blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial...
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ORIGINAL ARTICLE
Blast-induced air and ground vibration prediction: a particleswarm optimization-based artificial neural network approach
Mohsen Hajihassani1 • Danial Jahed Armaghani2 • Masoud Monjezi3 •
Edy Tonnizam Mohamad2• Aminaton Marto2
Received: 28 January 2014 / Accepted: 1 March 2015
� Springer-Verlag Berlin Heidelberg 2015
Abstract Mines, quarries, and construction sites face
environmental damages due to blasting environmental
impacts such as ground vibration and air overpressure.
These phenomena may cause damage to structures,
groundwater, and ecology of the nearby area. Several
empirical predictors have been proposed by various
scholars to estimate ground vibration and air overpressure,
but these methods are inapplicable in many conditions.
However, prediction of ground vibration and air over-
pressure is complicated as a consequence of the fact that a
large number of influential parameters are involved. In this
study, a hybrid model of an artificial neural network and a
particle swarm optimization algorithm was implemented to
predict ground vibration and air overpressure induced by
blasting. To develop this model, 88 datasets including the
parameters with the greatest influence on ground vibration
and air overpressure were collected from a granite quarry
site in Malaysia. The results obtained by the proposed
model were compared with the measured values as well as
with the results of empirical predictors. The results indicate
that the proposed model is an applicable and accurate tool
to predict ground vibration and air overpressure induced by
blasting.
Keywords Vibration blasting impacts � Ground
vibration � Air overpressure � Artificial neural network �Particle swarm optimization
Introduction
In rock quarry blasting, only 20–30 % of the energy pro-
duced by explosives is utilized to fragment and displace the
rock mass. The rest of the energy is wasted and produces
undesirable environmental impacts such as ground vibra-
tion, air overpressure (AOp), flyrock, and back-break (Se-
garra et al. 2010; Monjezi et al. 2012; Raina et al. 2014;
Jahed Armaghani et al. 2014; Marto et al. 2014; Ebrahimi
et al. 2015). Various empirical predictors have been
established to predict ground vibration and AOp induced
by blasting. However, such approaches only consider
limited numbers of parameters influencing ground vibra-
tion and AOp, although these phenomena are also affected
by other controllable or uncontrollable parameters such as
blast geometry and geological conditions (Douglas 1989;
Singh and Singh 2005). As a result, in many cases, em-
pirical methods are not accurate enough, while prediction
of the ground vibration and AOp with a high degree of
accuracy is important to estimate the blasting safety area.
In addition to the empirical equations, the use of sta-
tistical methods such as simple and multiple regression
& Masoud Monjezi
Mohsen Hajihassani
Danial Jahed Armaghani
Edy Tonnizam Mohamad
Aminaton Marto
1 Construction Research Alliance, Universiti Teknologi
Malaysia, UTM Skudai, 81310 Johor, Malaysia
2 Department of Geotechnics and Transportation, Faculty of
Civil Engineering, Universiti Teknologi Malaysia, UTM
Skudai, 81310 Johor, Malaysia
3 Department of Mining, Tarbiat Modares University,
14115-143 Tehran, Iran
123
Environ Earth Sci
DOI 10.1007/s12665-015-4274-1
techniques for ground vibration and AOp prediction has
received attention mainly due to their ease of use (Hu-
daverdi 2012). Apart from the statistical methods, the use
of soft computing techniques for prediction of ground vi-
bration and AOp recently has been highlighted in the lit-
erature (Khandelwal and Singh 2006; Monjezi et al. 2010,
2011; Mohamed 2011).
Artificial neural networks (ANNs), known as flexible
nonlinear function approximations, have been widely used
to analyze engineering problems. Although ANNs are able
to directly map input to output patterns and utilize all in-
fluential parameters in the prediction of ground vibration
and AOp, there are still some limitations: the slow rate of
learning and the possibility of becoming trapped in local
minima (Eberhart and Shi 1998; Eberhart et al. 1996; Tsou
and MacNish 2003; Hajihassani et al. 2014). To address
these limitations, the use of powerful optimization algo-
rithms such as particle swarm optimization (PSO) is ad-
vantageous for improving ANN performance (Kennedy
and Eberhart 1995; Adhikari and Agrawal 2011; Jahed
Armaghani et al. 2014; Momeni et al. 2015). This study
presents a novel approach founded on a hybrid PSO-based
ANN model to predict ground vibration and AOp induced
by blasting.
Blast vibrations
Blasting operations may cause excessive environmental
impacts such as ground vibration, AOp, dust, and flyrock
(Bhandari 1997). High blast vibrations can cause damage
to structures and nearby residential areas (Konya and
Walter 1990; Singh and Singh 2005). In the following
parts, effective parameters and relevant previous investi-
gations of these vibrations are discussed comprehensively.
Ground vibration
Ground vibration is a wave motion which travels away
from the blast to nearby areas (Khandelwal and Singh
2009). A considerable amount of energy is applied in every
ground vibration in which this energy is supposed to be
applied to rock fracturing. The problems caused by ground
vibration include large impacts on the structures, ground-
water, and ecology of the nearby area (Khandelwal and
Singh 2009; Duvall et al. 1963; Ghasemi et al. 2013).
When an explosive is detonated in a blasthole, the
chemical reaction of the explosives produces a high-pres-
sure and high-temperature gas. This gas pressure crushes
the rock adjacent to the blasthole. The detonation pressure
decays or dissipates quickly. A wave motion is created in
the ground by the strain waves conveyed to the surrounding
rocks (Duvall and Petkof 1959). Due to various breakage
mechanisms like radial cracking, crushing, and reflection
breakage in the free face, the strain energy carried by these
strain waves fragments the rock mass. During and after the
stress wave propagation, high-pressure high-temperature
gases extend radial cracks and any discontinuity, fracture,
or joint (Dowding 1985). The strain waves propagate as
elastic waves when the stress wave intensity diminishes to
the level where no permanent deformation occurs in the
rock mass (see Fig. 1). These waves are identified as
ground vibration. The ground vibration spreads from the
blasthole in all directions (Dowding 1985) and can cause
damage to buildings and other structures (Siskind et al.
1980).
