Prediction of blast induced ground vibrations and frequency in opencast mine: A neural network approach

被引：232

作者：

Khandelwal, M ^{[1
]}

Singh, TN ^{[1
]}

机构：

[1] Indian Inst Technol, Dept Earth Sci, Bombay 400076, Maharashtra, India

来源：

JOURNAL OF SOUND AND VIBRATION | 2006年 / 289卷 / 4-5期

关键词：

D O I：

10.1016/j.jsv.2005.02.044

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

This paper presents the application of neural network for the prediction of ground vibration and frequency by all possible influencing parameters of rock mass, explosive characteristics and blast design. To investigate the appropriateness of this approach, the predictions by ANN is also compared with conventional statistical relation. Network is trained by 150 dataset with 458 epochs and tested it by 20 dataset. The correlation coefficient determined by ANN is 0.9994 and 0.9868 for peak particle velocity (PPV) and frequency while correlation coefficient by statistical analysis is 0.4971 and 0.0356. (c) 2005 Elsevier Ltd. All rights reserved.

引用

页码：711 / 725

页数：15

共 30 条

[11] Khandelwal M., 2004, Ind. Min. Eng. J., V43, P19
[12] KHANDELWAL M, 2002, THESIS BANARAS HINDU
[13] Khandelwal M, 2002, NAT S NEW EQ NEW TEC, P38
[14] Langefors U, 1958, WATER POWER
[15] A PRACTICAL BAYESIAN FRAMEWORK FOR BACKPROPAGATION NETWORKS
MACKAY, DJC
[J]. NEURAL COMPUTATION, 1992, 4 (03) : 448 - 472
[16] Damage assessment in structure from changes in static parameter using neural networks
Maity, D
Saha, A
[J]. SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, 2004, 29 (3): : 315 - 327
[17] MCKENZIE C, 1990, QUARRY MANAGE, P23
[18] Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness
Meulenkamp, F
Grima, MA
[J]. INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES, 1999, 36 (01): : 29 - 39
[19] *ND STAND I, 1973, ISI B
[20] SARMA SK, 1982, THESIS BANARAS HINDU

← 1 2 3 →