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Environmental Earth Sciences

ISSN 1866-6280

Volume 72

Number 10

Environ Earth Sci (2014) 72:3915-3928

DOI 10.1007/s12665-014-3280-z

Environmental impact of blasting atDrenovac limestone quarry (Serbia)

Dejan Vasovi, Sran Kosti, Marina

Ravili & Slobodan Trajkovi

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O R I G I N A L A R T I C L E

Environmental impact of blasting at Drenovac limestone quarry(Serbia)

Dejan Vasovic Srdan Kostic Marina Ravilic

Slobodan Trajkovic

Received: 30 September 2013/ Accepted: 8 April 2014 / Published online: 8 June 2014

Springer-Verlag Berlin Heidelberg 2014

Abstract In present paper, the blast-induced ground

motion and its effect on the neighboring structures areanalyzed at the limestone quarry Drenovac in central

part of Serbia. Ground motion is examined by means of

existing conventional predictors, with scaled distance as a

main influential parameter, which gave satisfying predic-

tion accuracy (R[ 0.8), except in the case of Ambraseys

Hendron predictor. In the next step of the analysis, a feed-

forward three-layer back-propagation neural network is

developed, with three input units (total charge, maximum

charge per delay and distance from explosive charge to

monitoring point) and only one output unit (peak particle

velocity). The network is tested for the cases with different

number of hidden nodes. The obtained results indicate that

the model with six hidden nodes gives reasonable predic-

tive precision (R & 0.9), but with much lower values of

mean-squared error in comparison to conventional predic-

tors. In order to predict the influence level to the neigh-

boring buildings, recorded peak particle velocities and

frequency values were evaluated according to United

States Bureau of Mines, USSR standard, German

DIN4150, Australian standard, Indian DMGS circular 7

and Chinese safety regulations for blasting. Using the best

conventional predictor, the relationship between the

allowable amount of explosive and distance from explosivecharge is determined for every vibration standard. Fur-

thermore, the effect of air-blast overpressure is analyzed

according to domestic regulations, with construction of a

blasting chart for the permissible amount of explosive as a

function of distance, for the allowable value of air-blast

overpressure (200 Pa). The performed analysis indicates

only small number of recordings above the upper allowable

limit according to DIN4150 and DMGS standard, while,

for all other vibration codes the registered values of ground

velocity are within the permissible limits. As for the air-

blast overpressure, no damage is expected to occur.

Keywords Blasting Peak particle velocity Artificialneural network Frequency Building vibration

Introduction

Blasting is a commonly performed excavation technique in

various mining and civil engineering projects, for the

purpose of tunnel, subway, highways or dam construction.

These activities, usually performed both on surface and

underground, often induce ground motion and air blast

which could affect the existing nearby buildings and

infrastructure. According to Kuzu (2008), only 2030 % of

the energy from blasting is used to fragment the rock.

Moreover, with the increase of legal environmental con-

straints on the level of allowable environmental distur-

bances induced by blasting operations, there is an

increasing need to design blasting with greater precision.

On the other hand, when it comes to quarrying, blasting

operations must be carried out to provide such production

that overall profits of quarrying operation are maximized.

D. Vasovic

Department of Architectural Technologies, Faculty of

Architecture, University of Belgrade, Belgrade, Serbia

S. Kostic (&)

Department of Geology, Faculty of Mining and Geology,

University of Belgrade, Belgrade, Serbia

e-mail: [email protected]

M. Ravilic S. TrajkovicDepartment of Underground Mining, Faculty of Mining and

Geology, University of Belgrade, Belgrade, Serbia

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Even though it seems that these two requirements are

mutually opposed, blasting should be designed in a way

that no ground motion and, simultaneously, no structural

vibrations are recorded above a certain threshold level. At

the same time, the effect of air blast should be reduced to

minimum.