Many parameters are involved in ground vibration such
as the blasting design, the distance between the free face
and the monitoring point, and geological conditions (Wiss
and Linehan 1978; Khandelwal and Singh 2006; Ghoraba
et al. 2015). It is essential to optimize the blasting design
parameters to decrease ground vibration based on the
properties of the rock mass, which include rock strength,
density, wave velocity, and discontinuity conditions (Singh
and Sastry 1986). The ground vibration can be measured in
terms of the peak particle velocity (PPV) and frequency. In
the Indian Standard Institute (Bureau of Indian Standard
1973) and German DIN Standard 4150 (New 1986), the
PPV is considered as a vibration index, which is a sig-
nificant indicator for controlling structural damage. PPV
due to ground vibration in surface blasting is a significant
parameter for the prediction of ground vibration. PPV
principally depends on two parameters: the maximum
charge used per delay and the distance from the free face
(Ozer et al. 2011; Basu and Sen 2005).
Soft computing techniques have been widely used by
several researchers to predict PPV. Singh and Singh (2005)
employed ANN and regression analysis to predict PPV. In
their study, the hole diameter, number of holes, hole depth,
burden, spacing, and the distances from the free face were
considered as input parameters. They demonstrated that
Fig. 1 Ground vibration due to blasting (Bhandari 1997)
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123
ANN is a more accurate approach compared to regression
analysis for predicting ground vibration. Khandelwal and
Singh (2006) investigated four widely used empirical pre-
dictors to estimate the PPV for 150 blast datasets and
compared the computed results with actual field data.
Subsequently, they developed an ANN with two inputs
(maximum charge per delay and distance from free face)
and one output (PPV). They found that ANN results are
more accurate compared to empirical predictors. Iphar
et al. (2008) utilized two different methods including
simple regression and the adaptive neuro-fuzzy inference
system (ANFIS) to predict PPV induced by blasting. They
used 44 PPV values obtained from blasting operations in
Turkey. Their results showed that the ANFIS model
yielded better results in comparison to regression analysis.
Khandelwal and Singh (2009) used ANN and multivariate
regression analysis (MVRA) techniques to predict PPV and
frequency by incorporating rock properties, blast design,
and explosive parameters. A total of 174 vibration records
were used to predict PPV and frequency with ten input
parameters. The ANN results indicated closer agreement
with the field datasets as compared to MVRA prediction.
An ANN model with four input parameters—maximum
charge per delay, distance from the free face to the
monitoring point, stemming length, and hole depth—was
developed by Monjezi et al. (2011) to predict PPV. A
database consisting of 182 datasets was collected at dif-
ferent strategic and vulnerable locations around the Kan-
dovan tunnel in Iran. They demonstrated that ANNs are
applicable tools to predict blast-induced ground vibration.
In addition, from the sensitivity analysis they found that the
distance from the free face has the most influence on PPV,
while stemming has the least. Fisne et al. (2011) utilized a
fuzzy logic approach and classical regression analysis to
predict PPV using 33 datasets obtained from the Akdaglar
quarry in Turkey. In their research, the charge weight and
distance from the free face were considered as input pa-
rameters to predict PPV. They concluded that the predicted
PPVs obtained from the fuzzy model were much closer to
the measured values in comparison to those predicted by
the statistical model. Monjezi et al. (2013) predicted PPV
using different empirical equations and the ANN tech-
nique. They compared the computed results with the actual
field data obtained from Shur River Dam in Iran. Total
charge, maximum charge per delay, and distance between
the shot point and the monitoring station were considered
as input parameters for the prediction of PPV. They found
that the ANN model was more accurate in comparison to
the empirical equations. In another study on PPV predic-
tion, Hajihassani et al. (2014) used a combination of an
imperialism competitive algorithm (ICA)-ANN to predict
PPV values obtained from Harapan Ramai granite quarry.
In their study, the burden-to-spacing ratio, stemming
length, maximum charge per delay, Young’s modulus,
p-wave velocity, and distance from the free face were
utilized as model inputs. They concluded that the ICA-
ANN approach can predict PPV with higher accuracy
compared to empirical equations.
Air overpressure
The explosion (blast) is produced by the shock wave of a
chemical reaction where the pressure of reactive gases
reaches sonic velocity (Baker et al. 1983). The gas pressure
velocity increases rapidly in the blasthole. Consequently,
the pressure in the blasthole suddenly loads the surrounding
rocks, which move away from the borehole. The pressure
in terms of blasting is mainly considered using shock and
gas mechanisms (Roy 2005).
AOp is created by a large wave from the explosion point
to the free surface. Hence, the AOp is a wave which is
refracted horizontally by density variations in the atmo-
sphere. AOp atmospheric pressure waves comprise an
audible high-frequency and a sub-audible low-frequency
sound. Generally, four important sources can cause AOp
waves in blasting operations: the air pressure pulse, which
results from displacement of the rock at the bench face as
the blast progresses; the rock pressure pulse, which is in-
duced by ground vibration; the gas release pulse, which
results from the escape of gases through rock fractures; and
the stemming release pulse, which results from the escape
of gases from the blasthole when the stemming is ejected
(Siskind et al. 1980; Wiss and Linehan 1978; Morhard
1987).
According to Kuzu et al. (2009), AOp is identified in
terms of sound and measured in decibels (dB) or pascals
(Pa), where 20 Hz is the lowest sound detectable by the
human ear. Hence, it is undeniable that there is a possibility
of concussion in the human ear with sound at more than
20 Hz. In addition, structural damage may occur at an AOp
level of 180 dB, general window breakage occurs at
171 dB, and occasional window breakage occurs at
151 dB (Kuzu et al. 2009). According to Siskind et al.
(1980), as reported by the United States Bureau of Mines
(USBM), a value of 134 dB is recommended for AOp
limitation. Therefore, many attempts have been made to
control AOp values (Kuzu et al. 2009; Rodriguez et al.
2010).
Several parameters affect AOp in blasting operations.
According to Khandelwal and Kankar (2011), blast ge-
ometry, explosive charge weight per delay, distance be-
tween the free face and the monitoring point, geological
discontinuities, blasting direction, surface topography, and
vegetation are the foremost parameters influencing AOp.
