Evaluation and prediction of blast-induced ground vibration using support vector machine

ARTICLE IN PRESS

International Journal of Rock Mechanics & Mining Sciences 47 (2010) 509–516

Contents lists available at ScienceDirect

International Journal ofRock Mechanics & Mining Sciences

1365-16

doi:10.1

� Tel.

E-m

journal homepage: www.elsevier.com/locate/ijrmms

Technical Note

Evaluation and prediction of blast-induced ground vibration using supportvector machine

Manoj Khandelwal �

Department of Mining Engineering, College of Technology and Engineering, Maharana Pratap University of Agriculture and Technology, Udaipur 313 001 India

a r t i c l e i n f o

Article history:

Received 18 May 2009

Received in revised form

15 December 2009

Accepted 8 January 2010Available online 31 January 2010

1. Introduction

Surface mining operations have increased throughout theworld for extraction of minerals from the earth crust. Drilling andblasting combination is still an economical and viable method forthe excavation and displacement of rockmass in mining as well asin civil construction works. Whenever an explosive chargedetonates in a blast hole, a gigantic amount of energy in termsof pressure (50 GPa) and temperature (5000 K) is liberated [1–3].Only a fraction of this energy is used for the actual fragmentationand displacement of rockmass, and the rest of the energy iswasted and creates a number of nuisances such as blast vibration,air blast, flyrock, noise, dust dispersion, back break, etc. [4].

The ill effects of blasting are unavoidable and cannot becompletely eliminated but should be minimized to avoid damageto the surrounding environment [5]. Among all the ill effects,ground vibration is a major concern to the planners, designers andenvironmentalists. It is very important as compared to otherblasting nuisances due to involvement of public residing in theclose vicinity of mining sites, regulating and ground vibrationstandards setting agencies together with mine owners [6]. Alsowith the emphasis shifting towards eco-friendly, geo-environ-mental activities, the field of ground vibration have become animportant parameter for the smooth running of a project.

To avoid socio-economic problems created by induced groundvibrations and to have cost effective blasting operations,pre-operational planning becomes essential. The economics, incase of small mining projects, may restrict continuous monitoringof ground vibrations during mining operations. By measuringvibration data, prior to actual operations and further planningwith the help of predictor equations may help the mine

09/$ - see front matter & 2010 Elsevier Ltd. All rights reserved.

016/j.ijrmms.2010.01.007

: +91 294 2471 379; fax: +91 294 2471 056.

ail address: [email protected]

management and owners. Thus, it is imperative to predict thevibration level prior to the operations.

A number of researchers have suggested various methods tominimize the ground vibration level during the blasting. Groundvibration is directly related to the quantity of explosive used anddistance between blast face to monitoring point as well asgeological and geotechnical conditions of the rock units in theexcavation area [7,8]. There are a number of vibration predictorsavailable suggested by different researchers [9–15]. All vibrationpredictor equations have their site specific constants, therefore,they cannot be used in a generalized way.

Geological and geotechnical conditions and distance betweenblast face to monitoring point cannot be altered but the onlyfactor i.e. quantity of explosive can be estimated based on certainempirical formulae proposed by the different researchers to makeground vibrations in a permissible limit. An appropriate and rockfriendly blasting can be only alternative for smooth progress ofthe rock removal process.

There has always been the need of a simple technique for theprediction of blast induced ground vibration by some indirect butrelevant and reliable method with greater accuracy. So, here anattempt has been made to predict the ground vibration by supportvector machine (SVM) taking into consideration of distancebetween blast face to monitoring point and maximum explosivecharge used in a delay. The prediction capability of SVM iscompared with widely used conventional predictors vis-�a-vismulti-variate regression analysis.

Over the past few years, various artificial intelligent (AI)techniques such as Hidden Markov Models (HMM), ArtificialNeural Networks (ANN) and Support Vector Machines (SVM) havebeen used in various mining, civil and geo-engineering applica-tions.

