Blasting pattern design by neural network

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Design of blasting pattern in proportion to the peak particle velocity (PPV):

Artificial neural networks approach

H. Bakhshandeh Amnieh , A. Siamaki, S. Soltani

Dep. of Mining Eng., University of Kashan, Kashan 87317-51167, Islamic Republic of Iran

a r t i c l e i n f o

Article history:

Received 9 August 2011

Received in revised form 1 March 2012

Accepted 4 May 2012

Available online 15 June 2012

Keywords:

Ground vibration

Blasting pattern

Neural network

Peak particle velocity

a b s t r a c t

Ground vibration is a side effect of blasting and causes the destruction of buildings and other surrounding

facilities. Different damage mitigation standards have been presented in this connection. Ground vibra-

tion is affected by parameters of blasting pattern design, distance from blasting site and explosive weight.

In this research, ground vibrations data generated by 20 blasts in Sarcheshmeh copper mine, Kerman, at

47 locations have been recorded. The artificial neural network (ANN) has been trained using these peak

particle velocity (PPV) data and other parameters such as block volume and explosive type employed. The

trained network is capable of presenting appropriate specifications for the safe blasting pattern, consid-

ering the structure in question and its allowable vibration. The network outputs include burden, spacing

and total weight of explosive used. To verify training corrections, network was tested and correlation

coefficients of 0.651, 0.77 and 0.963 were obtained for the total explosive weight, burden and spacing,

respectively. The effects of explosive type were studied with due regards to recorded data.

2012 Elsevier Ltd. All rights reserved.

1. Introduction

Although a small proportion of the energy released by explo-

sives are consumed in the mass excavation of rocks (Singh,

2006), blasting remains an important practical option as far as cost

advantages and ease of operation are concerned (Rai and Singh,

2004). Vibrations due to blasting cause the energy of explosive to

transfer. The disturbance ensued spreads inside the rock mass in

the form of stress waves and source energy is transferred in the

form of energy flux (Srbulov, 2010). Transferred energy induces

vibrations inside the rockmass and on the ground surface. Surface

waves cause vibrations in the nearby structures. When wave fre-

quency is in the range of the normal frequency of the structure it

will result in more damages due to the resonance phenomenon.

Blasting pattern parameters such as number of blast holes,

diameter, depth, spacing and burden, stemming height, maximumweight of the explosive per delay and horizontal distance from

measuring point influence greatly vibration and fragmentation

caused by blasting operations (Singh and Singh, 2005).

A large number of correlations have been presented by

researchers for blasting patterns design. Those presented by Pearse

(1995), Allsman (1960) and Speath (1960) are based on explosive

specifications and rock mass strength. They did not include how-

ever explosive type and weight, as well as rockmass characteristics,

as their influence were difficult to quantify. In Ash, (1968)

presented a simple relation for determining burden based on blast

hole diameter. Livingston (1956) had suggested a relation to deter-mine burden based on Cratering Theory (Bhandari, 1997). Other

blasting parameters such as spacing, stemming and sub-drilling

were introduced using burden. Most of the correlations presented

for burden determination are pivoted on rock characteristics, blast

hole diameter and explosive properties. Many do not consider

however vibrations caused by blasting. Hence, a method for blast-

ing patterns design is presented in this study in which blasting

vibrations is considered. It should be mentioned however, that

due to lack of access to geological characteristics, the correspond-

ing parameters are not included in this study, and should be in-

cluded in furthering this work.

ANN technique, developed since 1980s and was extensively

used in solving complex engineering problems. It is capable of pre-

dicting a generalized solution from the patterns obtained duringtraining and previous learning (Dehghani and Ataee-pour, 2011).

PPV is an important parameter in blasting and many attempts in

estimating it have been carried out by Singh and co-workers using

ANN (Singh et al., 1994, 2004, 2008; Singh and Premkrishnan,

2000; Singh, 2004; Khandelwal and Singh, 2006; Sawmliana

et al., 2007; Mohammad, 2009). Blast design and explosive param-

eters were incorporated (Singh and Verma, 2010) in an ANFIS mod-

el to calculate blast frequency by generating a sugeno type fuzzy

inference system. In their recent work Verma and Singh (2011)

employed genetic algorithm (GA) and incorporated several blast

design and explosive parameters to predict PPV and following a

comparative study have suggested better performance of GA over

0925-7535/$ - see front matter 2012 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.ssci.2012.05.008

Corresponding author. Tel.: +98 913 3156035; fax: +98 361 5552292.

E-mail address: [email protected] (H. Bakhshandeh Amnieh).

