitting, and
(c) 20% are used as a completely independent test of network generalization.
a b
Fig - 4 - Three independent data sets for (a) Independent and (b) Split Sample testing (Priddy & Keller, 2005).

a b
Fig - 5 - Block diagram: (a) Training Stage and (b) Operation stage (Priddy and Keller, 2005).
For training the ANN, Scaled Conjugate Gradient (trainscg) is recommended as it uses gradient calculations which are more
memory efficient than the Jacobian calculations i.e. Two algorithms Levenberg-Marquardt and Bayesian Regularization. The
training continued until the validation error failed to decrease for six iterations (validation stop). From a given random
initialization of the network, every 'run' produces distinct results. We get distinct results from those depicted here, but if the
modelling process goes well, we should expect results of thesamequality.Ifweclick Performanceinthetrainingwindow;a plot
of the training errors; validation errors; and test errors appeared. The only sign of the derivative is used to determine the
direction of the weight updates.
6. RESULTS AND DISCUSSIONS
The model is validated by comparing the results with the remaining 140 rock slope cases and found that the predicted results
are having a very close relationship with the actual results. The value of correlation coefficient, R isfoundtobe0.99 andhaving
a very low RMSE value of 0.05. The Simulink model for ANN is shown in Fig 7. Hence, it is concluded that ANN can be used as a
good prediction tool for slope stability risk analysis. The Error Histogram of the network is shown above, the blue bars
represent the training data, the green bars represent the validation data,andtheredbarsrepresenttestingdata.Thehistogram
gives the indication of outliners, which are data points where the fit is significantly worse than most data. In this case, most
errors fall between -25.03 and 25.7. These outliners give the idea to determine if the data is bad, or if those data points are
different than the rest of the data set. The magnitude of the derivative has no effect on the weight update. The update value for
each weight and bias is increased by a predefined value whenever derivative of the performance function w.r.t.that weighthas
the same sign for the two successive iterations. The update value is decreased by that value whenever the derivative w.r.t.that
weight changes signs from the previous iteration.

In this case, the result is reasonable because of the following: (1) The final mean-square error is small; (b) The test set error,
and the validation set error has similar characteristics (green & red lines in the plot); and (c) No significance over fitting has
occurred by iteration 6 (where the best validation performance occurs). The coefficient of correlation is used to determinethe
relative correlation and the goodness of fit between the predicted and observed data. A suggested guide for values of |R|
between 0.0 and 1.0: (1) |R| > 0.8 => Strong correlation exists between two sets of variables; (2) 0.2 < |R| < 0.8 => Correlation
exists between the two sets of variables: and (3) |R| < 0.2 => Weak correlation exists between the two sets of variables. The
regression plot gives the value of R for training, testing, and validation in Fig 6. From the regression plot, it was found that the
value of R equals to 0.99 which is very close to unity. Hence, it can be stated that the prediction results bear a closerelationship
between the input variables.
Fig - 6 - Regression Plot showing the value of R for training, testing, and validation.
Fig - 7 - Simulink Model for ANN.

Fig - 8 - Performance Plot for Predicted ANN Model.
Fig - 9 - Training State Plot for showing Gradient and Validation check with epoch.
Fig -10 - Error Histogram Plot of Predicted ANN Model.

7. CONCLUSIONS
 Risk-based design provides enough quantitative information to: (a) Define acceptable risks in terms of safety and
economics; and (b) Assess relative risks for different slope configurations.
 The results of the analytical analyses form the basis for more vigorous numerical analysis which serves to verify and
validate the recommended discontinuity sets, slope angles and the open pit geometry.
 Before the slope designs are accepted, they must be aligned with the slope failure criteria thatrequirethewallsofthepitto
be stable for the required life of the pit, which may extend into closure.
 Discontinuity data recording should be simultaneouslycarriedoutwithquarryingoperations.Itwill providea guidelinefor
carrying out excavations in other parts of the deposit.
 Finally, implement the steps as recommended in the DGMS Circular No.2, 2010 to control slope failures.
REFERENCES
1. Abramson, L. W. (1996), Slope Stability and Stabilisation Methods. Wiley, New York.
2. Xia-Ting Feng (2017), Rock Mechanics and Engineering, Vol. 3: Analysis, Modeling & Design,Ch.-25,Openpitslopedesign,
Read & Stacey, pp 785-818.
3. Kyle Rollins, Dimitrios Zekkos, Geotechnical Engineering State of the Art and Practice (2012), Ch - 6, Assessment of Slope
Stability, American Society of Civil Engineers.
4. Chowdhury, R. N., Geotechnical slope analysis,Performanceindicatorsandbasicprobability concepts,(2010),Ch-3,pp111
- 126, Taylor & Francis Group.
5. Vaziri A., Moore L., Hosam Ali H., Monitoring systems for warning impending failures in slopes and open pit mines, Nat
Hazards (2010) 55:501–512.
6. Haykin, S. (1999). Neural Networks—A Comprehensive Foundation. Prentice Hall, Upper Saddle River, New Jersey.
7. Smith, J., Machine Learning with Neural Networks using MATLAB, (2017), Create Space IndependentPublishingPlatform.
8. Priddy K. L., and Keller P.E. (2005). ‘Artificial Neural Networks - An Introduction’, SPIE - The International Society for
Optical Engineering, Bellingham, Washington.
9. Chaturvedi, D. K., Modeling and Simulation of Systems using MATLAB and Simulink, (2010), Ch-10, pp 503 - 511,Taylor&
Francis Group.
10. Demuth H. and Beale M. (2010), Neural Network Toolbox for Use with MATLAB. The Math Works Inc., Natick, Mass.
11. Fleurisson, A., and Cojean, R., Error Reduction in Slope Stability Assessment. Bhattacharya, LieberwirthandKlein;Surface
Mining Methods, Technology and Systems. Vol. 1, Wide, 41p, 2014.
AUTHOR
Mr. Prashant K. Nayak,
Assistant Professor, Department of Mining Engineering, Godavari Institute of Engineering &
Technology (Autonomous), Rajahmundry, Andhra Pradesh, India.

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