Numerous researchers have used ANN to predict the blastinduced ground vibration [6–8,16–21]. ANN is an information

www.elsevier.com/locate/ijrmms

dx.doi.org/10.1016/j.ijrmms.2010.01.007

mailto:[email protected]

ARTICLE IN PRESS

Fig. 1. Ground vibration due to blasting.

M. Khandelwal / International Journal of Rock Mechanics & Mining Sciences 47 (2010) 509–516510

processing system simulating the structure and functions of thehuman brain. It is a highly interconnected structure that consistsof many simple processing elements (called neurons) capable ofperforming massively parallel computation for data processingand knowledge representation. The neural network is firsttrained by processing a large number of input patterns and thecorresponding output. The neural network is able to recognizesimilarities, when presented with a new input pattern afterproper training and predicting the output pattern. The conven-tional pattern recognition method and ANN requires sufficientnumber of samples, which are sometimes difficult to obtain [22],whereas SVM is based on structural risk minimization principleand has very good generalization with few data samples.

SVM is a new generation learning system based on advances instatistical learning theory, enabling non-linear mapping of ann-dimensional input space into a higher dimensional featurespace, where, for example, a linear classifier can be used. The SVMcan train non-linear models based on the structural riskminimization principle that seeks to minimize an upper boundof the generalization error rather than minimize the empiricalerror as implemented in other neural networks. This inductionprinciple is based on the fact that the generalization error isbounded by the sum of the empirical error and a confidenceinterval term depending on the Vapnik–Chervonenkis (VC)dimension. Based on this principle, SVM will achieve an optimalmodel structure by establishing a proper balance between theempirical error and the VC-confidence interval, leading eventuallyto a better generalization performance than other neural networkmodels. An additional merit of SVM is that training SVM is auniquely solvable quadratic optimization problem, and thecomplexity of the solution in SVM depends only on the complex-ity of the desired solution, rather than on the dimensionality ofthe input space. Thus, SVM use a non-linear mapping, based on akernel function, to transform an input space to a high dimensionspace and then look for a non-linear relation between inputs andoutputs in the higher dimension space. SVM not only have arigorous theoretical background, but also can find global optimalsolutions for problems with small training samples, high dimen-sion, non-linearity and local optima. Originally, SVM weredeveloped for pattern recognition problems [23–25]. Recently,SVM has been shown to give good performance for a wide varietyof problems, such as non-linear regression.

Feng et al. [26] modeled non-linear displacement time series ofgeo-materials using evolutionary support vector machines andfound very accurate results. Liu et al. [27] used support vectormachine approach to design the tunnel shotcrete–bolting support.Zhao [28] used SVM for the slope stability analysis and shownthat the SVM based first-order second-moment method reliabilityanalysis can be used successfully for slope reliability analysisbased on the limit equilibrium method, such as the Bishop’smethod and Spencer’s method. Zhi-xiang [29] calculated thesubsidence coefficient by SVM.

Khandelwal and Kankar [30] predicted the blast induced airover pressure incorporating maximum charge per delay anddistance between blast face to monitoring point using supportvector machine and found better results than generalized cuberoot predictor equation. Kovacevic et al. [31] used SVM for theestimation of values of soil properties and soil type classificationbased on known values of particular chemical and physicalproperties in sampled profiles and found very good results.Khandelwal et al. [32] predicted the blast induced groundvibration of Dharapani Magnesite Mine, Pithoragarh, India usingSVM and found superior results as compared to vibrationpredictor equations.

These applications demonstrate that support vector machine iscapable of solving problems in which many complex parameters

influence the process and results, when process and results arenot fully understood, and where historical or experimental dataare available. The prediction of blast induced ground vibrations isalso of this type.