Konya and Walter (1990) found that AOp can be controlled
by the type and length of stemming materials. Their
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123
findings reveal that a stemming particle size of about 0.05
times the blasthole diameter provides the best confinement
and the materials have to be angular to function properly.
Moreover, other parameters affect AOp, such as over-
charging, weak strata, atmospheric conditions, and sec-
ondary blasting (Siskind et al. 1980; Griffiths et al. 1978;
Dowding 2000). However, AOp induced by blasting is not
easy to predict as the same blast design can produce dif-
ferent results in different cases.
Based on the parameters that influence AOp, many at-
tempts have been made to establish correlations for the
prediction of AOp induced by blasting. Rodrıguez et al.
(2007) developed a semi-empirical model for the predic-
tion of the air wave pressure outside a tunnel due to
blasting. This method was tested in several cases and it was
proven that it can be used under different conditions. Kuzu
et al. (2009) established a new empirical relationship be-
tween AOp and two other parameters (distance between
free face and monitoring point and maximum charge per
delay), which are the most important variables for AOp.
They used 98 AOp records from quarry blasting operations
under different conditions and demonstrated that the pro-
posed equation predicted AOp with reasonable accuracy.
Segarra et al. (2010) provided a new AOp prediction
equation based on monitoring data obtained from two
quarries. Blasting data and AOp measurements were ob-
tained from 122 records of 40 blasting operations in rocks
with low to very low strength. They concluded that the
accuracy of AOp prediction was 32 %. In addition, the
proposed model was validated using five new blasting data
with 22.6 % accuracy.
Apart from empirical equations, some soft computing
methods have been developed to predict AOp. Khandelwal
and Singh (2005) presented an ANN model to predict AOp
from two variables including distance (between free face
and monitoring point) and sound pressure level. They
compared the ANN results with the USBM predictor and
multivariate regression analysis (MVRA) results. The
comparison showed that ANN yielded better estimates
compared to USBM and MVRA predictors. Mohamed
(2011) predicted the AOp using a fuzzy inference system
and ANN using two parameters: maximum charge per
delay and distance from the free face to the monitoring
point. He compared the results with the values obtained by
regression analysis and observed field data, and concluded
that the ANN and fuzzy models gave more accurate pre-
diction compared to regression analysis. Khandelwal and
Kankar (2011) predicted AOp due to blasting using 75
datasets obtained from three mines by the support vector
machine (SVM) method. They compared the AOp values
predicted by SVM with the results of a generalized pre-
dictor equation. Using the maximum charge per delay and
distance from the free face to the monitoring station as
input parameters, they showed that the values of AOp
predicted by SVM were much closer to the actual values as
compared to the values predicted by the generalized pre-
dictor equation. Tonnizam Mohamad et al. (2012) used
ANN to predict AOp using 38 datasets obtained from
blasting operations. In their study, the hole diameter, hole
depth, spacing, burden, stemming, powder factor, and
number of rows were used as input parameters. Their re-
sults show the applicability of the proposed model to the
prediction of AOp.
PPV and AOp prediction methods
Estimating a safe zone for blasting operations is an im-
portant subject in the field of geotechnical engineering, and
prediction of blasting environmental impacts such as PPV
and AOp before blasting operations is always necessary.
Many attempts have been made to predict PPV and AOp
using empirical methods. In the following sections, a brief
review of these empirical methods is presented.
PPV prediction methods
Many researchers established empirical vibration equations
to predict PPV (Duvall and Petkof 1959; Bureau of Indian
Standard 1973; Langefors and Kihlstrom 1963; Davies
et al. 1964; Ghosh and Daemen 1983; Roy 1993). In most
of these equations, the maximum charge per delay and
distance from the free face are considered as the main in-
fluential parameters for PPV prediction. It is well known
that PPV is influenced by other factors such as blast ge-
ometry, rock strength, and discontinuity conditions which
have not been incorporated explicitly in any of the em-
pirical equations. So, different equations give different
PPV values for the same blasting operation and there is no
uniformity among the results predicted by different equa-
tions. Table 1 illustrates the PPV equations proposed by
different researchers.
AOp prediction methods
Some empirical equations have been suggested to predict
AOp. According to the National Association of Australian
State Road Authorities 1983, AOp from confined blasthole
charges can be estimated from the following empirical
formula:
P ¼140
ffiffiffiffiffiffi
E200
3
q
d; ð1Þ
where P is the AOp (kPa), E the mass of charge per delay
(kg), and d the distance from the free face (m). McKenzine
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123
(1990) recommended an equation to describe the decay of
AOp as follows:
dB ¼ 165� 24logðD=W1=3Þ; ð2Þ
where dB is the decibel reading with a linear of flat
weighting, D the distance between the free face and the
monitoring point (m), and W the explosive charge weight
per delay (kg).
Applying the cube root scaled distance factor (SD), in
the absence of monitoring equipment, is another method of
estimating the blast-induced AOp. The correlation between
explosive charge weight per delay, distance, and SD is
given as follows:
SD ¼ DW�0:33; ð3Þ
where D denotes the distance (m or ft), W the explosive
charge weight (kg or lb), and SD the scaled distance
(m kg-0.33 or ft lb-0.33).
Through the availability of sufficient data, establishment
of the relationship between the values of SD and AOp is
possible. A site-specific AOp attenuation formula can be
developed when statistical analysis techniques can practi-
cally represent AOp data (White and Farnfield 1993;
Rosenthal and Morlock 1987; Cengiz 2008). The predic-
tion equation is shown as follows:
AOp ¼ HðSDÞ�b; ð4Þ
where AOp is measured in pascals or decibels, H and b are
the site factors, and SD is the scaled distance factor as
given in Eq. (3). The scaled distance factor is widely used
in surface blasting to predict AOp (Kuzu et al. 2009;
Hustrulid 1999). The site factor values, H and b, for dif-
ferent blasting conditions are tabulated in Table 2.
Hybrid PSO-based ANN
Many researches have been conducted to improve the
performance and generalization capabilities of ANNs. Or-
dinary ANNs employ the backpropagation (BP) algorithm
in the learning process, which is a local search learning
algorithm; therefore, the learning process of ANNs might
cause the convergence of the solution to fail (Liou et al.
