A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network

被引:93
作者
Zhou, Jian [1 ,2 ]
Aghili, Nasim [3 ,4 ]
Ghaleini, Ebrahim Noroozi [5 ]
Dieu Tien Bui [6 ,7 ]
Tahir, M. M. [8 ]
Koopialipoor, Mohammadreza [9 ]
机构
[1] Cent South Univ, Sch Resources & Safety Engn, Changsha 410083, Peoples R China
[2] State Key Lab Safety & Hlth Met Mines, Maanshan 243000, Peoples R China
[3] Univ Sains Malaysia, Sch Housing Bldg & Planning, George Town 11800, Malaysia
[4] Univ Teknol Malaysia, Fac Geoinformat & Real Estate, Dept Real Estate, Johor Baharu 81310, Johor, Malaysia
[5] Amirkabir Univ Technol, Fac Min & Met, Tehran, Iran
[6] Ton Duc Thang Univ, Geog Informat Sci Res Grp, Ho Chi Minh City, Vietnam
[7] Ton Duc Thang Univ, Fac Environm & Labour Safety, Ho Chi Minh City, Vietnam
[8] Univ Teknol Malaysia, Construct Res Ctr, ISIIC, Fac Civil Engn, Johor Baharu 81310, Johor, Malaysia
[9] Amirkabir Univ Technol, Fac Civil & Environm Engn, Tehran 15914, Iran
关键词
Monte Carlo simulation; Flyrock phenomenon; ANN; Risk assessment; Sensitivity analysis; GROUND VIBRATION PREDICTION; SHEAR-STRENGTH PREDICTION; FUZZY MODELING APPROACH; ROCK FRAGMENTATION; INFERENCE SYSTEM; OPTIMIZATION; MACHINE; DESIGN; PARAMETERS; ALGORITHM;
D O I
10.1007/s00366-019-00726-z
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
One of the undesirable phenomena in the surface mines, which results in various hazards for human and facilities, is flyrock. It seems that the careful study of the subject and its effects on the environment can affect the control of flyrock hazards in the studied area. Therefore, the use of intelligent models and methods which are capable of predicting and simulating the risk of flyrock can be considered as an appropriate solution in this regard. The current research was conducted using nonlinear models and Monte Carlo (MC) simulation. The data used in this study consist of 260 samples of rock thrown from a mine in Malaysia. The parameters used in these models include hole's diameter (D), hole's depth (HD), burden to spacing (BS), stemming (ST), maximum charge per delay (MC), and powder factor (PF). At first, multiple regression analysis (MRA) and artificial neural network (ANN) models were used in order to develop a non-linear relationship between dependent and independent parameters. The ANN model was an appropriate predictor of flyrock in the mine. Then using the best implemented model of ANN, the flyrock environmental phenomenon was simulated using MC technique. MC simulation showed a proper level of accuracy of flyrock ranges in the mine. Using this simulation, it can be concluded with 90% accuracy that the Flyrock phenomenon does not exceed 331 m. Under these conditions, this simulation can be used for various areas requiring risk assessment. Finally, a sensitive analysis was carried out on data. This analysis showed MC has the greatest effect on flyrock.
引用
收藏
页码:713 / 723
页数:11
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