2. Mechanism of ground vibration

When an explosive charge detonates in the blast hole, intensedynamic stresses are set up around it due to sudden accelerationof the rockmass by detonating gas pressure on the hole wall.The strain waves transmitted to the surrounding rock sets up awave motion in the ground [33]. The strain energy carried out bythese strain waves fragments the rockmass due to differentbreakage mechanisms such as crushing, radial cracking andreflection breakage in the presence of a free face. The crushedzone and radial fracture zone encompasses a volume ofpermanently deformed rock. When the stress wave intensitydiminishes to the level where no permanent deformation occursin the rockmass (i.e. beyond the fragmentation zone), strainwaves propagate through the medium as the elastic waves,oscillating the particles through which they travel (Fig. 1). Thesewaves in the elastic zone are known as ground vibration, whichclosely confirm to the visco-elastic behaviour. The wave motionspreads concentrically from the blast site in all directions and getsattenuated due to the spreading of fixed energy over a greatermass of material and away from its origin [34]. Even though, theground vibration attenuates exponentially with distance but dueto large quantity of explosive, it can still be high enough to causedamage to buildings and other man made and natural structuresby causing dynamic stresses that exceed material strength [35].

3. The study area

The study was conducted at the Jayant opencast mine ofNorthern Coalfields Limited (NCL), which is a subsidiary companyof Coal India Limited. It is located at Singrauli, District Sidhi (M.P.),India. The area of NCL lies geographically between latitudes of 24100–241 120 and longitudes 821 300–821 450 and belongs to theGondwana super group. The dip of the strata is gentle and varyingfrom 21 to 51.

The coalfield can be divided into two sub basins, viz. Mohersub-basin (312 km2) and Singrauli Main basin (1890 km2).The field is divided into eleven major mining blocks namelyKakri, Bina, Marrack, Khadia, Dhudhichua, Jayant, Nighahi,Amlohri, Moher, Gorbi and Jhingurdah [36].

ARTICLE IN PRESS

Fig. 2. Blast face of Jayant mine.

PP2

P1

P2 }1{ =+ bxwT

P }0{ =+ bxwT

P1 }1{ −=+ bxwT

Margin

Fig. 3. Hyper-plane classifying two classes: (a) small margin and (b) large margin.

M. Khandelwal / International Journal of Rock Mechanics & Mining Sciences 47 (2010) 509–516 511

The overburden in this area is mostly medium to coarse-grained sandstones, carbonaceous shales and shaly sandstones.Fig. 2 shows the blast face of Jayant mine. The mine uses largedragline (24 m3 bucket size and 96 m boom length) in 40 mbenches. The bench is drilled with 311 mm diameter holes.Normal blast consists of firing 50–60 holes; consuming 150–200 tof explosive. Each hole of 35–40 m length is charged with 300 kgof explosive and the maximum charge per delay is about 6000 kg.Nonel and MS connectors are used for initiation. The inter-holedelay was 17–25 ms, whereas, inter-row delay was 2–4 times theinter-hole delay.

4. Support vector machine (SVM)

A SVM is a supervised machine learning method based on thestatistical learning theory. It is a very useful method forclassification and regression in small-sample cases such as faultdiagnosis. Pattern recognition and classification using SVM isdescribed here in brief; a more detailed description can be foundin [37,38].

Initially consider a simple case of two classes, which can beseparated by a linear classifier. Fig. 3 shows triangles and squaresstand for these two classes of sample points, respectively. Hyper-plane P is one of the separation planes that separate two classes.P1 and P2 (shown by dashed lines) are the planes those are parallelto P and pass through the sample points closest to P in these twoclasses. Margin is the distance between P1 and P2. The SVM triesto place a linear boundary between the two different classes, andorientate it in such way that the margin is maximized, whichresults in least generalization error. The nearest data points thatused to define the margin are called support vectors.

This is implemented by reducing it to a convex optimizationproblem: minimizing a quadratic function under linear inequalityconstraints [37]. Consider a training sample set {(xi,yi)}; i=1 to N,where N is the total number of samples. It is wished to determine,among all linear separation planes that separates input samplesinto two classes, which separation plane will have the smallestgeneralization error. Let us assume the samples can be classifiedinto two classes namely triangle and square class. Labels yi=�1and yi= +1 are associated with triangle and square class,respectively. If data are linearly separable, the hyper-planef(x)=0 that separates the given data is given as

f ðxÞ ¼XN

i ¼ 1

wixiþb¼ 0 ð1Þ

where w is an N-dimensional vector. The vector w defines adirection perpendicular to the hyperplane. The scalar value b