2009). Since PSO is a robust global search algorithm, it can
be used to adjust the weights and biases of an ANN to
increase the performance and accuracy. The following
sections describe the procedure of ANN and PSO in
minimization problems and the implementation of hybrid
PSO-based ANN models.
Artificial neural network
An artificial neural network (ANN) can be identified as a
simplified mathematical model of reasoning based on the
human brain. ANN is able to determine the complex re-
lationship among variables for the simulation of one (or
more) output(s) (Specht 1991). A specific ANN model can
be defined using three important components: the transfer
Table 1 Empirical PPV
predictor presented by different
researchers
References Equation Site constant for granite
USBM by Duvall and Petkof (1959) v = K[R/HQMax]-B K: 179.31, B: 1.09
Langefors and Kihlstrom (1963) v = K[H(QMax/R2/3)]B K: 44.43, B: -1.18
General predictor by Davies et al. (1964) v = KR-B(QMax)A K: 212.27, B: 1.09, A: 0.52
Bureau of Indian Standard (1973) v = K[(QMax/R2/3)]B K: 6.33, B: 0.22
Ghosh–Daemen predictor (1983) v = K[R/HQMax]-Be-aR K: 780.36, B: 1.26, a: 0.0004
CMRI by Roy (1993) v = n ? K[R/HQMax]-1 K: 168.91, n: 1.57
v peak particle velocity (mm/s), Qmax maximum charge per delay (kg), R distance between blast face and
vibration monitoring point (m), (K, B, A, a, n) site constants
Table 2 Site factors H and bfor different blasting conditions
References Description H b
Siskind et al. (1980) Quarry blasts, behind face 622 0.515
Quarry blasts, direction of initiation 19,010 1.12
Quarry blasts, front of face 22,182 0.966
Hopler (1998) Confined blasts for AOp suppression 1906 1.1
Blasts with average burial of the charge 19,062 1.1
Hustrulid (1999) Detonations in air 185,000 1.2
Kuzu et al. (2009) Quarry blasts in competent rocks 261.54 0.706
Quarry blasts in weak rocks 1833.8 0.981
Overburden removal 21,014 1.404
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function, network architecture, and learning rule (Simpson
1990). Based on the type of problem, these components
need to be defined as an initial set of weights and display
how weights should be modified during training to increase
the performance (Monjezi and Dehghani 2008). The mul-
tilayer perceptron (MLP) is one of the most well-known
feedforward neural network models and typically contains
an input layer of source neurons, at least one hidden layer
of computational neurons, and one output layer. Each of
these layers has its own specific function. The input layer
accepts inputs from the outside world and distributes them
to the subsequent layers. Features hidden in the input
patterns are detected by the neurons in the hidden layer.
The output layer exploits these features to determine the
output pattern (Bounds et al. 1998).
Several algorithms have been recommended for the
training of neural networks. The BP is the most popular
learning method among a vast number of MLP learning
algorithms (Basheer and Hajmeer 2000). In the BP method,
the input data are presented to the input layer to be
propagated through the network until an output is gener-
ated. Each neuron determines its net weighted input using
the following equation:
X ¼X
n
i¼1
xi wi�h; ð5Þ
where n is the number of inputs, and xi and wi denote the
values of the ith input and weight, respectively. The
threshold applied to the neurons is denoted by h. This input
value passes through one of the activation functions such as
a sigmoid, step, or linear function. Such a procedure is
technically known as a learning or training procedure. The
network computes its actual outputs, its weights, and a
mathematical function model threshold. Afterward, the
actual output is compared to the historical outputs to cal-
culate the output error (Rafiai and Jafari 2011). The ob-
tained error is propagated back through the network and
updates the individual weights. This process is called the
backward pass. This procedure is repeated until the error
reaches a defined level such as the mean square error
(MSE) (Simpson 1990; Kosko 1994). However, for train-
ing an ANN model, an experimental database requires an
appropriate number of datasets (Dreyfus 2005).
Particle swarm optimization
The particle swarm optimization (PSO) algorithm
originated from the social behavior of organisms (indi-
viduals) in swarms like flocks of birds (Kennedy and
Eberhart 1995). PSO is an evolutionary population-based
optimization technique that can be used to solve global
optimization problems within a nonlinear procedure. In
PSO algorithm, each particle denotes candidates’ solution
to the optimization problem. In this algorithm, particles
flow throughout the multidimensional search space to find
the best solution. Therefore, in each optimization problem,
several particles should be produced and scattered in the
search space. The particles change their positions in the
search space based on their experiences and those of
neighboring particles, and therefore the particles make use
of their own experience and those of their neighbors (En-
gelbrecht 2007). These particles form a population which is
technically known as a swarm.
Finding the best solution using PSO starts with initial-
ization of random particles (solutions) which are assigned
random positions and velocities. Subsequently, the algo-
rithm searches for the best solution through an iterative
procedure (Eberhart and Shi 2001). In the process each
particle keeps track of its best position, known as its per-
sonal best (pbest), as well as the overall best value accom-
plished by other particles in the swarm, known as the
global best (gbest).
Through the learning process, a particle’s journey to-
ward both the pbest and the gbest position is speeded up by
calculating a new velocity. The new velocity is calculated
in terms of the particle’s distance from the pbest and gbest
positions, which will affect the particle’s next position in
the next iteration.
A relatively simple procedure is required to obtain the
optimized solution using PSO as compared to the other
optimization algorithms (Van den Bergh and Engelbrecht
2000). In fact, PSO operates based on two simple equations
(Eqs. 6 and 7) for updating the particles’ velocities and
positions. To increase the convergence rate of the algo-
rithm, an inertia weight can be used in the original equa-
tions (Shi and Eberhart 1998), as in Eq. (6). The inertia
weight determines the rate of contribution of a particle’s
previous velocity to its current velocity:
vnew��! ¼ w � v~þ r1C1 � pbest
��!� p~� �
þ r2C2 � gbest��!� p~� �
;
ð6Þ
pnew��! ¼ p~þ vnew
��! ð7Þ
where vnew��! is the new velocity and w is the inertia weight.
v~, pnew��!, and p~ are the current velocity, new position, and
current position of particles, respectively, C1 and C2 are
acceleration constants, pbest��! is the personal best position of
the particle, gbest��! is the globally best position among all
particles, and r1 and r2 are random values in the range (0,
1) sampled from a uniform distribution.
