Rock Slope Assessment using Artificial Neural Networks · based on several criteria: the structural...

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 assess and manage slope stability risk. For this purpose, the slope face of a study area is discretized into cells having homogenous aspect, slope angle, rock properties and joint set orientations. 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 implement the design, how to assess

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 hard rock.

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 important joint characteristics are spacing, 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 minimum volume of dislodged debris that constitutes a ‘true’ slope

failure, then the statistical distribution of computing Factors of Safety (FS) could be a measure of stability risks. FoS is defined as

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 aspect of the hazard and vulnerability (Wolf, et. al, 2018). In slope

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 failed material.

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 method and can induced instability due to excavation 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 - II Expansion

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 proposed quarry is between

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 observed by

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 for each type of failure on

a slope. Each type of failure is assumed to act independently, and they therefore follow separate calculation paths. Calculation of

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 remaining parameters are slope angle, slope height, and ground water level.

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

Pla

ne

fa

ilu

re

Criteria Plane Orientation Slope Orientation Initial Parameter

FoS Parameter Dip azm

Th

e p

lan

e m

ust

dip

>3

00

at

an

az

imu

th o

f sl

op

e o

f a

slo

pe

azm

+

/- 2

00

& p

lan

e d

ip <

slo

pe

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



© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | 63

We

dg

e f

ail

ure

Criteria Wedge Plane Orientation Slope

Orientation Initial

Parameter FoS

Parameter Set 1 Set 2

Dip azm Dip azm Dip

Inte

rsec

tio

n m

ust

be

form

ed f

rom

pla

nes

fro

m s

epar

ate

sets

. In

ters

ecti

on

mu

st d

ip >

30

0 a

nd

day

ligh

t o

n t

he

slo

pe.

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

To

pp

lin

g P

lan

e

Criteria

Toppling Plane Orientation Initial

Parameter Mode Set 1 Set 2 Set 3

Dip azm Dip azm Dip azm

Inte

rsec

tio

n m

ust

dip

>6

00

tow

ard

(sl

op

e az

m +

18

0)

+

/- 2

0 &

pla

ne

mu

st d

ip <

30

0 to

war

d s

lop

e az

m +

/- 2

0. 30-45 +20-90 30-45 -20-90 <30 +/- 20 0

ψp

< Ф

p –

Sta

ble

X

/ Y

< t

an ψ

p –

To

pp

le

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

Ind

ices

of

each

typ

e o

f fa

ilu

re a

re m

ult

ipli

ed b

y t

he

pri

nci

ple

sp

acin

g

fact

ors

an

d p

ersi

sten

ce f

acto

rs f

or

each

rel

evan

t jo

int

set.

Sp

aci

ng

Fa

cto

r

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

Pe

rsis

ten

ce

Fa

cto

r

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

Ap

ert

ure

Fa

cto

r

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

Blo

ck s

ize

s

Fa

cto

r

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

We

ath

eri

ng

F

act

or

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

Str

en

gth

F

act

or

Parameter Parameter Value Type

Weak 2 Multiplicative

Moderate strong 1.5 Multiplicative

Strong 1 Multiplicative

Very strong 1

GW

Fa

cto

r Parameter Parameter Value Type

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 are arranged in the form of

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-layered networks.

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 using this set of inputs produces its own outputs.

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 different layers of the network: a feed-forward pass and a backward pass.

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 target output.

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 called the training epoch. During the testing

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 of multilayer perceptron with BP algorithm

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 the same quality. If we click Performance in the training window; 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 is found to be 0.99 and having

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, and the red bars represent testing data. The histogram

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 weight has

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 determine the

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 close relationship

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 that require the walls of the pit to

be stable for the required life of the pit, which may extend into closure.

Discontinuity data recording should be simultaneously carried out with quarrying operations. It will provide a guideline for

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, Open pit slope design,

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, Performance indicators and basic probability concepts, (2010), Ch-3, pp 111

- 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 Independent Publishing Platform.

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, Lieberwirth and Klein; 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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