moves the hyper-plane parallel to itself, this value is sometimescalled the bias (or threshold). A distinct separating hyper-planeshould satisfy the constraints f ðxiÞZ1 if yi ¼ 1, and f ðxiÞr�1 ifyi ¼�1, or it can be presented as

yif ðxiÞ ¼ yiðwT xþbÞZ1 i¼ 1;2; . . . ;N ð2Þ

Since the resulting geometric margin will be equal to 1/JwJ2, thevector w that minimizes JwJ2 under constraint (2) is related to thevector that forms the optimal hyper-plane. So, the optimal hyper-plane separating the data can be obtained as a solution to thefollowing optimization problem:

Minimize 12JwJ2

ð3Þ

Subject to yiðwT xiþbÞZ1 i¼ 1;2; . . . ;N ð4Þ

To find the solution of the above quadratic optimizationproblem, the saddle point of the Lagrange function has to bedetermined. The Lagrangian function for optimization problem isgiven as

Minimize Lðw; b; lÞ ¼ 12JwJ2

�XN

i ¼ 1

li½yiðwT xiþbÞ�1� ð5Þ

where liZ0 are the Lagrange multipliers. To find the saddle pointone has to minimize this function over w and b and to maximize itover nonnegative Lagrange multipliers liZ0. Setting the deriva-tives of L with respect to w and b to zero. We have:

@Lðw; b; lÞ@w

¼w�XN

i ¼ 1

yilixi ¼ 0) w¼XN

i ¼ 1

yilixi ð6Þ

@Lðw; b; lÞ@b

¼XN

i ¼ 1

yili ¼ 0 ð7Þ

Substituting results from Eqs. (6) and (7) into Eq. (5) gives

WðlÞ ¼XN

i ¼ 1

li�1

2

XN

i;j ¼ 1

yiyjliljðxi xjÞ ð8Þ

The notation L(w,b,l) is changed to W(l) to reflect the lasttransformation. To construct the optimal hyper-plane, coefficientsli are to be determined that maximize the function (8). Thus bysolving the Dual optimization problem, the coefficients ‘li

o’ can beobtained which is required to express the ‘w’ from Eq. (6):

Maximize WðlÞ ¼XN

i ¼ 1

li�1

2

XN

i;j ¼ 1


Subject to

liZ0

XN

i ¼ 1

liyi ¼ 0; i¼ 1;2; . . . ;N

8>><>>:

ð10Þ

ARTICLE IN PRESS

Table 2Output parameter with range, mean and standard deviation.

S. No. Output parameter Range Mean Standarddeviation

1. Peak particle velocity

(mm/s)

0.31–

92.30

15.594 16.808


The value of bo is chosen to maximize margin and calculated as

bo ¼maxyi ¼ �1ð/wo xiSÞþminyi ¼ 1ð/wo xiSÞ

2ð11Þ

This leads to the decision function

f ðxÞ ¼ sign� XN

i;j ¼ 1

liyiðxi xjÞþb�

ð12Þ

When the training data is not linearly separable in featurespace, the optimization problem cannot be solved since nofeasible solution exists. To allow for the possibility of samplesviolating constraints, slack variables (xiZ0) are introduced. Aclassier which generalizes well is then found by controlling boththe classier capacity (via JwJ) and the number of training errors.The optimal hyper-plane separating the data can be obtained as asolution to the following optimization problem:

minimize 12JwJ2

þCXN

i ¼ 1

xi\ ð13Þ

subject to yiðwT xiþbÞZ1�xi ð14Þ

and

xiZ0; i¼ 1;2; . . . ;N ð15Þ

where C is a constant representing the error penalty. Rewritingthe above optimization problem in terms of Lagrange multipliers,leads to the following problem:

Maximize WðlÞ ¼XN

i ¼ 1

li�12

XN

i;j ¼ 1


Subject to

0rlirC

XN

i ¼ 1

liyi ¼ 0; i¼ 1;2; . . . ;N

8>><>>:

ð17Þ

The Sequential Minimal Optimization (SMO) algorithm givesan efficient way of solving the dual problem arising from thederivation of the SVM. SMO decomposes the overall QP probleminto QP sub-problems.