PSO-based ANN algorithm
Many attempts have been made to improve the ANN per-
formance by means of optimization algorithms, due to the
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123
fact that an optimum search process of conventional ANNs
might fail and return an unsatisfactory solution (Liou et al.
2009; Engelbrecht 2007). Several studies have been con-
ducted to investigate the ability of PSO as a training al-
gorithm for a number of different ANN architectures.
Eberhart and Kennedy (1995) presented the first results of
utilizing the basic PSO to train ANNs, whereas several
scholars have further demonstrated the capability of PSO in
training ANNs and showed that the PSO is an effective
alternative for training ANNs (Mendes et al. 2002; Settles
and Rylander 2002; Gudise and Venayagamoorthy 2003).
It should be noted that further attempts have been made to
employ other optimization techniques in training ANNs,
for example, genetic algorithm and ant colony optimization
techniques (Montana and Davis 1989; Socha and Blum
2007). Nevertheless, it has been proven that ANNs trained
by PSO provide more accurate results compared to other
learning algorithms (Engelbrecht 2007).
In ANN training, a set of weights and biases are deter-
mined which minimize an objective function such as MSE.
So, MSE can be used as the fitness function in training an
ANN using PSO, due to the fact that a fitness function is
required to generate a PSO-based ANN model.
In a minimization problem, there is one global minimum
and a number of local minima. ANN searches for a solution
in the local region due to its inherent property and therefore
usually gets trapped in a local minimum. PSO has a com-
petent capability to search the entire search space to find the
global minimum and continues searching around it. Hence,
a hybrid PSO-based ANN model has the search properties
of both PSO and ANN, where PSO looks for the global
minimum in the search space and ANN uses the global
minimum to find the best results (Gordan et al. 2015).
The learning process in a PSO-based ANN model is
initialized by generating a group of random particles in
which each particle represents a set of weights and biases
in the model. The PSO-based ANN model is trained using
the initial weights and biases (i.e., initial position of par-
ticles), the particle’s velocity and position are updated
using the PSO equations, and subsequently, in each it-
eration, the weights and biases of the model are adjusted. In
each iteration, the MSEs between the actual and predicted
values are calculated and the errors are reduced by
changing the positions of the particles. This process is
continued to find the best weights and biases for an ANN to
minimize the error function.
Case study and data collection
This study was conducted at Hulu Langat quarry site in
Selangor State, Malaysia. Geographically, the quarry lies at
a latitude of 3�70000N and a longitude of 101�490100E and is
located in the south of Selangor. An overall view of the
Hulu Langat quarry site is shown in Fig. 2. This quarry is
composed of granitic rocks with the capacity to produce
large amounts (between 280,000 and 360,000 tons per
month) of aggregate. Blasting is carried out 10–12 times
per month, depending on the weather conditions. All
blasting operations are conducted using blastholes 89 mm
in diameter. Ammonium nitrate and fuel oil (ANFO) and
dynamite were used as the main explosive material and for
initiation, respectively. The blastholes were stemmed using
fine gravels.
During 9 months from August 2012 to April 2013, 88
datasets were collected. During data collection, blasting
parameters including hole depth, maximum charge per
delay, burden, spacing, stemming length, sub-drilling,
powder factor, and number of holes were obtained. In each
blast, PPV and AOp values were recorded using a Vi-
braZEB seismograph. In the case of AOp, the values were
monitored during each blasting operation using linear L
type microphones connected to the AOp channels. A range
of AOp values from 88 dB (7.25 9 10-5 psi or 0.5 Pa) to
148 dB (0.0725 psi or 500 Pa) can be recorded by Vi-
braZEB. The microphones have an operating frequency
response from 2 to 250 Hz, which is adequate for mea-
suring AOp accurately in the frequency range critical for
structures and human hearing. All AOp and PPV values
were recorded in front of the quarry bench and ap-
proximately perpendicular to it.
The crushing plant and workshops are located about
400 m to the southwest of the quarry face, while the
nearest residential area is about 800 m to the west of the
quarry face. Therefore, the distance between the monitor-
ing point and free face was set as 300, 600, and 700 m.
Figure 3 shows the location of the quarry site and nearest
residential area.
The use of the SD is a common technique to predict PPV
and AOp values resulting from blasting. The relationships
between the SD and the two parameters of distance and
explosive charge weight per delay are formulated as fol-
lows for PPV prediction (Duvall and Petkof 1959):
SD ¼ DW�0:5; ð8Þ
where W is the maximum charge per delay (kg) and D
represents the distance between the monitoring point and
free face (m). The correlation of maximum charge weight
per delay, distance, and SD is given in Eq. (3) for AOp
prediction. Afterward, PPV and AOp values can be deter-
mined using the USBM-suggested equation as follows:
PPV=AOp ¼ KðSDÞB; ð9Þ
where B and K are site constants. The graphs of the mea-
sured PPV and AOp values against their SDs are shown in
Figs. 4 and 5, respectively. In addition, two empirical
Environ Earth Sci
123
equations were proposed for the prediction of PPV and
AOp values as indicated in these figures. Coefficient of
determination R2 values equal to 0.581 and 0.410 for PPV
and AOp prediction suggest that the proposed equations
can predict them with good accuracy level.
Development of PSO-based ANN model for PPVand AOp prediction
A MatLab code was developed to predict PPV and AOp
using a hybrid PSO-based ANN model. ANNs work based
on given data and do not have previous knowledge about
the subject of prediction. Therefore, to predict the PPV and
AOp induced by blasting, all relevant parameters should be
determined. The following sections describe the develop-
ment procedure of a PSO-based ANN model to predict the
PPV and AOp induced by blasting.
Input and output parameters
Determining the input parameters is the first step of de-
veloping a prediction model for PPV and AOp. To develop
a comprehensive and accurate model, the parameters with
the greatest influence on PPV and AOp should be deter-
mined. In determining the influential parameters, it should
be considered that the selected parameters must represent
the site conditions as well as the blast design parameters,
Fig. 2 A view of the Hulu
Langat granite quarry site
Fig. 3 Location of the quarry site and the nearest residential areas
Environ Earth Sci
123
must be measurable, and must be easy to obtain
concurrently.