Safety Science 50 (2012) 19131916

Contents lists available at SciVerse ScienceDirect

Safety Science

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http://dx.doi.org/10.1016/j.ssci.2012.05.008mailto:[email protected]://dx.doi.org/10.1016/j.ssci.2012.05.008http://www.sciencedirect.com/science/journal/09257535http://www.elsevier.com/locate/sscihttp://www.elsevier.com/locate/sscihttp://www.sciencedirect.com/science/journal/09257535http://dx.doi.org/10.1016/j.ssci.2012.05.008mailto:[email protected]://dx.doi.org/10.1016/j.ssci.2012.05.008


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the conventional empirical equations and ANN for their respected

results. In this study, using blasting vibration data recorded in Sar-

cheshmeh Copper mine and their corresponding patterns, a net-

work for designing the appropriate pattern for a specific PPV was

trained.

2. Ground vibration data

Ground vibration data were obtained from blastings at Sar-

cheshmeh copper mine, Kerman, using PDAS-100 (3-component

seismographs) and L-4C (3-component seismometer). Seismome-

ters were mounted in three directions (radial, tangential and verti-

cal). Explosives were ANFO and Emulan with the minimum weight

of 1290 kg and maximum weight of 26,000 kg, the minimum num-

ber of blast holes was 19 and the maximum was 73, the least mea-

sured distance was 410 m and the most was 3825 m. Considering

theeffect of thePPV on thedesign of blastingpattern,the total resul-

tantof the PPV was analyzedby relation1 below (Najm et al., 2002):

PPV

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPPVV

2 PPVr2 PPVl

2

q1

Fig. 1 shows maximum PPV versus dominant frequency of thedata generated at Sarcheshmeh copper mine, Kerman. As shown,

the recorded dominating frequency varies in a range of 220 Hz.

According to USBM standard (1980), the potential damage to resi-

dential buildings due to blasting vibration frequencies of less than

40 Hz is more than those of 40 Hz and above. Also, for frequencies

of less than 40 Hz, a PPV range between 19.05 mm/s (for modern

structures) and 12.7 mm/s (for older structures with plaster walls)

were proposed; the lowest limit suggested is 13 mm/s for plaster

walls (California Department of Transportation, 2004; Dyno Nobel).

Considering the above standards, blasts with vibrations greater

than 12.7 mm/s become significant. All blast vibrations recorded

with frequencies more than 12 Hz, were carried out by Emulan

due to the high strength of this explosive compared with ANFO.

As demonstrated in Fig. 2, PPV caused by Emulan blasting is

more than that by ANFO for an equal scaled distance; for a scaled

distance of 10, Emulans PPV is 24 times that of ANFO.

Considering above, use can be made of ANFO for blasts near vul-

nerable regions and civilian structures. A combination of Emulan

and ANFO may also be suggested in appropriate weight ratio for

such applications as well as wet blast-hole conditions.

3. Artificial neural network

Artificial neural network is an information-processing system

which tries to emulate data processing ability of human brain with

two distinct characteristics. Firstly, data is generated through a

trial and error learning process and secondly, neurons connections,

known as weight are used to save data (Rojas, 1996; Bakhshandeh

Amnieh et al. 2010). Multi-layer perception (MLP), is employed in

this technique which consists of at least three layers: input, output

and intermediate or hidden layers. The number of hidden layers

and neurons selected depends on complexity of the problem tobe solved (Monjezi et al., 2011).

For the first time in 1986, Rumelhart proposed back propaga-

tion algorithm (BPA) for training of the MLP networks and determi-

nation of weights (Bakhshandeh Amnieh et al. 2010). BPA is the

most versatile and robust technique and provides efficient learning

procedure for MLP. The fact that BPA are especially capable of solv-

ing predictive problems makes them very popular (Khandelwal

and Singh, 2009).

BPA is used for convergence towards the least error. In these

networks, the weights and the biases are updated in every training

period and these changes are made for minimizing the network

operation function. Operation function finds the error between

the network output and the real one for which use is usually made

of the mean square error function. Data processing details are de-scribed in several publications and to give a background here we

repeat the explanation of Singh and his co-workers.

In a network, the jth neuron, in the hidden layer, is connected to

a number of inputs:

xi x1;x2;x3; . . . ;xn 2

The net input values in the hidden layer will be

Netj Xn

i1

xiwij hj 3

wherexi are the input units, wij are the weights on the connection of

the ith input and jth neuron, hj is the bias neuron (optional) and n is

the number of input units.