Fig. 4. Measured and predicted PPV by SVM.

5. Data set

A total of 174 blast vibration records were monitored atdifferent vulnerable and strategic locations in and around to theJayant opencast mine as per the ISRM standards [39]. Out of 174blast vibration data sets, 154 were used for the training andtesting of SVM model as well as to determine site constants forthe different vibration predictor equations and MVRA equations,whereas 20 randomly selected data sets were used for thevalidation of the SVM model as well as different conventionalvibration predictors and MVRA equations. Explosive charge usedper delay and distance between blast face to monitoring pointwas taken as an input, whereas PPV was taken as an outputparameter for the validation and testing of the SVM, MVRA andwidely used conventional predictors. Tables 1 and 2 show the

Table 1Input parameter with range, mean and standard deviation.

S. No. Input parameter

1. Maximum charge per delay (Qmax) in kilograms

2. Distance of monitoring point from blasting face (D) in meters

input and output parameters range with their mean and standarddeviation, respectively.

6. Testing and validation of SVM

For training and testing of the data set, the Weka software [40]is used, which is a collection of machine learning algorithmsfor data mining tasks. The algorithms can be applied directly toa data set. Weka contains tools for data pre-processing,classification, regression, clustering, association rules and visua-lization.

Sequential Minimal Optimization (SMO) algorithm is used dueto its quickly solving capability. SMO decomposes the overallquadratic programming problem into sub-problems of quadraticprogramming by using the Osuna’s theorem to ensure conver-gence. There are two components in SMO: an analytic method forsolving for the two Lagrange multipliers; and a heuristic one forchoosing multipliers in optimization. The advantage of SMO liesin the fact that solving for two Lagrange multipliers can be doneanalytically. Thus, numerical quadratic programming optimiza-tion is avoided completely.

One-hundred fifty-four blast data sets of the Jayant mine wereused for the training of SVM model, whereas twenty new data setswere used for the validation of the SVM model. The resultspresented in this section demonstrate the performance of theSVM model. Coefficient of determination between the predictedand observed values of PPV is taken as a performance measure.The prediction was based on the input data sets discussed above.

Fig. 4 illustrates the measured and predicted PPV on 1:1 slopeline with their respective coefficient of determination (CoD). CoD

Range Mean Standard deviation

75–6000 1374.339 1691.647

35–8400 829.903 1462.66

ARTICLE IN PRESS

Table 4Sample testing data set used for validation of the SVM model.


between predicted and measured values is as high as 0.960,whereas, mean absolute error (MAE) is 0.257 for PPV.

S. No. Qmax (kg) D (m) PPV (mm/s)

1. 5600 1000 6.57

2. 2300 350 69.8

3. 300 203 8.8

4. 150 105 37.58

5. 2487 1898 20.97

Table 5Different conventional predictors.

Name Equation

USBM (1959) v=K [D/OQmax]�B

Langefors–Kihlstrom (1963) v=K [O (Qmax/D2/3)]B

General predictor (1964) v=K D�B (Qmax)A

Ambraseys–Hendron (1968) v=K [D/ (Qmax)1/3]�B

Bureau of Indian Standard (1973) v=K [(Qmax/D2/3)]B

Ghosh–Daemen predictor (1983) v=K [D/OQmax]�B e�aR

CMRI predictor (1993) v=n+K [D/OQmax]�1

Where v, peak particles velocity (PPV) in mm/s; Qmax, Maximum charge per delay

in kg; D, Distance between blast face to vibration monitoring point in m, and; K, B,

a and n, site constants.

Table 6Calculated values of site constants.

