ORIGINAL ARTICLE

Evaluation and prediction of blast-induced ground vibrationat Shur River Dam, Iran, by artificial neural network

Masoud Monjezi • Mahdi Hasanipanah •

Manoj Khandelwal

Received: 23 April 2011 / Accepted: 17 January 2012 / Published online: 18 February 2012

� Springer-Verlag London Limited 2012

Abstract The purpose of this article is to evaluate and

predict blast-induced ground vibration at Shur River Dam

in Iran using different empirical vibration predictors and

artificial neural network (ANN) model. Ground vibration

is a seismic wave that spreads out from the blasthole

when explosive charge is detonated in a confined manner.

Ground vibrations were recorded and monitored in and

around the Shur River Dam, Iran, at different vulnerable

and strategic locations. A total of 20 blast vibration records

were monitored, out of which 16 data sets were used for

training of the ANN model as well as determining site

constants of various vibration predictors. The rest of the 4

blast vibration data sets were used for the validation and

comparison of the result of ANN and different empirical

predictors. Performances of the different predictor models

were assessed using standard statistical evaluation criteria.

Finally, it was found that the ANN model is more accurate

as compared to the various empirical models available. As

such, a high conformity (R2 = 0.927) was observed

between the measured and predicted peak particle velocity

by the developed ANN model.

Keywords Ground vibration � Blasting � Artificial

neural network � Shur River Dam

1 Introduction

Ground vibrations resulting from the blasting are nettle-

some problems for mining. Whenever an explosive charge

detonates in a blasthole, huge amount of energy in terms of

high pressure and temperature is released. Only a part

(20–30%) of this energy is used for the actual fragmenta-

tion and displacement of the rock mass, and the rest of the

energy is wasted away and creates nuisances, such as blast

vibration, air blast, flyrock, noise, dust and back break. All

around the world, researchers are working to provide

appropriate damage criteria and to enhance the blasting

efficiency with higher trustiness and lesser nuisances [1–4].

Ground vibration is the result of the blasting operation.

This phenomenon is very crucial and critical as compared

to other ill effects that involve public residents in the close

vicinity of mining sites, mine owners and environmental-

ists. Also, with the emphasis shifting toward eco-friendly,

sustainable and geo-environmental activities, the field of

ground vibration has now become an important and imper-

ative parameter for safe and smooth running of any mining

and civil project [5]. When intensity of ground vibrations is

high, it annoys human beings and can even damage nearby

structures. As a consequence, measurement and prediction

of ground vibration level is necessary to judge the design of

blasting operations [6]).

Level of ground vibration depends mainly on two

parameters, that is, controllable parameters and uncon-

trollable parameters. Controllable parameters include bur-

den, spacing, stemming, subdrilling, hole diameter, total

charge and maximum charge per delay and delay interval,

M. Monjezi (&)

Faculty of Engineering, Tarbiat Modares University,

Tehran, Iran

e-mail: monjezi@modares.ac.ir

M. Hasanipanah

Islamic Azad University, Tehran South Branch, Tehran, Iran

M. Khandelwal

Department of Mining Engineering, College of Technology

and Engineering, Maharana Pratap University of Agriculture

and Technology, Udaipur 313001, India

e-mail: mkhandelwal1@gmail.com

123

Neural Comput & Applic (2013) 22:1637–1643

DOI 10.1007/s00521-012-0856-y

while uncontrollable parameters are rock conditions,

geology and rock properties [7].

A number of empirical equations were proposed by

different researchers to predict the peak particle velocity

(PPV) and to minimize and control the ground vibration.

Ground vibration is defined in terms of PPV. The maximum

velocity (measured in millimeters per second or inches per

second) at which a particle moves in the ground relative to

its inactive state is termed as PPV. The most commonly used

empirical predictor equations are shown in Table 1.

Where PPV is peak particle velocity (mm/s), D is the

distance between the blasting face to measuring locations

(m), W is the maximum explosive charge per delay (kg), K,

a, b and n are the site constants, which can be calculated by

multiple regression analysis.

Because the number of influencing parameters is too

high and the inter-relation among them is also very com-

plicated, empirical methods may not be fully suitable for

such problems. Currently, various artificial intelligence

techniques are frequently applied in such type of problems.

