Prediction of blast-induced vibrations in limestone quarries using Support Vector Machine

One of the major environmental concerns related to blasting operation in mining and civil engineering projects is ground vibration. The ground parameters should be taken into account by the prediction models, especially if the ground conditions have variable characters. In a blasting environment, this is usually possible by using an empirical method. However, in this study, the application of a novel artificial method, called a ‘Support Vector Machine’ (SVM), has been offered for the prediction of blast-induced ground vibration by taking into consideration the maximum charge per delay and the distance between the blast face and monitoring point. Two limestone quarries have been studied through this research. The results clearly show that the SVM can be used as a reliable predictor technique to predict the vibration level with a correlation coefficient of 0.944 which has been obtained by comparing measured and predicted values.

[1]  Hai-Xin Duan,et al.  Efficient performance estimate for one-class support vector machine , 2005, Pattern Recognit. Lett..

[2]  Manoj Khandelwal,et al.  Evaluation and prediction of blast induced ground vibration using support vector machine , 2010 .

[3]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[4]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[5]  Ali Kahriman,et al.  Analysis of parameters of ground vibration produced from bench blasting at a limestone quarry , 2004 .

[6]  Harish Karnick,et al.  Kernel-based online machine learning and support vector reduction , 2008, ESANN.

[7]  Dimitri P. Solomatine,et al.  Model Induction with Support Vector Machines: Introduction and Applications , 2001 .

[8]  H. S. Venkatesh,et al.  Influence of total charge in a blast on the intensity of ground vibrations—field experiment and computer simulation , 2005 .

[9]  A. V.DavidSánchez,et al.  Advanced support vector machines and kernel methods , 2003, Neurocomputing.

[10]  J. R. Thoenen,et al.  Seismic Effects of Quarry Blasting , 1942 .

[11]  T. N. Singh,et al.  An intelligent approach to prediction and control ground vibration in mines , 2005 .

[12]  D. E. Fogelson,et al.  Review of criteria for estimating damage to residences from blasting vibrations , 1962 .

[13]  R Holmberg.  20 – Drilling and blasting of rock masses , 1992 .

[14]  Qing Li,et al.  Adaptive simplification of solution for support vector machine , 2007, Pattern Recognit..

[15]  Gary J. Koehler,et al.  Using domain-specific knowledge in generalization error bounds for support vector machine learning , 2009, Decis. Support Syst..

[16]  Giuseppe Jurman,et al.  Terminated Ramp-Support Vector Machines: A nonparametric data dependent kernel , 2006, Neural Networks.

[17]  Massimiliano Pontil,et al.  Support Vector Machines: Theory and Applications , 2001, Machine Learning and Its Applications.

[18]  R. Rai,et al.  Prediction of maximum safe charge per delay in surface mining , 2005 .

[19]  T. Hagan.  Rock breakage by explosives , 1980 .

[20]  M. Hood,et al.  A state-of-the-art review of mechanical rock excavation technologies , 2010 .

[21]  Maziar Palhang,et al.  Generalization performance of support vector machines and neural networks in runoff modeling , 2009, Expert Syst. Appl..

[22]  Dawei Han,et al.  SUPPORT VECTOR MACHINES IDENTIFICATION FOR RUNOFF MODELLING , 2004 .

[23]  Jih Pin Yeh,et al.  Optimal reduction of solutions for support vector machines , 2009, Appl. Math. Comput..

[24]  Chih-Hung Wu,et al.  A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression , 2009, Expert Syst. Appl..

[25]  Giorgio Berta,et al.  Blasting-induced vibration in tunnelling , 1994 .

[26]  C. Dowding.  Blast vibration monitoring and control , 2004 .

[27]  M. T. Mohamed,et al.  Artificial neural network for prediction and control of blasting vibrations in Assiut (Egypt) limestone quarry , 2009 .

[28]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[29]  D. Siskind,et al.  STRUCTURE RESPONSE AND DAMAGE PRODUCED BY GROUND VIBRATION FROM SURFACE MINE BLASTING. , 1980 .

[30]  H. Ergin,et al.  An assessment of environmental impacts of quarry-blasting operation: a case study in Istanbul, Turkey , 2005 .