There are two main groups of parameters that affect

ground vibrations induced by blasting (Hudaverdi 2012).The first group consists of blast design parametersbur-

den, spacing between holes, bench height, drillhole diam-

eter, stemming height and subdrilling. The second group of

parameters is represented by rock properties. If the layer is

composed of several different soil types, the transmission

path of blasting vibration would be very complicated

because of the wave reflection and refraction (Kim and Lee

2000). On the other hand, if the waves induced by blasting

propagate through hard rock, the existing discontinuities in

a rock mass could cause significant spatial variations in

blast-induced ground motions due to uncontrollable phys-

ical conditions and their effect on fragmentation mecha-nism (Erarslan et al. 2010). As it could be seen, ground

motion shows large spatial variation primarily due to

geological setting, which is commonly treated as an

uncontrollable parameter, in comparison to amount of

explosive, distribution of blasting boreholes and their

depth, which are usually set up according to design

demands. This is why ground motion due to blasting is

usually evaluated using empirical attenuation equations,

which estimate peak particle velocity (PPV) based on the

quantity of explosive used and distance between the

explosive charge and monitoring station. These equations

are of great interests for field engineers, since they enable

them to predict the maximum ground vibration (Duvall and

Petkof1959; Langefors and Kihlstrom1963; Davies et al.

1964; Ambraseys and Hendron1968; Ghosh and Daemen

1983; Singh and Roy1993).

As a rule, blast-induced ground vibrations are accom-

panied by air-blast overpressure, which refers to the pres-

sures above normal atmospheric pressure, generated by

detonation of explosive charges. These air vibrations usu-

ally have low frequency (\20 Hz), which is similar to

natural frequencies of many residential structures, so air

blast could cause resonance effect, and, consequently, a

possible structural damage (Olofsson1990; Bhandari1997;

Kuzu and Ergin 2005).

Both ground motion and air blast could affect nearby

structures. Different countries have set their own standards

on the basis of their extensive field investigations carried

out in their mines for several years. Peak particle velocity

has been traditionally used in practice for the measurement

of blast damage to structures. In this criterion, the shape of

the waveform and duration of dynamic loading are not

taken into account (Langefors et al. 1958; Edwards and

Northwood 1960; Duvall and Fogelson 1962; Nicholls

et al.1971; Singh and Vogt1998). Later on, both PPV and

predominant frequency of the seismic wave were used as a

damage criterion (Siskind et al.1980a; German Institute of

Standards 1986; DGMS (Tech) S and T Circular No. 7

1997; Australian standard2006; USSR standard, Singh and

Roy2010; Lu et al. 2012). Sometimes even PPV and dis-

tance from the explosive charge to the endangered structureis also used as a damage criterion (Rosenthal and Morlock

1987). All these standards give the threshold level of PPV

for various types of structures, like hospitals, residential

buildings, office and commercial areas (Singh and Roy

2010).

As for the air-blast effects, there are two risks which air

blasts pose including direct human irritation, and pressure

causing damage on neighboring structures such as breaking

windows. Existing standards are usually based on the

maximum pressure that structural elements can resist, as

well as the human response (Mohanty 1998). Australian

guidelines recommend maximum level for air-blast over-pressure of 120 dBL based on human discomfort (ANZEC

1990; Environment Australia1998). The same criterion is

given in Chinese National standard (General Administra-

tion of Quality, Supervision, Inspection and Quarantine

2003; Lu et al.2012). USBM predicts a certain higher level

(133 dBL), which is a safe structural level, but may affect

residents (Siskind et al. 1980a, b). In this study, existing

Serbian Technical guidelines are used, which give critical

values of air overpressure due to amount of explosive used

and distance from the explosive charge (Technical guide-

lines for using explosives and blasting in mine operations

1988).

In present paper, the ground motion and air-blast over-

pressure due to blasting are investigated, as well as their

effect on infrastructure, at a limestone quarry Drenovac

in central part of Serbia. Limestone is chosen as a repre-

sentative rock unit for investigating the ground vibration

because it is the most common rock type in Serbian

quarries, and limestone is mostly used rock type for civil

engineering purposes. Moreover, rather than being excep-

tions, there are many experimental investigations on blast-

induced vibrations in limestone. Kesimal et al. (2008)

investigated the impact of blast-induced ground motion on

slope stability at Arakli-Tasonu limestone quarry in Trab-

zon (Turkey). Afeni and Osasan (2009) studied the level of

noise generated and ground vibrations induced during

blasting operations at the Ewekoro limestone quarry in

Nigeria, and their effect on residential structures within

villages near the quarry. Mohamed (2009) developed an

artificial neural network model for PPV prediction in a

limestone quarry in Egypt, by analyzing the predictive

power of ANN with different number of input units. Torno

et al. (2011) developed a computational fluid dynamics

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model in order to simulate the dispersion of blast-generated

dust in limestone quarries. Mohamadnejad et al. (2012)

used artificial neural network and support vector machine

for prediction of blast-induced vibrations in two limestone

quarries.

