Using Artificial Neural Network to Predict Blast-induced Ground Vibration

被引：2
|
作者：
Gao, Fuqiang [1 ]
Wang, Xiaoqiang [1 ]
机构：
[1] Luoyang Inst Sci & Technol, Dept Civil Engn, Luoyang, Peoples R China
来源：
PROGRESS IN CIVIL ENGINEERING, PTS 1-4 | 2012年 / 170-173卷
关键词：
Blasting vibration velocity; Artificial neural network; Sodev's predictor; Coefficient of determination; Mean absolute error;
D O I：
10.4028/www.scientific.net/AMM.170-173.1013
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
Prediction of peak particle velocity (PPV) is very complicated due to the number of influencing parameters affecting seism wave propagation. In this paper, artificial neural network (ANN) is implemented to develop a model to predict PPV in a blasting operation. Based on the measured parameters of maximum explosive charge used per delay and distance between blast face to monitoring point, a three-layer ANN was found to be optimum with architecture 2-5-1. Through the analysis of coefficient of determination (CoD) and mean absolute error (MAE) between monitored and predicted values of PPV, it indicates that the forecast data by the ANN model is close to the actual values.
引用
收藏
页码：1013 / 1016
页数：4
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