7. Prediction by multi-variate regression analysis (MVRA)

The purpose of multiple regressions is to learn more about therelationship between several independent or predictor variablesand a dependent or criterion variable. The goal of regressionanalysis is to determine the values of parameters for a functionthat cause the function to best fit a set of data observationsprovided. In linear regression, the function is a linear (straight-line) equation. When there is more than one independentvariable, then multivariate regression analysis is used to getthe best-fit equation. Multiple regressions solve the data setsby performing least squares fit. It constructs and solves thesimultaneous equations by forming the regression matrix andsolving for the co-efficient using the backslash operator. TheMVRA was done by same data sets, which were used in the SVMtraining. The equation for PPV prediction by MVRA is

PPV¼ 15:6755þ0:0013Qmax ðkgÞ20:0023D ðmÞ ð18Þ

This equation is used for the prediction of PPV for twenty blastvibration cases. Fig. 5 illustrates the measured and predicted PPVon 1:1 slope line with their respective CoD. The CoD for PPV is0.142. This shows that an MVRA equation is not able to predict thePPV up to the desired level of accuracy. The MAE for PPV is 2.821.The high MAE value of PPV shows that prediction by MVRA ishaving high error (Tables 3 and 4).

Equation Site constants

K B A a n

USBM 179.3081 1.0904

Langefors–Kihlstrom 44.43242 –1.1795

General predictor 212.27 1.0949 0.5203

Ambraseys–Hendron 329.3063 –0.9702

Bureau of Indian Standard 6.328489 0.2118

Ghosh–Daemen predictor 780.36 1.2588 0.0004

8. Prediction by conventional predictors

Table 5 shows the various available conventional vibrationpredictor equations proposed by various researchers. The siteconstants were determined from the multiple regression analysisof earlier used 154 data sets. The calculated values of site constantsfor the various predictor equations are shown in Table 6.

Fig. 5. Measured and predicted PPV by MVRA.

Table 3Sample training data set used for learning the SVM model.

S. No. Qmax (kg) D (m) PPV (mm/s)

1. 350 101 15.66

2. 5600 3000 1.64

3. 2400 950 3.43

4. 1800 325 33.27

5. 737 420 13.72

CMRI predictor 168.91 1.5669

Fig. 6. Measured and predicted PPV by USBM predictor.

Figs. 6–12 illustrate the predicted graph between measuredand predicted PPV by conventional predictor equations on 1:1slope line with their respective coefficient of correlation.

9. Results and discussion

Fig. 13 shows a comparison between predicted PPV by SVM,MVRA and conventional predictor equations. Here, prediction by

ARTICLE IN PRESS

Fig. 8. Measured and predicted PPV by General predictor.

Fig. 9. Measured and predicted PPV by Ambraseys–Hendron predictor.

Fig. 10. Measured and predicted PPV by Bureau of Indian Standard predictor.

Fig. 11. Measured and predicted PPV by Ghosh–Daemen predictor.

Fig. 12. Measured and predicted PPV by CMRI predictor.

Fig. 7. Measured and predicted PPV by Langefors–Kihlstrom predictor.


SVM is closer to the measured PPV, whereas prediction byconventional predictors and MVRA has wide variation. Theaccuracy of MVRA and conventional predictors deteriorates at PPVof 10 mm/s and higher. Fig. 13 also revealed that SVM predicted PPVis very close to the measured PPV line, whereas conventionalpredictors show very high level of error. Table 7 shows the CoD andMAE of PPV predicted by SVM, MVRA and the various conventionalpredictors. It can be said that prediction capability of SVM is quiteremarkable and compares well to field observations.

10. Conclusions

Based on the study, it is established that the SVM seems to bethe better option for close and appropriate prediction of PPV to

protect the surrounding environment and structure. The useof any conventional vibration predictor without validation mayinvite further complication for smooth conduct of miningoperations. This study indicates that all conventional predictorsare either over estimating or underestimating the safe explosivecharge to keep the PPV level under the safe limit. Both thepredictions are not appropriate for the site where populations areresiding very near to the mine.

It was found that coefficient of determination betweenmeasured and predicted PPV was very high by SVM, whereas itwas very less by different conventional vibration predictors andMVRA. Coefficient of determination between measured andpredicted PPV was 0.960 by SVM, while it was ranging from

ARTICLE IN PRESS

Fig. 13. Comparison of PPV.