The net output from hidden layer is calculated using a logarith-mic sigmoid function

Oj fNetj 1=1 eNetjhj 4

The total input to the kth unit is

Ok fNetk 5

In the learning process, the network is presented with a pair of

patterns, an input pattern and a corresponding output pattern. The

network computes its own output pattern using its weights and

thresholds. Now, the actual output is compared with the desired

output. Hence, the error at any output in layer k is

el tk Ok 6

where tk is the desired output and Ok is the actual output.The total error function is given by

Fig. 1. PPV versus frequencies for data collected from Sarcheshmeh copper mine,Kerman.

Fig. 2. PPV versus scaled distance for data collected at Sarcheshmeh copper mine,

Kerman.

1914 H. Bakhshandeh Amnieh et al./ Safety Science 50 (2012) 19131916


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E 0:5Xn

k1

tk Ok2 7

Training of the network is basically a process of arriving at an

optimum weight space for the network. The steepest descent error

surface is made using the following rule:

rWjk gdE=dWjk 8

where g is the learning rate parameter and E is the error function.The update of weights for the (n + 1)th pattern is given as

Wjkn 1 Wjkn rWjkn 9

Similar logic applies to the connections between the hidden and

output layers. This procedure is repeated with each pair of training

case. Each pass through all the training patterns is called a cycle or

epoch. The process is then repeated as many epochs as needed un-

til the error is within the user specified goal (Khandelwal and

Singh, 2009).

4. Blasting design algorithm using ANN

To train the ANN, 51 sets of data recorded in 20 blasts in Sar-

cheshmeh copper mine were used: 41 sets to train the network

and 10 to test its correctness. Desired PPV, distance between the

measuring point and blasting location, density of explosive and

volume of extraction block were selected as the network input

parameters. Rock mass density was not been considered as an in-

put parameter as it does not alter much in this region. The de-

signed network output contains burden, spacing and total weight

of explosive. Fig. 3 illustrates the network back propagation

algorithm.

Delay time could not be considered as a network output param-

eter since it varied in different blasting patterns. Table 1 displays

variation limits of the above parameters.

In all recorded patterns, blast hole depth was 15 m out of which

3.5 m was sub-drilling. Stemming varied between 6 and 7 m. Num-

ber of blast holes in each row and the number of rows were deter-mined based on dimensions of the extracted block, geomechanical

characteristics of the rock mass and production capacity. Basting

delays were determined according to the designers wish consider-

ing the dimensions required by fragmentation, fly rock, ground

vibration and air blast.

The network was trained using the BPA which does not always

converge to the absolute minimum; it might stop at a local

minimum (Petr et al., 2003). The errors of such networks are con-

trolled by the performance function. As mentioned before this func-

tion controls training process by controlling error between the

output and the real values. In our proposed network, the mean

square error function has been chosen as the performance function.

The network designed for blasting pattern with 4 hidden layers

and one output layer is shown in Fig. 4. Increasing the number of

layers and the number of neurons in each layer not only enhances

network ability in training, but also increases training time (a lim-

iting element in training). The layers array of this network is in the

form {16 14 12 10 3}. In the hidden layers, use has beenmade of the Sigmoid Tangent function as the transfer function

which is able to scale the response in a span of [1,1]. A linear

transfer function was also used in the output layer. The algorithm

used in this network is that of LevenbergMarquardt which is a

BPA different from GaussNewton optimization method. The

weights new order in the K+ 1th epoch is calculated according to

relation 10.

Wk 1 wk JTJ k I1JT ek 10

where J corresponds to the Jacobs matrix written for each neuron

as follows:

J

@e1

@w1. . .

@e1

@wn

@e1

@w0

. . .

. . . @ep@w1

. . .

@ep@wn

@ep@w0

266664

377775

x11

. . . xn1

1

. . .

. . .

xp1 . . . xnp 1

26664

37775 11

and w is the weight vector, w0 is the neuron bias, e in the error vec-

tor (difference between the network and the real outputs). k is the

modified parameter based on the error function E. IfE decreases in

Fig. 3. Network back propagation algorithm for design pattern of blasts at Sarcheshmeh copper mine, Kerman.

Table 1

Variation limits of parameters used to train the network.

No. Parameter Range

1 Peak particle velocity (mm/s) 0.8310.99

2 Distance from blast point (m) 7403525

3 Explosive density 0.81.5

4 Extraction block volume (m2) 29,760102,000

5 Burden (m) 7.07.5

6 Spacing (m) 7.09.57 Charge weight (ton) 7.0027.14

H. Bakhshandeh Amnieh et al./ Safety Science 50 (2012) 19131916 1915


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each epoch, it will be acceptable, otherwise k will vary and w(K+ 1)

is re-calculated (Dohnal, 2004).

