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Utilization of a nonlinear support vector machine to predict blasting vibration characteristic parameters in opencast mine

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Warianty tytułu
PL
Wykorzystanie metody SVM do prognozowania parametrów wibracji wybuchowych w kopalniach odkrywkowych
Języki publikacji
EN
Abstrakty
EN
Characteristic parameters of blasting vibration (BVCP) have great effects on its damage level. The prediction of BVCP is helpful to study blasting vibration effect. In this paper, an attempt has been made to predict blast-induced ground vibration using support vector machine (SVM) to avoid the limitation of the prediction with only one index and to improve the prediction precision. A Grid search method-based SVM prediction model for BVCP was established on the basis of nonlinear model-based SVM. To construct the model, nine factors affecting blasting vibration characteristic variables are taken as input parameters, whereas, peak particle velocity (PPV), dominant frequency (Df) and its time duration (Dt) are considered as output parameters. A database consisting of 108 datasets was collected from Tonglvshan copper mine in China. From the prepared database, 93 datasets were used for the training of the model, whereas 15 randomly selected datasets were used for the validation of the SVM model. To compare the performance of the developed SVM model with that of artificial neural network (ANN) model, the same database was applied. Superiority of the proposed SVM model over ANN model was examined by calculated coefficient of determination for predicted and measured values of PPV, Df and Dt. Concluded remark is that the prediction’s BVCP can reliably be estimated from the indirect methods using SVM analysis.
PL
Przy przewidywaniu efektów i szkód wibracji wybuchowych ważny jest parametr BVCP – blasting vibration characteristic parameter. W artykule przedstawiono model matematyczny do prognozowania efektów drgań wybuchowych z wykorzystaniem metody SVM.
Rocznik
Strony
127--132
Opis fizyczny
Bibliogr. 20 poz., rys., tab., wykr.
Twórcy
autor
  • School of Resources and Safety Engineering, Central South University, Changsha, China
autor
  • School of Resources and Safety Engineering, Central South University, Changsha, China
autor
  • School of Resources and Safety Engineering, Central South University, Changsha, China
Bibliografia
  • [1] GU D.S., Li X. B., Modern mining science and technology for metal mineral resources, Beijing: China Metallurgical Industry Press, (2006).
  • [2] Shi X.Z., Study of time and frequency analysis of blasting vibration signal and the prediction of blasting vibration characteristic parameters and damage, Ph.D Thesis, Changsha: Central South University, (2007).
  • [3] Shi X.Z., Chen S.R., Delay time optimization in blasting operations for mitigating the vibration-effects on final pit walls’ stability, Soil. Dyn. Earthq. Eng. 31(2011), No. 8, 1154-1158.
  • [4] Khandelwal M., Singh T.N., Evaluation of blast-induced ground vibration predictors, Soil. Dyn. Earthq. Eng., 27(2007), No. 2, 116−125.
  • [5] Iphar M., Yavuz M., Ak H., Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system, Environ. Geol, 56(2008), No. 1, 97–107.
  • [6] Singh T. N., Singh V., An intelligent approach to prediction and control ground vibration in mines, Geotechnical and Geological Engineering, 23(2005), No. 3, 249–262.
  • [7] Khandelwal M., Singh T.N., Prediction of blast-induced ground vibration using artificial neural network, Int. J. rock. Mech. Min. Sci., 46(2009), No. 7, 1214-1222.
  • [8] Mostafa T M, 2009. Artificial neural network for prediction and control of blasting vibrations in Assiut (Egypt) limestone quarry, Int. J. rock. Mech. Min. Sci., 46: 426–431 .
  • [9] Shi X.Z., Xue J.G., Chen S.R., A fuzzy neural network prediction model based on rough set for characteristic variables of blasting vibration. Journal of Vibration and Shock, 28(2009), No. 7, 73-76.
  • [10] Shi X.Z., Zhou J., Du K., Wang H.Y., BDA model for predicting destructive effect of blast vibration on housing, Journal of Vibration and Shock, 29(2010), No. 7, 60-65.
  • [11] Shi X.Z., Zhou J., Cui S., Huang M., Qiu X.Y., Sun L. Distance discriminant analysis model and its application for prediction residential house’s damage against blasting vibration of open pit mining, Journal of Central South University (Science and Technology), 42(2011), No. 2, 441-448.
  • [12] Vapnik V.N., The nature of statistical learning theory. Springer-Verlag, New York, (1995).
  • [13] Gunn S.R., Support vector machines for classification and regression, Technical Report. University of Southampton, (1998).
  • [14] Gopalakrishnan K., Kim S., Support vector machines approach to HMA stiffness prediction, Journal of Engineering Mechanics(ASCE), 137(2011), No. 2, 138-146.
  • [15] Zhou, J., Li, X. B., Shi X. Z., Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines, Safety Science, 50(2012). No. 4, 629-644.
  • [16] Zhou J., Li X. B., Evaluating the thickness of broken rock zone for deep roadways using nonlinear SVM and multiple linear regression models, Procedia Engineering, 26(2011), 972–981.
  • [17] Shi X.Z., Zhou J., Wu B.B., Huang D., Wei W., Support vector machines approach to mean block size of rock fragmentation due to bench blasting prediction, Trans. Nonferrous Met. Soc. China, 22(2012), No. 2, 432-441.
  • [18] Matlab 7.0. program, available from The Mathworks Inc., Natick, MA, http://www.mathworks.com.
  • [19] Chang C.C., Lin C.J., LIBSVM: a library for support vector machines, http: //www.csie.ntu.edu.tw/ ~cjlin/libsvm, (2000).
  • [20] Lin H T, Huang T K. Grid parameter search for regression. http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8f78030b-b8f7-472e-abfa-6e35128db254
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