The artificial neural network (ANN) is a branch of the

artificial intelligence science and has developed rapidly

since the 1980s. Nowadays, ANN is considered one of the

intelligent tools to solve complex problems. ANN has the

ability to learn from patterns acquainted before. It is a

highly interconnected network of a large number of pro-

cessing elements called neurons in an architecture inspired

by the brain. ANN can be massively parallel and hence said

to exhibit parallel distributed processing [13].

ANN shows characteristics such as mapping capabilities

or pattern association, generalization, robustness or fault

tolerance as well as parallel and high-speed information

processing. ANN learns by examples; thus, it can be

trained with known examples of a problem to acquire

knowledge about it. Once appropriately trained, the net-

work can be put to effective use of solving unknown or

untrained instances of the problem [14]. Due to its multi-

disciplinary nature, ANN is becoming popular among the

researchers, planners, designers, etc. as an effective tool for

the accomplishment of their work. Therefore, ANN is

being successfully used in many industrial areas as well as

in research area. A number of researchers are using ANN in

various geo-mining problems [5, 6, 13, 15–19].

In the present paper, different empirical equations and

ANN technique have been used to predict PPV and the

computed results are compared with the data of the actual

field.

2 Case study

The project test site is the region of Shur River Dam in Sar

Cheshmeh, Iran. Topography of the region has led to

design dam structure in two separate parts, as shown in

Fig. 1. The first part is the main dam with height 85.5 m,

and the second part is the saddle dam with height 36.5 m

from the foundation. The dam can store up to 34 million

cubic meters of water. Different mines are in the vicinity of

the dam site. Mines in this region include main mine and

second mine. Distance between main mine and second

mine, and river dam are 1,000 and 500 m, respectively.

There is always a high risk of damage to dam due to blast

vibration. So, a blast vibration study was carried out at dam

site while blasting is performed in the mines. A total of 20

blast vibration records were monitored at the dam site.

Ground vibration was recorded with the help of a seis-

mograph MR 2002-CE from M/S SYSCOM, Swiss, Fig. 2.

This seismograph measures PPV in three orthogonal

directions. Dynamic range of this seismograph is more than

96 dB and sampling rate is 500 samples per second.

3 Data set

In this study, a total of 20 blast vibration records were

monitored at the dam site from which 16 data sets were

used for training of the ANN model and getting site

Table 1 Different empirical formulas for predicting PPV [8–12]

Empirical formulas Equation

USBM PPV ¼ K � Dffiffiffiffi

Wph in

Ambraseys–Hendron PPV ¼ K � DW0:33

h in

Davies et al. [10] PPV ¼ K�Db

W�a

Indian Standard PPV ¼ K � WD2=3

h i�n

Roy PPV ¼ aþ K � Dffiffiffiffi

Wph i�n

Fig. 1 Location of main dam and saddle dam

1638 Neural Comput & Applic (2013) 22:1637–1643

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constants of empirical equations, whereas the rest of the 4

data sets were used for the validation and comparison of

the ANN model and the results of other predictors.

Different blast parameters collected from the site are

PPV (mm/s), total charge (kg), maximum charge per delay

(kg), and distance between shot point and monitoring sta-

tion (m). Table 2 shows sample data sets of blast vibration.

Also, Fig. 3 shows the particle velocity produced from a

blast event.

4 Neural network model

ANNs have originated from the biological structure of the

human brain. Neural network model is built of cells that are

called neurons. Each neuron can receive, process and

transmit the electro-chemical signals dendrites extend from

the cell body to other neurons. Therefore, the neural net-

work establishes a relationship between input and output.

4.1 Training a network with back-propagation

algorithm

A neural network needs first to be trained before importing

new data sets. Different algorithms are available to train

neural networks, but back-propagation algorithm is mostly

used to solve the predicting problems [19].

The back-propagation networks have an input layer, one

or more hidden layers, and an output layer, as shown in

Fig. 4. Each layer is composed of different processing

elements called neurons. A transfer function processes

input data that reach the corresponding neuron. To differ-

entiate between the different processing units, values called

biases are introduced in the transfer functions. These biases

are referred to as the temperature of a neuron.

The algorithm that is used to train network is scaled

conjugate gradient. This algorithm is so abstruse, but its

base is made by two ways, namely Levenberg–Marquardt

algorithm and conjugate gradient algorithm.

