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

Mining Science and Technology 2010年第1期20卷 64-70页

作者： kHANDELWAL M kankar p k HARSHA S p Department of Mining Engineering College of Technology & Engineering Maharana Pratap University of Agriculture & Technology Udaipur 313001 India Department of Mechanical & Industrial Engineering Indian Institute of Technology Roorkee 247667 India

We present the application of Support Vector Machine (SVM) for the prediction of blast induced ground vibration by taking into consideration of maximum charge per delay and distance between blast face to monitoring point. To investigate the suitability of this approach, the predictions by SVM have been compared with conventional predictor equations. Blast vibration study was carried out at Magnesite mine of pithoragarh, India. Total 170 blast vibrations data sets were recorded at different strate-gic and vulnerable locations in and around to mine. Out of 170 data sets, 150 were used for the training of the SVM network as well as to determine site constants of different conventional predictor equations, whereas, 20 new randomly selected data sets were used to compare the prediction capability of SVM network with conventional predictor equations. Results were compared based on Co-efficient of Determination (CoD) and Mean Absolute Error (MAE) between monitored and predicted values of peak particle Veloc-ity (ppV). It was found that SVM gives closer values of predicted ppV as compared to conventional predictor equations. The coef-ficient of determination between measured and predicted ppV by SVM was 0.955, whereas it was 0.262, 0.163, 0.337 and 0.232 by USBM, Langefors-kihlstrom, Ambraseys-Hendron and Bureau of Indian Standard equations, respectively. The MAE for ppV was 11.13 by SVM, whereas it was 0.973, 1.088, 0.939 and 1.292 by USBM, Langefors-kihlstrom, Ambraseys-Hendron and Bureau of Indian Standard equations respectively.

关键词： blast vibration support vector machine generalised predictor equations

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