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Prediction of flyrock in open pit blasting operation using machine learning method 被引量:12
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作者 manoj Khandelwal m. monjezi 《International Journal of Mining Science and Technology》 SCIE EI 2013年第3期313-316,共4页
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. ... 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. 展开更多
关键词 Blasting Soungun Copper Mine Flyrock Support vector machine MVRA
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Evaluation of boring machine performance with special reference to geomechanical characteristics 被引量:1
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作者 K. Goshtasbi m. monjezi P. Tourgoli 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2009年第6期615-619,共5页
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 geomechanic... 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 geomechanical properties of rock mass encountered in the Isfahan metro tunnel, the performance of the roadheader and tunnel boring machine (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 m^3/h respectively. However the actual values in practice are 34.2 and 51.3 m^3/h. The operational cutting rate of the roadheader in sandstone and shale are 8.2 and 9.7 m^3/h respectively, but the actual values are 6.5 and 6.7 m^3/h. The penetration rate of the TBM in shale is predicted to be 50-60 mm/round. 展开更多
关键词 performance prediction ROADHEADER cutting rate specific energy tunnel boring machine penetration rate
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