A direct relationship exists between the blast design
parameters and the PPV and AOp values in blasting op-
erations. Therefore, the blast design parameters including
hole depth, maximum charge per delay, burden-to-spacing
ratio, stemming length, sub-drilling, powder factor, and
number of holes were taken into account in modeling. The
values of PPV and AOp may increase if the design of these
parameters is carried out improperly.
Geological discontinuities, in addition to the aforemen-
tioned parameters, have a significant impact on PPV and
AOp in blasting operations. In the presence of geological
discontinuities, explosive gases escape intensely from the
discontinuities, leading to high vibration magnitudes.
Therefore, as a degree of jointing or fracturing in a rock
mass, the rock-quality designation (RQD) was used in
modeling to represent the geological discontinuities.
It is obvious that the PPV and AOp values decrease as
the distance between the free face and the monitoring point
increases. Therefore, as an influential parameter, this pa-
rameter was used in modeling. Table 3 shows the input and
output parameters and their ranges. The modeling proce-
dure was started by normalization of the input and output
data. It is recommended that the input and output data be
normalized before they are presented to the network. Ac-
cording to Rafig et al. (2001), normalization helps to im-
prove the learning speed of the network. By using the
following equation, the data were normalized into a range
of -1 to 1.
xN ¼ x�Min x
Max x�Min x
� �
�2� 1; ð10Þ
where xN is the normalized value of the variable x, and Min
x and Max x are the minimum and maximum values, re-
spectively, for the variable x.
Fig. 4 Relationship between
scaled distance and PPV values
Fig. 5 Relationship between
scaled distance and AOp values
Environ Earth Sci
123
Network design
A PSO-based ANN model performs best when its pa-
rameters are selected properly. The PSO parameters (in-
cluding number of particles, acceleration constants for
gbest (C1) and pbest (C2), and inertia weight) as well as the
ANN parameters (network architecture including the
number of hidden layers and the number of nodes in a
hidden layer) are related to the performance of the PSO-
based ANN model. Therefore, many computations were
conducted to determine the optimal configuration of the
PSO-based ANN model. A series of sensitivity analyses
was conducted to find the optimum PSO parameters.
Subsequently, the optimum network architecture was de-
termined using the trial and error method as well as the K-
fold cross-validation technique.
PSO parameters
A MatLab code was developed to perform the sensitivity
analyses. These analyses consist of several independent
steps to determine the optimum number of particles, ac-
celeration constants, and inertia weight. The performance
of PSO-based ANN models in minimizing the MSE was
evaluated during the sensitivity analyses.
As an initial model, a PSO-based ANN model consisting
of a single hidden layer with nine nodes was used. For each
analysis, 80 % of the data was assigned for training while
the remaining 20 % was used for testing. Each analysis was
conducted three times and the best value was selected as
the representative value of the model.
To obtain the appropriate number of particles in the
swarm (swarm size), a series of sensitivity analyses was
applied to the PSO swarm size because no other method of
finding the optimum swarm size exists. While a small
swarm may fail to converge to a global solution, a large
swarm may lead to delay in the convergence and decrease
the efficiency. The analyses were performed by setting a
fixed iteration number of 1000 for each model with various
numbers of particles and a fixed value of 2 for both ac-
celeration constants, C1 and C2. R2 and MSE are the model
selection criteria. Figure 6 illustrates the results of the
sensitivity analyses for the number of particles.
According to Fig. 6a, in general, the values of R2 have
been increased by increasing the number of particles.
However, after a significant increase in the values of R2
Table 3 Input and output
parameters used in the
prediction model
Parameter Category Unit Symbol Minimum Maximum Average St. deviation
Hole depth Input (m) A 10 17 14.4 2.28
Charge per delay Input (kg) B 56.3 101.6 84.5 14.27
Burden to spacing Input – C 0.7 0.92 0.8 0.06
Stemming length Input (m) D 1.9 3.6 2.7 0.43
Sub-drilling Input (cm) E 25 45 35.3 6.71
Powder factor Input (kg/m3) F 0.4 1.18 0.9 0.22
RQD Input (%) G 41 77 59.4 10.81
Distancea Input (m) H 300 700 553.3 129.46
Number of holes Input – I 15 63 38.1 11.01
Peak particle velocity Output (mm/s) PPV 1.1 9.5 3.5 2.21
Air overpressure Output (dB) AOp 90 127 105.4 10.49
a Distance between free face and monitoring point
Fig. 6 a R2 for models with different swarm size, b MSE for models
with different swarm sizes
Environ Earth Sci
123
when the number of particles increased from 10 to 250, the
network performance does not improve afterward. The
same results were obtained between the number of parti-
cles and the values of MSE, as shown in Fig. 6b. Mean-
while, the training time was gradually increased by
increasing the number of particles, as shown in Fig. 7. As
a result, a swarm size of 250 was selected as the optimum
number of particles to avoid ineffectual iterations in the
models.
The next sensitivity analyses were conducted to deter-
mine the optimum values of the acceleration constants, C1
and C2. A series of candidate combinations were used
based on the original (Kennedy and Eberhart 1995) and
modified (Clerc and Kennedy 2002) acceleration constants,
as shown in Table 4. The analyses were conducted using
the obtained optimum swarm size of 250 on the initial
network, including a single hidden layer with nine nodes.
The results of the sensitivity analyses are shown in Table 4.
Superior results were obtained by model number 3 com-
pared to other models, as it had the highest values of R2 and
the lowest values of MSE for the training and testing
datasets. As a result, the values of 1.714 and 2.286 were
selected for acceleration constants C1 and C2 to be used in
the prediction of PPV and AOp.
The next step in designing an optimum network is
finding an appropriate inertia weight. Based on the inertia
weight suggested in previous studies (Shi and Eberhart
1998; Clerc and Kennedy 2002), four tests with different
inertia weights were designed to find the optimum value of
the inertia weight in the PSO equation. The same initial
swarm with a size of 250 was applied in all the tests, and
the acceleration constants were set at the previously de-
fined optimum values of 1.714 and 2.286 for C1 and C2,
respectively. Figures 8 and 9 show the values of R2 and
MSE for the training and testing datasets at different inertia
weights. According to these figures, the highest R2 and the
lowest MSE for the training and testing datasets were ob-
tained at an inertia weight of 0.5. Hence, this value was
selected as the optimal inertia weight.