Fig. 2 MR2002 to measure ground vibration

Table 2 Results of the ground vibration measurements

Event Total

charge (kg)

Maximum charge

per delay (kg)

Distance

(m)

PPV

(mm/s)

1 5,280 2,800 1,334.5 1.9

9 6,400 5,300 823 5.38

14 8,500 6,400 904 5.42

Fig. 3 Particle velocity from an

operation blasting, recorded by

MR2002

Neural Comput & Applic (2013) 22:1637–1643 1639

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5 Model performance

Performances of the different predictor models were

assessed using different standard statistical performance

evaluation criteria. The statistical measures considered

were coefficient of determination (COD), mean absolute

percentage error (MAPE), root mean square error (RMSE),

variance absolute relative error (VARE), median absolute

error (MEDAE) and variance account for (VAF). Various

statistical performance measures are listed in Table 3.

Where ti is measured values and xi is predicted values by

predictor models. A model which gives higher VAF is

better than that which gives lower VAF. A model which

gives lower MAPE is better than that which gives higher

MAPE. A model which gives lower VARE is better than

that which gives higher VARE. A model which gives lower

MEDAE is better than that which gives higher MEDAE.

6 Network architecture for predicting PPV

In this study, a four-layer feed-forward back-propagation

neural network is considered as the model. The architecture

of the network is given in Table 4.

7 Testing and validation by ANN

For validating the ANN model, some data sets that were

not used for training networks were randomly chosen. The

coefficient of the determination and the various statistical

performance measures between the predicted and the recorded

values are calculated for the evaluation of network per-

formance. Figure 5 shows the correlation between recorded

PPV and predicted PPV by ANN. Here, COD between

measured and predicted PPV is 0.927, whereas RMSE is

0.071.

Fig. 4 Back-propagation network

Table 3 List of the performance measures

Statistical

parameter

Equation

Mean absolute

percentage errorMAPE ¼ 1

n�P

n

i¼1

ti�xi

ti

�

�

�

�

�

�

� �

� 100

Root mean square

error RMSE ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

1n�

P

n

i¼1

½ðti � xiÞ2�s

Variance absolute

relative errorVARE ¼ 1

n�P

n

i¼1

ti�xi

ti

�

�

�

�

�

��mean ti�xi

ti

�

�

�

�

�

�

� �2� �

� 100

Median absolute

error

MEDAE = median (ti - xi)

Variance account

forVAF ¼ 1� var(ti�xiÞ

varðtiÞ

h i

� 100

Table 4 The architecture of the network

No. of input neurons 3

No. of output neurons 1

No. of hidden layers 2

No. of neurons in first hidden layer 4

No. of neurons in second hidden layer 4

No. of training data sets 16

No. of testing data sets 4

R2 = 0.927

4.1

4.2

4.3

4.4

4.5

4.6

4.7

4.8

4.2 4.3 4.4 4.5 4.6 4.7 4.8

measured PPV (mm/s)

pre

dic

ted

PP

V b

y A

NN

Fig. 5 Measured and predicted PPV by ANN

Table 5 Calculated values of site constants

Equation Site constants

K n a b

USBM 39.24 -0.83 – –

Ambraseys–Hendron 45.165 -0.58 – –

Davies et al. [10] 1.365 – 0.46 -0.38

Indian Standard 2.3 0.19 – –

Roy 52 0.54 – –

1640 Neural Comput & Applic (2013) 22:1637–1643

123

8 Predictions by different empirical equations

Table 5 illustrates the values of site constants of different

predictor equations, which were determined by multiple

regression analysis.

Figures 6, 7, 8, 9 and 10 graphically show correlations

between recorded and predicted PPV by different empirical

equations. Here, COD is 0.945, 0.193, 0.491, 0.391 and

0.949 for USBM, Ambraseys–Hendron, Davies et al. [10],

Indian Standard and Roy, respectively, whereas RMSE is

0.382, 0.416, 0.835, 0.493 and 0.428 for USBM, Ambra-

seys–Hendron, Davies et al. [10], Indian Standard and Roy,

respectively.

R² = 0.945

0

1

2

3

4

5

6

4.2 4.3 4.4 4.5 4.6 4.7 4.8

measured PPV (mm/s)

pre

dic

ted

PP

V b

y U

SB

M

Fig. 6 Measured and predicted PPV by USBM

R² = 0.193

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

4.2 4.3 4.4 4.5 4.6 4.7 4.8

measured PPV (mm/s)

pre

dic

ted

PP

V b

y A

-H

Fig. 7 Measured and predicted PPV by Ambraseys–Hendron

R² = 0.491

0

1

2

3

4

5

6

4.2 4.3 4.4 4.5 4.6 4.7 4.8

measured PPV (mm/s)

pre

dic

ted

PP

V b

y D

avie

s et

al.