The goal of the paper is manifold. Firstly, the predictive

power of existing conventional predictors is to be estimated

using the recorded PPV values. Secondly, a new model is

intended to be developed, using artificial neural network

approach (ANN), with three main input units (total charge,

maximum charge per delay and distance from the explosive

charge to monitoring station). In the final phase of the

research, the effect of the recorded PPV and corresponding

frequency, as well as air-blast overpressure, on the nearby

residential structures is estimated using the existing

vibration standards.

The scheme of this paper is as follows. Blasting and

field measurements provides the description of the

applied methodology and test procedure, which includes

the blasting equipment, and the corresponding field work.

In Analysis of blast-induced ground motion existing

conventional predictors are used, and their predictive

power is evaluated for the recorded PPV, after which a new

model is suggested by using ANN approach. In Conclu-

sions the impact of ground vibration and air-blast over-

pressure on nearby residential structures is estimated. Final

section presents discussion on the obtained results, with

suggestions for further research.

Blasting and field measurements

The main parameters of performed blasting are given in

Table1, while the position of recording instruments andblasting shots is shown in Fig. 1.

The velocity of ground oscillation induced by blasting

was measured by mobile seismograph of Vibralok type,

with frequency range 2250 Hz, sampling of 1,000 Hz and

trigger levels of 0.1200 mm/s.

Analysis of blast-induced ground motion

PPV prediction using conventional equations

In order to develop a proper and accurate PPV prediction

model, empirical attenuation equations are commonly

used, which represent prediction models for PPV as a

function of scaled distance, already used in Erarslan et al.

(2008), Iphar et al. (2008), Kuzu (2008). Various conven-

tional predictors proposed by different researchers are

given in Table2(Duvall and Petkof1959; Langefors and

Kihlstrom1963; Davies et al. 1964; Ambraseys and Hen-

dron 1968; Singh and Roy 1993). These equations are

developed on the basis of the assumption that the total

Table 1 Main blasting

parametersNo. of

explosive

charges

No. of

blast holes

Maximum charge

per delay (kg)

Total

charge (kg)

No. of

measurement

stations

Total number of

vibration records

8 210 3285.2 6001,988.6 6 32

Fig. 1 Position of recording instruments (MM1MM7) and explo-

sive charges (18) at Drenovac limestone quarry

Table 2 Different conventional predictors

Conventional predictor Equation

Duvall and Petkof (1959) (USBM) vK R= ffiffiffiffiffiffiffiffiffiffiQmaxp BLangefors and Kihlstrom (1963)

vKffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffi

Qmax=R2=3 ph iB

General predictor (Davies et al. 1964) v = KR-B(Qmax)A

Ambraseys and Hendron (1968) vK R= ffiffip3Qmax BSingh and Roy (1993) (CMRI) vnK R= ffiffiffiffiffiffiffiffiffiffiQmaxp av is peak particle velocity (PPV) in mm/s, Qmax is maximum charge

per delay, in kg, R is distance between the blasting source and

vibration monitoring point, in meters, and K, B, A, a, n are site

constants

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energy of the ground motion generated by blasting varies

directly with the weight of detonated explosives and that it

is inversely proportional to the square distance from

blasting point. It has to be emphasized that the ultimate

goal of the present research is not to evaluate the existing

conventional predictors; the only intention is to examine

their predictive power for the specific case under study

(Iphar et al. 2010).