Network architecture
PSO can only adjust the weights and biases of a model to
minimize the learning error. Therefore, the network ar-
chitecture, composed of the number of hidden layer(s) and
the number of nodes in each hidden layer, should be de-
termined through the trial and error method as a conse-
quence of the fact that there is no absolute method of
determining the optimum network architecture.
Following the determination of the PSO parameters, the
optimal network architecture was obtained. This was done
through the trial and error method. A K-fold cross-valida-
tion technique (Diamantidis et al. 2000) was employed to
evaluate the performance of each model. In this technique,
the data are divided into K parts, of which K - 1 parts are
used for training and one part is used to test the model. The
process is repeated K times and therefore all of the data are
used in the training and testing steps.
To determine the optimal network architecture, 14
hybrid models were considered and a fivefold cross-
validation was used to evaluate the performance of the
models. Each model was trained with fourfold cross-
Fig. 7 Training consumed time for models with different swarm
sizes
Table 4 The results of
sensitivity analyses for
acceleration constants
C1 and C2
Model Relationship C1 C2 C1 ? C2 Training Testing
R2 MSE R2 MSE
1 C1 = 0.25 C2 0.8 3.2 4 0.73 0.141 0.66 0.195
2 C1 = 0.5 C2 1.333 2.667 4 0.85 0.085 0.83 0.086
3 C1 = 0.75 C2 1.714 2.286 4 0.89 0.056 0.88 0.072
4 C2 = 0.25 C1 3.2 0.8 4 0.70 0.155 0.70 0.150
5 C2 = 0.5 C1 2.667 1.333 4 0.72 0.180 0.72 0.142
6 C2 = 0.75 C1 2.286 1.714 4 0.81 0.112 0.80 0.092
8 C1 = C2 2 2 4 0.79 0.120 0.77 0.112
9 C1 = C2 1.75 1.75 3.5 0.75 0.122 0.74 0.124
10 C1 = C2 1.5 1.5 3 0.67 0.201 0.66 0.159
Environ Earth Sci
123
validation (70 datasets) and tested with the rest of the data
(18 datasets). Therefore, each model was trained and
tested five times with different combinations of training
and testing datasets. The values of Rave2 and MSEave for
the testing datasets were considered as the model per-
formance criteria.
The processes were conducted with different numbers of
hidden layers in the networks and various numbers of
nodes in each hidden layer. Hidden layers of one and two
layers were considered to find the optimum number of
hidden layer(s) and 6, 9, 12, 15, 18, 21, and 24 nodes were
considered to find the optimum number of nodes in each
hidden layer. All models were trained with the optimized
PSO parameters obtained in previous analyses. The results
of the analyses are tabulated in Table 5.
Figures 10 and 11 display the values of Rave2 and MSEave
for trained models with different architectures. According
to the figures, model number 3 presents the best perfor-
mance in terms of values of Rave2 and MSEave for the testing
datasets among all models: 0.89 for Rave2 and 0.038 for
MSEave. Therefore, the architecture of model number 3
was selected as the optimum architecture. The structure of
the selected PSO-based ANN model consisting of one
hidden layer and 12 nodes in the hidden layer is illustrated
in Fig. 12.
Analysis of the results
A graphical comparison between the measured and pre-
dicted values of PPV and AOp employing different
training datasets is shown in Fig. 13. According to the
figure, a superior concordance exists between the mea-
sured and predicted values of PPV and AOp. This is
because of the capability of PSO to minimize the error
Fig. 8 R2 for training and testing datasets at different inertia weights
Fig. 9 MSE for training and testing datasets at different inertia
weights
Table 5 Performance of trained PSO-based ANN models
Model Network architecture Train Test
hidden layers Nodes in hidden layers R2 MSE R2 MSE
Min Max Ave Min Max Ave Min Max Ave Min Max Ave
1 1 6 0.70 0.82 0.76 0.133 0.167 0.146 0.66 0.82 0.74 0.112 0.166 0.143
2 1 9 0.81 0.89 0.85 0.056 0.130 0.097 0.80 0.88 0.84 0.072 0.090 0.088
3 1 12 0.80 0.95 0.89 0.014 0.060 0.034 0.85 0.94 0.89 0.029 0.050 0.038
4 1 15 0.82 0.95 0.86 0.033 0.110 0.089 0.84 0.92 0.88 0.065 0.104 0.071
5 1 18 0.83 0.91 0.86 0.056 0.115 0.090 0.87 0.90 0.89 0.052 0.110 0.073
6 1 21 0.79 0.86 0.83 0.085 0.128 0.104 0.75 0.85 0.81 0.095 0.137 0.114
7 1 24 0.73 0.83 0.78 0.106 0.160 0.136 0.72 0.80 0.75 0.125 0.146 0.136
8 2 6 0.68 0.80 0.73 0.141 0.167 0.141 0.64 0.75 0.68 0.157 0.203 0.169
9 2 9 0.67 0.80 0.75 0.090 0.188 0.143 0.72 0.80 0.75 0.111 0.135 0.122
10 2 12 0.82 0.87 0.85 0.069 0.103 0.089 0.75 0.85 0.80 0.081 0.152 0.121
11 2 15 0.73 0.82 0.78 0.109 0.162 0.132 0.75 0.81 0.78 0.094 0.148 0.118
12 2 18 0.66 0.75 0.71 0.098 0.207 0.146 0.69 0.79 0.72 0.120 0.212 0.167
13 2 21 0.67 0.78 0.72 0.118 0.162 0.145 0.63 0.76 0.70 0.139 0.178 0.158
14 2 24 0.65 0.72 0.70 0.148 0.179 0.164 0.68 0.71 0.69 0.165 0.176 0.168
Environ Earth Sci
123
function with high efficiency; the PSO algorithm adjusts
the weights and biases of the error objective function of
ANN to obtain the minimum MSE. The same results were
obtained for the testing datasets, as can be seen in
Fig. 14. As shown in the figure, the predicted values of
PPV and AOp obtained by employing the proposed PSO-
based ANN model are in close agreement with the mea-
sured values. It is worth noting that the results of the
optimum PSO-based ANN model presented in these fig-
ures are the best obtained by model no. 3, whereas the
results tabulated in Table 5 are the average values of five
repeated runs.
