Application of Artificial Neural Networks for the Prediction of the Intensity of Ground Vibration at the Veliki Krivelj Copper Mine

被引:0
|
作者
Radisavljevic, J. [1 ]
机构
[1] Serbia Zijin Copper DOO, Bor 19210, Serbia
关键词
blasting; ground vibration; peak particle velocity; ANN; BLAST-INDUCED VIBRATIONS; PEAK PARTICLE-VELOCITY; MODEL; FEASIBILITY;
D O I
10.1134/S1062739123020047
中图分类号
TD [矿业工程];
学科分类号
0819 ;
摘要
This article presents an artificial neural network (ANN)-based mathematical model for the prediction of the intensity of ground vibration at the Veliki Krivelj copper mine. The starting points for the development of the model are the model of ground vibration, the software package Peltarion Synapse, as a basis, using artificial neural networks ANN and input-output data set of blasted patterns at the Veliki Krivelj open pit. The input-output set contains the values of the blasting parameters of individual blasting patterns and the measured peak particle velocities when blasting those patterns. The advantage of the ANN method was confirmed by comparing the results of predicting the particle velocity obtained by different methods.
引用
收藏
页码:211 / 224
页数:14
相关论文
共 50 条
  • [1] Application of Artificial Neural Networks for the Prediction of the Intensity of Ground Vibration at the Veliki Krivelj Copper Mine
    J. Radisavljevic
    Journal of Mining Science, 2023, 59 : 211 - 224
  • [2] Prediction of blast-induced ground vibration using artificial neural networks
    Monjezi, M.
    Ghafurikalajahi, M.
    Bahrami, A.
    TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY, 2011, 26 (01) : 46 - 50
  • [3] Application of artificial neural networks to ground settlements
    Yoon, YW
    Kang, BH
    Kang, TH
    SOIL MECHANICS AND GEOTECHNICAL ENGINEERING, VOL 1: ELEVENTH ASIAN REGIONAL CONFERENCE, 1999, : 451 - 454
  • [4] Prediction of rock fragmentation due to blasting in Sarcheshmeh copper mine using artificial neural networks
    Monjezi M.
    Amiri H.
    Farrokhi A.
    Goshtasbi K.
    Geotechnical and Geological Engineering, 2010, 28 (04) : 423 - 430
  • [5] Prediction of Ground Motion Intensity Measures Using an Artificial Neural Network
    Sreejaya, K. P.
    Basu, Jahnabi
    Raghukanth, S. T. G.
    Srinagesh, D.
    PURE AND APPLIED GEOPHYSICS, 2021, 178 (06) : 2025 - 2058
  • [6] Prediction of Ground Motion Intensity Measures Using an Artificial Neural Network
    K. P. Sreejaya
    Jahnabi Basu
    S. T. G. Raghukanth
    D. Srinagesh
    Pure and Applied Geophysics, 2021, 178 : 2025 - 2058
  • [7] Study on artificial neural network method for ground subsidence prediction of metal mine
    Zhao, Kang
    Chen, Si-ni
    SECOND INTERNATIONAL CONFERENCE ON MINING ENGINEERING AND METALLURGICAL TECHNOLOGY (MEMT 2011), 2011, 2 : 177 - 182
  • [8] Application Research of Genetic Algorithm and Artificial Neural Networks in the Prediction of Mine Water Gushing-out
    Dong Lili
    Qiao Yufeng
    Guo Xiaoshan
    2010 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE AND ENGINEERING (MSE 2010), VOL 3, 2010, : 164 - 168
  • [9] Application of Artificial Neural Networks for Prediction of Learning Performances
    Dharmasaroja, Permphan
    Kingkaew, Nicha
    2016 12TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD), 2016, : 745 - 751
  • [10] Application of artificial neural networks for prediction of concrete properties
    Abdulla, N.
    MAGAZINE OF CIVIL ENGINEERING, 2022, 110 (02):