Table 3 Calculated values of site constants

Site constants

USBM LK GP AH CMRI

K B K B K B A K B n K

3,659 1.70 3.82 0.43 56,954.05 1.87 0.41 19,608 1.81 -3.03 430.7

LK LangeforsKihlstrom, GP general predictor, AH AmbraseysHendron

Table 4 Coefficient of correlation (R) and mean-squared error (MSE) for measured PPV vs. predicted PPV by conventional predictors

Linear regression

USBM LK GP AH CMRI

R MSE R MSE R MSE R MSE R MSE

0.88 3.88 0.88 15.60 0.87 4.04 0.69 3.92 0.88 3.80

LK LangeforsKihlstrom, GP general predictor, AH AmbraseysHendron

Fig. 2 Measured PPV vs. predicted PPV by conventional predictors: a USBM, b LangeforsKihlstrom, c general predictor, d Ambraseys

Hendron, e CMRI


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The site constants were determined from the multiple

regression analysis of the total recordings (Table3).

Coefficient of correlation (R) and mean-squared error

(MSE) for measured PPV vs. predicted PPV by conven-

tional predictors is shown in Table4. The relationship

between measured and predicted PPV is given in Fig. 2.

It is clear from Fig.2 that all predictor equations, except

for AmbraseysHendron predictor, give rather high coef-ficient of correlation, in the range R = 0.870.88.

Regarding the past researches on this subject, the values of

coefficient of correlation above 0.8 indicate that the mea-

surement data could be used for PPV prediction by

deploying the conventional predictor equations (Kahriman

et al. 2006; Iphar et al. 2009).

PPV prediction using artificial neural network

As a next step in present analysis, a neural network model

is developed by using the same approach as in Monjezi

et al. (2013) with total charge, maximum charge per delayand distance from monitoring point to blasting source as

input parameters, whereas PPV was considered as a single

output parameter (Table5).

In this paper, in order to create an adequate ANN model

for PPV prediction, based on the recorded data, a three-

layer artificial neural network is chosen using back-prop-

agation with LevenbergMarquardt training algorithm.

This training algorithm is commonly considered as the

fastest method for training moderate-sized feed-forward

neural networks (Tiryaki 2008), and it is usually recom-

mended as a first choice for supervised learning, as in this

case (Hagan and Menhaj 1994). As for the activation

function, a sigmoid function is deployed, as the most

common transfer function implemented in the literature

(Sonmez et al. 2006).

Regarding the network architecture, one hidden layer

was chosen in present study, following the suggestion of

Rumelhart et al. (1986), Lipmann (1987) and Sonmez et al.

(2006). However, the number of hidden neurons was

determined using heuristics summarized by Sonmez et al.

(2006). As it is clear from Table 6, the number of neurons

that may be used in the hidden layer varies between 1 and

9. In present study, the number of hidden neurons was

selected as 2, 6 and 9 separately to establish the most

effective ANN architecture.

In all the examined cases, the total data set has been

divided as following: 50 % for training (16 recordings),

25 % for validation (8 recordings) and 25 % for testing (8

recordings), which corresponds well with the suggestion of

Looney (1996), who proposed 25 % for testing, and with

recommendation by Nelson and Illingworth (1990) who

supported the idea of 2030 % of data for testing.

It has to be emphasized that the analysis of this rela-

tively small data set could lead to ambiguous results andinterpretations. However, regarding the application of

ANN approach for prediction of blasting vibration, the

analysis of small data sets is not an exception. Moham-

adnejad et al. (2012) also examined small number of data

(37) using support vector machine algorithm and regression

neural network, obtaining rather high prediction accuracy

(R2 = 0.92). Moreover, Monjezi et al. (2013) developed a

four-layer feed-forward back-propagation neural network,

using only 20 data sets. In this case, high prediction

accuracy was also obtained (R2 = 0.927).

The possible ANN architectures were trained by using

combinations of the number of hidden neurons definedabove. In order to utilize the most sensitive part of neuron

and since output neuron being sigmoid can only give out-

put between 0 and 1, scaling of the output parameter was

necessary, and was performed in the following way:

scaled value = max:valueunscaled value =max:valuemin:value 1

In that way, numerical values of the analyzed parameter

were normalized in the range of [0, 1].