Table 7CoD and MAE of PPV by various models.

Model CoD MAE

SVM 0.960 0.257

MVRA 0.142 2.821

USBM 0.633 1.145

Langefors–Kihlstrom 0.106 2.177

General predictor 0.615 1.122

Ambraseys–Hendron 0.434 1.071

Bureau of Indian Standard 0.278 2.127

Ghosh–Daemen predictor 0.659 1.477

CMRI predictor 0.612 1.491


0.106 to 0.659 by different conventional vibration predictors and0.142 by MVRA. Application of SVM showed exceptional con-formity between the measured and predicted PPV as compared todifferent conventional vibration predictors and MVRA.

Considering the complexity of the relationship among theinputs and outputs, the results obtained by SVM is highlyencouraging and satisfactory. SVM can learn new patterns thatare not previously available in the training data set. SVM can alsoupdate knowledge over time as long as more training data sets arepresented. Therefore, the technique results in a greater degree ofaccuracy than any other analysis techniques.

By adopting SVM technique, PPV can be predicted prior to blastand accordingly blast design can be modified, so that blastnuisances can be minimized with greater degree of explosiveenergy utilization.

Acknowledgments

The financial assistance provided by Council of Scientific andIndustrial Research, New Delhi, India is thankfully acknowledged.The thanks are also due to the mine management of NorthernCoalfields Limited, Singrauli for providing necessary assistanceduring the field visit.

References

[1] McKenzie C. Quarry blast monitoring technical and environmental perspec-tive. Quarry Manage 1990;23–29.

[2] ISRM. Suggested method for blast vibration monitoring. Int J Rock Mech MinSci 1992;29:145–6.

[3] Cheng G, Huang SL. Analysis of ground vibration caused by open pitproduction blast. In: Holmberg, editor. Explosive and blasting technique.Rotterdam: Balkema; 2000. p. 63–70.

[4] Hagan TN. Rock breakage by explosives. In: Proc Natl Symp RockFragmentation, Adelaide, Australia, 1973. p. 1–17.

[5] Wiss JF, Linehan PW. Control of vibration and air noise from surface coalmines—III. US Bur Mines Rep OFR 103(3)–79, 1978.

[6] Khandelwal M, Singh TN. Evaluation of blast induced ground vibrationpredictors. Soil Dyn Earthquake Eng 2007;27:116–25.

[7] Khandelwal M, Singh TN. Prediction of blast induced ground vibrations andfrequency in opencast mine—a neural network approach. J Sound Vib2006;289:711–25.

[8] Khandelwal M, Singh TN. Prediction of blast induced ground vibration usingartificial neural network. Int J Rock Mech Min Sci 2009;46:1214–22.

[9] Duvall WI, Petkof B. Spherical propagation of explosion generated strainpulses in rock. US Bur Mines Rep Invest 5483, 1959. 21pp.

[10] Langefors U, Kihlstrom B. The modern technique of rock blasting. New York:Wiley; 1963.

[11] Davies B, Farmer IW, Attewell PB. Ground vibrations from shallow sub-surface blasts. The Engineer (London) 1964;217:553–9.

[12] Ambraseys NR, Hendron AJ. Dynamic behaviour of rockmasses. In: Rock MechEng Pract. London: Wiley, 1968. p. 203–7.

ARTICLE IN PRESS


[13] Bureau of Indian Standards. Criteria for safety and design of structuressubjected to underground blast. ISI Bulletin 1973; IS—6922.

[14] Ghosh A, Daemen JK. A simple new blast vibration predictor. In: Proc 24th USSymp Rock Mech, Texas A&M University, 1983. p. 151–61.

[15] Pal Roy P. Putting ground vibration predictors into practice. Colliery Guardian1993;241:63–7.

[16] Khandelwal M, Singh TN. Prediction of blast induced air overpressure inopencast mine. Noise Vib Worldwide 2005;36(2):7–16.

[17] Kuzu C. The importance of site-specific characters in prediction modelsfor blast-induced ground vibrations. Soil Dyn Earthquake Eng 2008;28(5):405–14.

