Predicting blast-induced ground vibration using various types of neural networks

被引：121

作者：

Monjezi, M. ^{[1
]}

Ahmadi, M. ^{[2
]}

Sheikhan, M. ^{[2
]}

Bahrami, A. ^{[1
]}

Salimi, A. R. ^{[2
]}

机构：

[1] Tarbiat Modares Univ, Fac Engn, Tehran, Iran

[2] Islamic Azad Univ, Tehran S Branch, Tehran, Iran

来源：

SOIL DYNAMICS AND EARTHQUAKE ENGINEERING | 2010年 / 30卷 / 11期

关键词：

D O I：

10.1016/j.soildyn.2010.05.005

中图分类号：

P5 [地质学];

学科分类号：

0709 ; 081803 ;

摘要：

Prediction of vibration is very important in mining operations as well as civil engineering projects In this paper, multi layer perceptron neural network (MLPNN), radial basis function neural network (RBFNN) and general regression neural network (GRNN) were utilized to predict ground vibration level in a Sarcheshmeh copper mine, Iran It was observed that the MLPNN gives the best results. For this technique root mean square error and coefficient of correlation were found 0.03 and 0954, respectively. Sensitivity analysis showed that distance from the blast, number of holes per delay and maximum charge per delay are the most effective parameters in making ground vibration in the blasting operation (C) 2010 Elsevier Ltd All rights reserved

引用

页码：1233 / 1236

页数：4

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