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| 1 | Prediction of flyrock in open pit blasting operation using machine learning method显示文摘Flyrock is one of the most hazardous events in blasting operation of surface mines. There are several empirical methods to predict flyrock. Low performance of such models is due to the complexity of flyrock analysis. Existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, the application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict flyrock in blasting operations of Soungun Copper Mine, Iran incorporating rock properties and blast design parameters using support vector machine (SVM) method. To investigate the suitability of this approach, the predictions by SVM have been compared with multivariate regression analysis (MVRA), too. Coefficient of determination (CoD) and mean absolute error (MAE) were taken as performance measures. It was found that CoD between measured and predicted flyrock was 0.948 and 0.440 by SVM and MVRA, respectively, whereas MAE between measured and predicted flyrock was 3.11 and 7.74 by SVM and MVRA, respectively. | Manoj Khandelwal M. Monjezi | 2013 | International Journal of Mining Science and Technology2013，23，3： | 5 |
| 2 | Environmental impact assessment of open pit mining in Iran显示文摘 | M. Monjezi K. Shahriar H. Dehghani F. Samimi Namin | 2009 | Environmental Geology2009，，1： | 1 |
| 3 | Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network显示文摘 | Masoud Monjezi Mahdi Hasanipanah Manoj Khandelwal | 2013 | Neural Computing and Applications2013，，7： | 1 |
| 4 | Prediction of rock fragmentation due to blasting in Sarcheshmeh copper mine using artificial neural networks 显示文摘 | MONJEZI M AMIRI H FARROKHI A GOSHTASBI K | 2010 | Geotechnical and Geological Engineering2010，28，： | 1 |
| 5 | Simultaneous prediction of fragmentation and flyrock in blasting opera- tion using artificial neural networks 显示文摘 | M Monjezi A Bahrami A Yazdian Varjani | 2010 | International Journal of Rock Mechanics & Mining Sciences2010，，47： | 1 |
| 6 | Evaluation of boring machine performance with special reference to geomechanical characteristics显示文摘The duration of tunneling projects mostly depends on the performance of boring machines. The performance of boring machines is a function of advance rate, which depends on the machine characterizations and geomechanical properties of rock mass. There were various theoretical and empirical models for estimating the advance rate. In this paper, after determining the geome-chanical properties of rock mass encountered in the Isfahan metro tunnel, the performance of the roadheader and tunnel boring ma-chine (TBM) were then evaluated using various models. The calculation results show that the average instantaneous cutting rate of the roadheader in sandstone and shale are 42.8 and 74.5 m3/h respectively. However the actual values in practice are 34.2 and 51.3 m3/h. The operational cutting rate of the roadheader in sandstone and shale are 8.2 and 9.7 m3/h respectively, but the actual values are 6.5 and 6.7 m3/h. The penetration rate of the TBM in shale is predicted to be 50-60 mm/round. | K. Goshtasbi M. Monjezi P. Tourgoli | 2009 | International Journal of Minerals,Metallurgy and Materials2009，16，6： | 1 |
| 7 | Prediction of blast-induced ground vibration using artificial neural networks显示文摘 | M. Monjezi M. Ghafurikalajahi A. Bahrami | 2010 | Tunnelling and Underground Space Technology incorporating Trenchless Technology Research2010，，1： | 1 |
| 8 | Superiori- ty of neural networks for pillar stress prediction in bord and pillar method显示文摘 | MONJEZI SMH M KHANDELWAL M | 2011 | Arab J Geosci2011，4，： | 1 |
| 9 | Prediction and analysis of blast parameters using artificial neural network显示文摘 | M. Monjezi T.N. Singh Manoj Khandelwal Shivam Sinha Vishal Singh I. Hosseini | 2009 | Noise & Vibration Worldwide2009，，5： | 1 |
| 10 | Prediction of rock frag- mentation due to blasting using artificial neural network 显示文摘 | Bahrami A Monjezi M Goshtasbi K | 2011 | Engineer- ing with Computers2011，27，2： | 1 |
| 11 | Prediction of Rock Fragmentation Due to Blasting in Sarcheshmeh Copper Mine Using Artificial Neural Networks 显示文摘 | Monjezi M Amifi H Farrokhi A | 2010 | Geotechnical and Geological Engineering2010，28，4： | 1 |
| 12 | Prediction of rock fragmentation due to blasting in Gol-E-Gohar iron mine using fuzzy logic 显示文摘 | MONJEZI M REZAEI M VARJANI A Y | 2009 | International Journal of Rock Mechanics & Mining Sciences2009，46，： | 1 |
| 13 | Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks显示文摘 | M. Monjezi A. Bahrami A. Yazdian Varjani | 2009 | International Journal of Rock Mechanics and Mining Sciences2009，，3： | 1 |
| 14 | Developing a new fuzzy model to predict burden from rock geomechanical properties显示文摘 | MONJEZI M REZAEI M | 2011 | Expert Systems with Applications2011，38，8： | 1 |
| 15 | Prediction of Backbreak in Open-Pit Blasting Operations Using the Machine Learning Method显示文摘 | Manoj Khandelwal M. Monjezi | 2013 | Rock Mechanics and Rock Engineering2013，，2： | 1 |
| 16 | Detection of hepatitis B virus core antigen by phage display mediated TaqMan real-time immuno- PCR显示文摘 | Monjezi R Tan SW Tey BT | 2013 | J Virol Methods2013，187，1： | 1 |
| 17 | Appli- cation of neural networks to predict net present value in mining projects显示文摘 | Sayadi A R Tavassoli S M M Monjezi M | 2014 | Arabian Journal of Geosciences2014，7，3： | 1 |
| 18 | E- mergence of SCCmec type III with variable antimicrobial resistance profiles and spa types among methicillin-resist- ant Staphylococcus aureus isolated from healthcare- and community-acquired infections in the west of lran 显示文摘 | MOHAMMADI S SEKAWI Z MONJEZI A | 2014 | International Journal of Infectious Diseases2014，，25： | 1 |
| 19 | Comparison between Lagrangian and Eulerian approaches in predicting motion of micron-sized particles in laminar flows显示文摘 | SAIDI M S RISMANIANA M MONJEZI M | 2014 | Atmospheric Environment2014，89，6： | 1 |
| 20 | Prediction and controlling of flyrock in blasting operation using artificial neural network 显示文摘 | Monjezi M Bahrami A Varjani A Y | 2011 | Arabian Journal of Geosciences2011，，4： | 1 |
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