Table 5 Inputoutput parameters for the ANN training and their

range

Data Parameter Range

Drenovac

Inputs Total charge (kg) 6001,988.6

Maximum charge per delay (kg) 3285.2

Distance from blasting source (m) 210.96737.38

Output Peak particle velocity (mm/s) 0.94914.997

Table 6 The heuristics used for the number of neurons in hidden

layer

Heuristic Calculated number

of neurons for this

study

References

B2 9 Ni ? 1 B7 Hecht-Nielsen

(1987)

3 9 Ni 9 Hush (1989)

(Ni ? N0)/2 2 Ripley (1993)

2N0Ni0:5N0 N20 Ni 3NiN0

1 Paola (1994)

2Ni/3 2 Wang (1994)ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiNiN0

p 2 Masters (1993),

Kaastra and Boyd

(1996)

2Ni 6 Kanellopoulas and

Wilkinson (1997)

Ni number of input neurons, N0 number of output neurons


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In order to avoid the possible occurrence of overfitting,

due to small dataset, an early stopping criterion is applied

(Yuan et al. 2007). Since a fast LevenbergMarquardt

training algorithm is used, the adaptive value l and its

decrease and increase factor were set to 1, 0.8 and 1.5,

respectively, so that the convergence is slow.

Mean-squared error (MSE) versus number of epochs for

testing and training data, using different number of hidden

neurons is shown in Fig.3. The best validation perfor-mance occurs at iteration 23, for the cases with 2 and 6

hidden neurons, and at iteration 14, for the case with 9

hidden neurons. It is clear that the results are reasonable,

without any significant overfitting, in all the examined

cases, since the training and the validation set errors have

similar properties. This result compares well with the

previous analyzes, claiming that large back-propagation

neural nets tend to learn models similar to those learned by

smaller nets (Lawrence and Giles2000).

Performance of the proposed neural network models

with scaled values for training, validation and testing set is

shown in Fig.4 for the examined cases with different

number of hidden nodes. Evidently, the outputs track the

target values very well in all the examined cases. How-

ever, for the case with two hidden nodes, shown in

Fig.4a, MSE for validation and test set (0.028 and 0.035,

respectively), is much larger in comparison with the

training set (0.002). Moreover, coefficient of correlationfor the training set, R & 0.97 is much higher when

compared to validation and test set (0.89 and 0.85,

respectively). Similar conclusions could be drawn for the

case with nine hidden neurons (Fig.4c). Apparently, it

turns out that the ANN model with six hidden neurons has

the best predictive performance, since the coefficient of

correlation for training, validation and test dataset is

nearly the same (R C 0.9), with the MSE in the range

0.0110.0282.

Fig. 3 MSE versus the number of epochs for training and testing data, using various number of hidden neurons: a two, b six and c nine


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Impact of blasting on neighboring structures

Evaluation of possible damage based on recorded PPV

and frequency

The main goal of the performed blasting was to estimate

the possible effect of blast-induced ground vibration on the

nearby residential structures. Concerning this, as it was

already stated in the introductory part, monitoring instru-

ments were located in front of the neighboring buildings, so

as to obtain a reliable data for assessing the blast-induced

damage. As for the existing structures near the studied

quarry, the present analysis focuses on the six domestic

houses of the same structural characteristics, but with dif-

ferent dimensions. These buildings belong to masonry type

of construction, with foundation made from concrete, while

the steel reinforcements are used for overfooting. Slab ismade of reinforced concrete.

In present paper, since there are no domestic regulations

on the impact of blasting on neighboring structures, pos-

sible structural damage is estimated using most frequently

applied criteria, which commonly define the limits for

structural damage as a function of PPV and frequency of

the blast. Figure5a presents the evaluation of damage

potential at monitoring stations for the conducted experi-

mental blasts according to the above-mentioned criteria by

Fig. 4 Comparison of the predicted and measured values of PPV for training, validation and test set (scaledvalues), for the following number of

hidden nodes: a two, b six and c nine


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DIN (German Institute of Standards1986) and US Bureau

of Mines (USBM) (Siskind et al. 1980a).On the basis of this analysis, it is clear that, according to

USBM criterion (Fig. 5b), conducted blasts are far from

resulting in any structural damage. However, DIN criterion

(Fig.5a) shows approximately ten cases off the upper

boundary predicted for the objects of class II. Even though

these recordings are far from the upper boundary of the first

class of objects (industrial buildings), one should bear in

mind that these vibrations could induce structural damage,

especially if the object is old or poorly constructed.