PPV and AOp prediction through PSO-based ANNmodel and empirical approaches
A comparison was conducted between the values of PPV
and AOP predicted by the proposed PSO-based ANN
model, empirical approaches, and measured values to
check the accuracy of the proposed model. For this pur-
pose, ten datasets were selected in terms of the distance
between the free face and the monitoring point, rock
properties, and blasting parameters, as listed in Table 6.
Figure 15 shows a comparison between the measured
PPV and predicted PPV values obtained by empirical
Fig. 10 Rave2 for trained PSO-based ANN models
Fig. 11 MSEave for trained
PSO-based ANN models
Environ Earth Sci
123
Fig. 12 Structure of the
selected PSO-based ANN model
to predict PPV and AOp
induced by blasting
Fig. 13 Comparison between measured and predicted a PPV and b AOp for different training datasets
Environ Earth Sci
123
approaches (see Table 1) and the proposed PSO-based
ANN model. It can be seen that the PPV values obtained
by the PSO-based ANN model are in very good agree-
ment with the measured values, whereas there are wide
variations in PPV values predicted by empirical methods.
For the selected datasets, AOp values were obtained by
means of empirical approaches (Eqs. 1, 2, and 4). Based on
the site conditions, three different sets of site factors
(H = 22,182 and b = 0.966; H = 19,062 and b = 1.1;
H = 261.54 and b = 0.706) were extracted from Table 2.
Fig. 14 Comparison between measured and predicted a PPV and b AOp for different testing datasets
Table 6 Investigated parameters to estimate PPV and AOp by PSO-based ANN model and empirical approaches
No. Hole
depth (m)
Charge per
delay (kg)
Burden to
spacing
Stemming
length (m)
Sub-drilling
(cm)
Powder factor
(kg/m3)
RQD
(%)
Distance
(m)
No. of
holes
2
10
57.8 0.79 2 25 0.42 76 600 43
9
10.8
64.2 0.81 1.9 30 0.67 70 700 24
14
11.3
60.4 0.82 2.9 30 0.56 76 700 41
26
13.3
77.0 0.81 2.7 28 0.83 61 600 56
39
14.9
85.6 0.70 3.1 33 1.01 76 600 39
45
15.2
87.7 0.81 3.1 35 0.99 57 600 37
49
15.5
90.3 0.81 3 40 1.04 56 600 39
70
16.5
94.8 0.78 3.4 40 1.16 49 600 35
78
16.6
97.5 0.80 3.1 43 1.04 69 300 42
85
17
98.9 0.75 3.3 44 1.17 48 300 39
Environ Earth Sci
123
Subsequently, a comparison was made between the mea-
sured AOp values and predicted AOp values obtained by
the PSO-based ANN model and empirical approaches, as
shown in Fig. 16. According to the figure, the proposed
PSO-based ANN model yields more accurate results
compared to empirical approaches. Based on Figs. 15 and
16, it can be concluded that the proposed PSO-based ANN
model is an applicable tool for the prediction of PPV and
AOp induced by blasting of this quarry with a high degree
of accuracy.
Fig. 15 Comparison of PPV for selected datasets
Fig. 16 Comparison of AOp for selected datasets
Environ Earth Sci
123
Sensitivity analysis
A sensitivity analysis was carried out to identify the rela-
tive influence of each parameter in the neural network
system by the cosine amplitude method (Yang and Zang
1997). To apply this method, all data pairs were expressed
in common X-space. The data pairs used to construct a data
array X are defined as:
X ¼ x1; x2; x3. . .; xi; . . .; xnf g:
The elements xi in the array X are a vector of length m, that
is:
xi ¼ xi1; xi2; xi3. . .; ximf g:
Each of these data pairs can be trained as a point in m-
dimensional space, where each point requires m-coordi-
nates for a full description. Thus, in the space pair, all the
points are associated with the achieved results. The fol-
lowing equation illustrates the strength of the relation (rij)
between the dataset Xi and Xj:
rij ¼Pm
k¼1 xikxjkffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Pmk¼1 x2
ik
Pmk¼1 x2
jk
q : ð11Þ
Table 7 shows the strengths of the relations (rij values)
between the input and output (PPV and AOp) parameters.
The sensitivity analysis results show that sub-drilling
(E) and maximum charge per delay (B) are the parameters
with the greatest influence on PPV, whereas stemming
length (D) and maximum charge per delay (B) are the
parameters with the greatest influence on AOp.
Conclusion
A MatLab code was developed to predict blast-induced
PPV and AOp using a hybrid PSO-based ANN model.
Eighty-eight datasets collected from Hulu Langat granite
quarry site in Malaysia were used to develop an optimum
PSO-based ANN model. Hole depth, maximum charge per
delay, burden-to-spacing ratio, stemming length, sub-dril-
ling, powder factor, RQD, distance between the free face
and the monitoring point, and number of holes were used as
input parameters, while PPV and AOp values were set as
output parameters. A series of sensitivity analyses were
conducted to determine the optimum PSO parameters. The
optimum network architecture was determined following
the trial and error method. Finally, a model with one hidden
layer and 12 nodes in the hidden layer was selected to be
used for prediction. A comparison was made between the
results obtained by the PSO-based ANN model and em-
pirical predictors as well as the measured values to examine
the applicability and accuracy of the proposed model. The
results indicate that the proposed PSO-based ANN model is
practically able to predict PPV and AOp induced by blast-
ing in granite quarry sites with similar conditions. Through
the sensitivity analyses, it was also found that the sub-
drilling and maximum charge per delay are the parameters
with the greatest influence on PPV, whereas the stemming
length and maximum charge per delay are the parameters
with the greatest influence on AOp.
Acknowledgments The authors would like to extend their appre-
ciation to the Universiti Teknologi Malaysia for UTM Research
University Grant No. 01H88 and for providing the required facilities
that made this research possible.
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