Fig. 5 below shows that the error of the designed network is

1.65 1012. To verify the training correctness, the network was

tested by sets of data and then the correlation coefficient related

to each output parameter was studied. Table 2 shows the testedvalues and the related correlation coefficients.

5. Conclusions

Mitigation of ground vibrations caused by blasting diminishes

not only damage to the nearby structures, but also the dissatisfac-

tion of people living near the blasting site. In this research, the ANN

has been trained for design of blasting patterns with such input

parameters as distance from the explosion site, corresponding

PPV, explosive density and volume of extracted block. A character-

istic of this network is to present a proper pattern considering the

allowable PPV of the nearby structures. The PPV scaled distance

and PPV recorded vibration frequency graphs were investigated

too. Results have shown that for equal scaled distances, Emulan

explosions create more ground vibration compared with ANFO

and for equal PPV, the recorded frequencies for Emulan explosions

are also higher.

References

Allsman, P.L., 1960. Analysis of explosive action in breaking rock. TransactionsSociety of Mining Engineers, AIME, 217, 468478.

Ash, R.L., 1968. The design of blasting rounds. In: Surface Mining, AIME, New York,pp. 373397.

Bakhshandeh Amnieh, H., Mozdianfard, M.R., Siamaki, A., 2010. Predicting ofblasting vibrations in Sarcheshme copper mine by neural network. SafetyScience 48, 319325.

Bhandari, S., 1997. Engineering Rock Blasting Operations. A.A. Balkema.Blasting and Explosive Quick Reference Guide. Dyno Nobel.California Department of Transportation, 2004. Transportation and Construction

Induced Vibration Guidance Manual. pp. 180190.Dehghani, H., Ataee-pour, M., 2011. Development of a model to predict peak

particle velocity in a blasting operation. International Journal of Rock Mechanicsand Mining Sciences 48, 5158.

Dohnal, J., 2004. Using of Levenberg-Marquardt Method in Identification by NeuralNetwork. Dept. of Control and Instrumentation, FEEC, BUT, .

Khandelwal, M., Singh, T.N., 2006. Prediction of blast induced ground vibration andfrequency in open cast mine: a neural network approach. Journal of Sound andVibration 289, 711725.

Khandelwal, M., Singh, T.N., 2009. Prediction of blast-induced ground vibrationusing artificial neural network. International Journal of Rock Mechanics andMining Sciences 46, 12141222.

Livingston, C.W., 1956. Fundamental concept of rock failure. Symposium on RockMechanics, Quarterly Colorado School of Mines 51 (3), 114.

Mohammad, M.T., 2009. Artificial neural network for prediction and control ofblasting vibration in Assiut (Egypt) limestone quarry. International Journal ofRock Mechanics and Mining Science 46, 426431.

Monjezi, M., Ghafurikalajahim, M., Bahrami, A., 2011. Prediction of blast-inducedground vibration using artificial neural networks. Tunnelling and UndergroundSpace Technology 26, 4650.

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Pearse, G.E., 1995. Rock Blasting- some aspects on the theory and practice. Mine andQuarry Engineering 25, 2530.

Petr, V, Simoes, M.G, Rozgonoyi, T.G, 2003. Future Development of Neural NetworkPrediction for Blasting Design Parameter of Production Blasting. Explosive andBlasting Technique, Holmberg, pp. 625630.

Rai, R., Singh, T.N., 2004. A new predictor for ground vibration prediction and itscomparison with other predictors. Indian Journal of Engineering and MaterialsSciences 11, 178184.

Rojas, R., 1996. Neural Network, A Systematic Introduction. Springer.Sawmliana, C., Pal Roy, P., Singh, R.K., Singh, T.N., 2007. Blast induced air

overpressure and its prediction using artificial neural network. InternationalJournal of Mining Technology 116 (2), 4148.

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Singh, T.N., 2006. Prediction of safe charge to control ground vibration in surfacemining. Ground Vibration in Mining, pp. 2126.

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Fig. 4. ANN structure for design of the blasting pattern.

Fig. 5. Mean square error versus epochs.

Table 2

Variation ranges of the parameters used in the network testing and correlation

coefficients of the network responses.

No. Parameter Range Correlation

coefficient

1 Peak particle velocity (mm/s)

1.8910.01

2 Distance from blast point

(m)

885.0023.10

3 Explosive density 0.81.5

4 Extraction block volume

(m3)

53,760

102,000

5 Burden (m) 7.07.5 0.7651

6 Spacing (m) 7.59.5 0.9648

7 Charge weight (ton) 15.0026.14 0.6514

1916 H. Bakhshandeh Amnieh et al./ Safety Science 50 (2012) 19131916
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Blasting pattern design by neural network