In Fig.5, different colors denote data from different

velocity components: red, blue and black stand for trans-versal, longitudinal and vertical component, respectively. It

has to be emphasized that the observed buildings belong to

second class of object according to DIN, so only the lowest

boundary is shown in Fig. 5a.

Besides these two widely used criteria, USSR criteria

are frequently applied, especially in Eastern European

countries (Singh and Roy 2010). According to this crite-

rion, the maximum allowable PPV, repeated and onefold,

for residential buildings of all types is 30 and 60 mm/s,

Fig. 5 Evaluation of damage potential: a DIN 4150, b USBM, c USSR standard, d Australian standard


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which is far above the maximum recorded PPV in analyzed

case, 14.997 mm/s (Fig.5c). On the other hand, Australian

standard (2006) predicts maximal velocity value of 19 mm/

s for frequency greater than 15 Hz, for houses and low rise

residential buildings, which is also higher than recorded

ground vibration (Fig.5d). The assessment of damage

criteria according to Indian DGMS (Tech) S and T Circular

No. 7 (1997) is given in Fig. 6a, while the level of recorded

ground velocity due to vibration frequency, evaluated

according to Chinese safety regulations GB6722-201X, isgiven in Fig.6b.

In Fig.6a, the upper boundary is given only for

domestic houses/structures, not belonging to the owner,

while in Fig. 6b, the upper boundary stands for the rein-

forced concrete buildings. As in Fig. 6, different colors

denote data from different velocity components: red, blue

and black stand for transversal, longitudinal and vertical

component, respectively.

As it could be seen, no structural damage is expected

according to USSR, Australian and Chinese criteria, while

three recorded values of PPV exceeds the upper limit in

Fig.6a (DMGS circular), which implies the necessity for

more careful blast design, in order to reduce any possible

damage to nearby objects.

In order to further evaluate the effect of blasting on

neighboring objects, the results from the measurements can

be extrapolated through the site-specific attenuation equa-

tions with the highest R and the smallest MSE (CMRI

predictor, Table4), to obtain practical blasting charts to be

employed for future blasting operations, using the damage

limits set by the above-mentioned damage criteria. Similar

approach has already been used in Ozer et al. (2008), Ak

et al. (2009), Dogan et al. (2013).

For the performed blasts, the frequency analyzes resul-

ted in a clustering in the range of 3.775 Hz. The corre-

sponding maximum permissible PPV values for

residential buildings are 5 mm/s for frequencies

\10 Hz, 15 mm/s for frequencies in the range 1050 Hz

and 20 mm/s for frequencies in the range 50100 Hz,

according to DIN standard. On the other hand, maximum

allowable PPV values using USBM standard are 20 mm/sfor frequencies\10 Hz, and 50 mm/s for higher frequen-

cies. Using conventional equations with the highest values

ofR2 and the smallest value of MSE (CMRI predictor) the

relationships between the permissible amount of explosive

and the distance, i.e., the practical blasting charts, are

obtained for a given structural type according to each

standard, and are plotted in Fig. 7a, b. Similar approach is

also used for other previously analyzed vibration standards:

for USSR regulations, maximum allowable PPV is equal to

30 mm/s for repeated blasting, and 60 mm/s for onefold

blasting (Fig.7c). According to Australian regulations,

maximum permissible PPV value is equal to 19 mm/s

(Fig.7d).

DMGS regulations predict maximum velocity of 5 mm/

s for dominant excitation frequency\8 Hz, 10 mm/s, for

frequencies in the range 825 Hz, and 15 mm/s, for fre-

quencies[25 Hz (Fig. 8a). On the other hand, according to

Chinese safety regulations GB6722-201X, the maximum

velocity of 35 mm/s is predicted for frequencies\10 Hz,

45 mm/s for frequencies in the range 1050 Hz and

50 mm/s for frequencies[50 Hz (Fig. 8b).