[10]

Fig. 8 Measured and predicted PPV by Davies et al. [10]

R2 = 0.3914

0

1

2

3

4

5

6

4.2 4.3 4.4 4.5 4.6 4.7 4.8

measured PPV (mm/s)

pre

dic

ted

PP

V b

y In

dia

n S

tan

dar

d

Fig. 9 Measured and predicted PPV by Indian Standard

R2 = 0.9494

0

1

2

3

4

5

6

4.2 4.3 4.4 4.5 4.6 4.7 4.8

measured PPV (mm/s)

pre

dic

ted

PP

V b

y R

oy

Fig. 10 Measured and predicted PPV by Roy

Neural Comput & Applic (2013) 22:1637–1643 1641

123

9 Results and discussion

Table 6 shows COD, MAPE, VARE, MEDAE, VAF and

RMSE for ANN and various vibration equations. Here,

maximum RMSE is for prediction by Davies et al. [10],

while minimum is for ANN.

10 Sensitivity analysis

Sensitivity analysis is a method to determine the effec-

tiveness of each input parameter on the amount of output

parameter. In this paper, input parameters are total charge,

maximum charge per delay and distance between shot

point and monitoring station, and output parameter is PPV.

For sensitivity analysis, the following equation was used:

Rij ¼Pm

k¼1 ðxik � xjkÞffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

Pmk¼1 x2

ik

Pmk¼1 x2

jk

q ð1Þ

where xi and xj are the elements of data pairs, which

indicate the input and output data sets, respectively. The

effect of each input parameter on the amount of PPV is

shown in Fig. 11. From the figure, it can be said that

distance between shot point and monitoring station has the

greatest effect on the amount of PPV, whereas maximum

charge per delay and total charge significantly affect the

blast vibration. In fact, sensitivity analysis is used before

determining the number of input parameters. Thus, low-

impact parameters are not used in the neural network and

network provides a model with more accuracy and less

error.

11 Conclusion

Ground vibration is an undesirable effect of blasting and

must be controlled and minimized to enhance the explosive

efficiency. Here, PPV has been predicted by the ANN as

well as different empirical equations. It was found that

results obtained from the neural network model is much

closer to reality, because of the empirical formula’s restric-

tions in utilized effective parameter of ground vibration.

However, empirical formula is based on only two parame-

ters: the maximum charge per delay and the distance of

explosion’s location. It is well known that ground vibration

is influenced by a number of parameters such as total

charge, burden, spacing and stemming. The advantage of

the neural network model is the unrestricted parameters

utilized for a more accurate prediction. If the parameter’s

variety in the input layer is more, the results will be closer to

reality. Selecting the number of hidden layers and the

number of neurons in the hidden layers contains no specific

theorems and is achieved by trial and error. It was found that

COD between measured and predicted PPV is 0.927,

whereas it was 0.945, 0.193, 0.491, 0.391 and 0.949 for the

USBM, Ambraseys–Hendron, Davies et al. [10], Indian

Standard and Roy equations, respectively. It was also found

that MAPE, VARE, MEDAE and RMSE were also mini-

mum for the ANN model as compared to various empirical

models. Sensitivity analysis was also carried out on maxi-

mum charge per delay, total charge and distance between

shot point and monitoring station, and it was found that

distance between shot point and monitoring station has

greatest effect on the PPV.

Table 6 Performance of different models for predicting PPV

Model COD MAPE VARE MEDAE VAF RMSE

USBM 0.945 7.25 0.26 0.264 19.71 0.382

Ambraseys–Hendron 0.193 8.75 0.129 0.366 3.37 0.416

Davies et al. [10] 0.491 16.67 0.768 0.692 6.79 0.835

Indian Standard 0.391 10.99 0.31 0.51 23.30 0.493

Roy 0.949 8.13 0.328 0.31 18.02 0.428

ANN 0.927 1.34 0.07 0.05 92.68 0.071

0.894 0.893

0.961

0.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

Total charge Max charge per delay Distance

Input parameters

Fig. 11 Sensitivity analysis for determining the effect of each input

parameter on PPV

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