[18] Monjezi M, Dehghani H. Evaluation of effect of blasting pattern parameterson back break using neural networks. Int J Rock Mech Min Sci 2008;45(8):1446–1453.

[19] Mohamed MT. Artificial neural network for prediction and control of blastingvibrations in Assiut (Egypt) limestone quarry. Int J Rock Mech Min Sci2009;46(2):426–31.

[20] Khandelwal M, Kumar DL, Mohan Y. Application of soft computing to predictblast-induced ground vibration. Engineering with Computers, 2009 (online).

[21] Monjezi M, Bahrami A, Varjani AY. Simultaneous prediction of fragmentationand flyrock in blasting operation using artificial neural networks. Int J RockMech Min Sci 2009; doi:10.1016/j.ijrmms.2009.09.008.

[22] Zacksenhouse M, Braun S, Feldman M. Toward helicopter gearbox diagnosticsfrom a small number of examples. Mech Syst Signal Process 2000;14(4):523–43.

[23] Scholkopf B, Burges C, Vapnik V. Extracting support data for a given task. In:Proc 1st Int Conf Knowl Discovery Data Min, Menlo Park, Calif, Am Assoc ArtifIntell Press, 1995.

[24] Schmidt M. Identifying speaker with support vector networks. In: ProcInterface ‘96, Sydney, 1996.

[25] Muller KR, Smola JA, Scholkopf B. Prediction time series with supportvector machines. In: Proc Int Conf Artif Neural Networks, Lausanne, 1997.p. 999–1004.

[26] Feng XT, Zhao H, Li S. Modeling non-linear displacement time series of geo-materials using evolutionary support vector machines. Int J Rock Mech MinSci 2004;41(7):1087–107.

[27] Liu KY, Qiao CS, Tian SF. Design of tunnel shotcrete–bolting support based on asupport vector machine approach. Int J Rock Mech Min Sci 2004;41(3):510–1.

[28] Zhao H. Slope reliability analysis using a support vector machine. ComputGeotech 2008;35:459–67.

[29] Zhi-xiang T, Pei-xian L, Li-li Y, Ka-zhong D. Study of the method to calculatesubsidence coefficient based on SVM. Proc Earth Planet Sci 2009;1:970–6.

[30] Khandelwal M, Kankar PK. Prediction of blast-induced air overpressure usingsupport vector machine. Arabian J Geosci 2009 (online).

[31] Kovacevic M, Bajat B, Gajic B. Soil type classification and estimation of soilproperties using support vector machines. Geoderma 2009 (online).

[32] Khandelwal M, Kankar PK, Harsha SP. Evaluation and prediction of blastinduced ground vibration using support vector machine. Min Sci Tech 2009(in press).

[33] Attewell PB. Recording and interpretation of shock effects in rock. Min MinerEng 1964:21–8.

[34] Dowding CH. Blast vibration monitoring and control. Englewoods Cliffs:Prentice-Hall; 1985.

[35] Siskind DE, Stagg MS, Kopp, JW, Dowding CH. Structure response and damageproduced by ground vibration from surface mine blasting. US Bur Mines RepInvest 8507, 1980. 74pp.

[36] Singh VK. Northern Coalfields Ltd.: surging ahead with time. J Mines MetFuels 2004:51.

[37] Vapnik VN. Statistical learning theory. New York: Wiley; 1998.[38] Cristianini N, Shawe-Taylor NJ. An introduction to support vector machines.

Cambridge: Cambridge University Press; 2000.[39] ISRM. Suggested method for blast vibration monitoring. Int J Rock Mech Min

Sci 1992;29:145–6.[40] Witten IH, Frank E. Data mining: practical machine learning tools and

techniques, 2nd ed.. San Francisco: Morgan Kaufmann; 2005.

doi:10.1016/j.ijrmms.2009.09.008

doi:10.1016/j.ijrmms.2009.09.008

Evaluation and prediction of blast-induced ground vibration using support vector machine

Documents

Transcript of Evaluation and prediction of blast-induced ground vibration using support vector machine