Fig. 6 Evaluation of damage potential according to:aDGMS (Tech) S and T Circular No. 7 ( 1997),b Chinese safety regulations GB6722-201X


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Apart from the previously analyzed vibration standards,

some other regulations are sometimes used for estimating

the structural damage due to blasting. Crandell (1949)

suggested the correlation between the building safety and

relative energy (ER), as a function of recorded particle

acceleration and frequency or velocity. Zeller (1931,1933,

1949) proposed a special scale according to Zellers power

(or strength) of vibration, as a function of recorded accel-eration and frequency. Moreover, Zeller developed a spe-

cial vibration parameter, called Strength, expressed in

vibrar units, as a ratio of Zellers power to its reference

value of 0.1 cm2/s3 (Steffens1974).

Effects of air-blast overpressure

Dynamic overpressure in air was monitored with the

microphones connected to the air-blast channels of

recording units. Air-blast overpressure (AOp) was mea-

sured in a range of 2150 Pa. The microphones have an

operating frequency response from 2 to 250 Hz, which is

adequate to measure accurately overpressure in the fre-

quency range critical for structures and in the range of

frequencies critical for human hearing (Raina et al. 2004;

Khandelwal and Singh 2005). The air-blast overpressure

was measured at four different distances from theblasting shots (Table7).

In terms of possible damage due to air-blast over-

pressure (windows breakage), a common limitation is

134 dB recommended in a report of United States

Bureau of Mines (USBM) (Siskind et al. 1980b).

Actually, 134 dB limit is one-half of the AOp of

140 dB that has served previously as a long-term

common standard for construction and quarry blasting.

Neither of these has been shown to cause window

Fig. 7 Blasting chart for regulations: a DIN4150, b USBM, c USSR, d Australian


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breakage or structural damage (Kuzu et al. 2009).

Technical guidelines for using explosives and blasting

in mine operations (1988) gives permissible

overpressure in function of the detonation frequency, in

the range 100500 Pa (Table8).

It is clear that in all the examined cases, air-blast

overpressure is well below the predicted limits according to

Tables7 and8, since the frequency of detonation is max-

imum twice a week.

Moreover, similarly to the previous case of blast-

induced ground motion, a permissible amount of explosive

could be determined as a function of distance from the

blasting shot and allowable air-blast overpressure.

According to Technical guidelines for using explosives and

blasting in mine operations (1988), for maximum two

detonations in a week, allowable air-blast overpressure is

200 Pa. Using the empirical formula, suggested in Kuzu

et al. (2009):

AOpk RQmax 0:33

!b2

where AOp is air overpressure (dB), k and b are the site

factors, R is the distance from blasting shot to monitoring

station and Qmax represents maximum charge per delay.

The site factors are determined according to the recordedvalues given in Table6(Fig.9a).

Based on Eq. (2) and determined values of site factors

(Fig. 9a), it is possible to construct a chart for the per-

missible amount of explosive as a function of distance

from the blasting shot, for the maximum allowable value

of air-blast overpressure (140 dB), according to Techni-

cal guidelines for using explosives and blasting in mine

operations (1988). This diagram is shown in Fig. 9b.

Fig. 8 Blasting charts for: a DMGS vibration standard, b Chinese safety regulations GB6722-201X

Table 7 Recorded values of air-blast overpressure

Recording

no.

Distance

from

explosive

charge to

measuring

point (m)

Total

amount of

explosive

(kg)

Maximal

amount of

explosives

per interval

(kg)

Air-blast

overpressure

Pa dB

M1 647.42 661.4 36.2 22.4 120.98

M2 605.54 1,980.6 71.2 2.7 102.61

M3 616.35 915.3 66.2 3 103.52

M4 644.64 915.3 66.2 25.2 122.01

Table 8 Maximum allowable air-blast overpressure in function of

detonation frequency (Technical guidelines for using explosives and

blasting in mine operations 1988)

Frequency of detonation Maximum allowable

overpressure increase

Several detonations during a day Daily monitoring of air-blast

overpressure must be

conducted

Several detonation twice a week \100 Pa

Maximum two detonations in a week \200 PaMaximum two detonations in a month \300 Pa

Maximum two detonations in a year \500 Pa


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Conclusions

In present paper, the blast-induced ground vibrations and

their effect on possible structural damage is analyzed at a

limestone quarry Drenovac in Serbia. The blasting was

performed at 8 locations, whereas the vibrations were

recorded at 6 different monitoring stations, with total

dataset of 32 measurements. Even though the analyzed

dataset is relatively small, it represents valuable experi-

mental data that has never been analyzed in such a mannerfor any type of surface blasting in Serbia, as far as authors

are aware.

In the first phase of the research, blast-induced ground

motion is examined using the existing conventional predic-

tors, and artificial neural network approach. The conducted

analysis showed that, by using conventional predictors, a

satisfying prediction accuracy is obtained (R[0.8), except

for the case of AmbraseysHendron predictor. Similarly, by

applying ANN with six hidden nodes in a hidden layer, a

prediction model is developed with similar predictive pre-

cision (R & 0.9) as in the case of conventional predictors,

but with the lower value of MSE (0.028).In the second phase of the research, the effect of blast-

induced ground motion and air overpressure on the

neighboring residential objects is evaluated, using the

existing vibration standards. The affected objects represent

domestic houses of simple constructional properties, with

brick, mortar and concrete as main construction materials.

The performed analysis showed that, according to USBM,

USSR, Australian and Chinese standards, no structural

damage is expected for the nearby objects. However, DIN

4150 and Indian DMGS standards indicate several cases of

recorded velocities off the upper limit for the chosen class

of objects. This further implies the need for more careful

blast design, in order to prevent the possible damage to the

residential objects in the quarrys surrounding.

Using the relation between the recorded PPV and scaled

distance with the highest coefficient of correlation and the

smallest value of mean-squared error (CMRI predictor), the

blasting charts for every analyzed vibration standard were

constructed, giving the permissible amount of explosive asa function of distance from the blasting shot.

Besides the measuring of blast-induced ground motion,

air-blast overpressure was also recorded at four different

monitoring points. According to USBM and domestic cri-

teria, recorded values of overpressure are far smaller than

the lowest threshold. Also, similarly to the previous analysis

of blast-induced ground motion, a blasting chart was con-

structed for the permissible amount of air-blast overpressure

as a function of distance to the explosive charge, using the

upper limit of 140 dB for air-blast overpressure, as pre-

dicted by the Technical guidelines for using explosives and

blasting in mine operations (1988). In this case, site factorsfor the attenuation Eq. (2) were determined only on the

basis of four recordings, which could lead to ambiguous

interpretations. In order to avoid this, further research

should include a larger data set of air-blast overpressure.

It has to be emphasized that the major constraint for the

performed analysis was the limited data set, which could

affect the results and, consequently, lead to dubious inter-

pretations and conclusions. Concerning this, an early

stopping criterion was applied in order to avoid overfitting,

Fig. 9 aDetermining the site factorsk(8.18) andb(0.51) for Eq. (2),

based on the recorded values of air-blast overpressure and scaled

distance, b chart for permissible amount of explosives (kg) as afunction of distance from the blasting shot to the monitoring station,

for the maximum allowable value of air-blast overpressure (140 dB)

(Technical guidelines for using explosives and blasting in mine

operations 1988)


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which gave reasonable results and prediction models.

However, one should note that a proposed ANN model

would certainly be improved by analyzing a larger dataset,

which could further increase the accuracy and reliability of

the suggested model.

As for the impact of induced ground vibrations on the

nearby objects, it is confirmed that in most of the analyzed

cases, recording velocities (and corresponding frequencies)are below the permissible values for the chosen class of

objects. However, several cases of higher recorded values

(according to DIN4150 and DMGS standard) imply the

need for more careful blast design, in order to avoid any

possible structural damage.

Acknowledgments This research was partly supported by the

Ministry of Education, Science and Technological Development of

the Republic of Serbia (Grants 176016 and 33029).

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