Using Artificial Neural Network to Predict Blast-Induced Ground Vibration

Fu Qiang Gao; Xiao Qiang Wang

doi:10.4028/www.scientific.net/AMM.170-173.1013

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Study on the Stratum Displacement Changes Caused by the Vertical Shaft Construction in Saturated Ultra Soft Soil
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 170-173Using Artificial Neural Network to Predict...

Using Artificial Neural Network to Predict Blast-Induced Ground Vibration

Abstract:

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 actua1 values.

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Periodical:

Applied Mechanics and Materials (Volumes 170-173)

Pages:

1013-1016

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.170-173.1013

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Online since:

May 2012

Authors:

Fu Qiang Gao, Xiao Qiang Wang

Keywords:

Artificial Neural Network (ANN), Blasting Vibration Velocity, Coefficient of Determination, Mean Absolute Error (MAE), Sodev’s Predictor

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References

[1] T. N Hagan: Rock Breakage by Explosives. In: Proceedings of National Symposium on Rock Fragmentation, Adelaide, pp.1-17 (1973)

Google Scholar

[2] M. Khandelwal, D. Lalit, M. Yellishetty: Application of Soft Computing to Predict Blast-induced Ground Vibration. Engineering with Computers. 27, 117-125 (2011)

DOI: 10.1007/s00366-009-0157-y

Google Scholar

[3] A. K Verma, T. N Singh: Intelligent Systems for Ground Vibration Measurement: a Comparative Study. Engineering with Computers. 27, 225-233 (2011)

DOI: 10.1007/s00366-010-0193-7

Google Scholar

[4] Singh, T.N., Kanchan, R., Saigal, K., Verma, A.K.: Prediction of P-wave Velocity and Anisotropic Properties of Rock Using Artificial Neural Networks Technique. J. Sci. Ind. Res. 63, 32-38 (2004)

Google Scholar

[5] T. N. Singh, R. Kanchan, A. K. Verma: Prediction of Blast Induced Ground Vibration and Frequency Using an Artificial Intelligence Technique. Int. J. Noise Vibration Worldwise Multi Sci Publ UK. 35, 7-14 (2004)

DOI: 10.1260/0957456042880192

Google Scholar

[6] M. Rezaei, M. Monjezi, S. G. Moghaddam: Farzaneh, F.: Burden Prediction in Blasting Operation Using Rock Geomechanical Properties. Arab. J. Geosci. (2011)

DOI: 10.1007/s12517-010-0269-0

Google Scholar

[7] A. Bahrami, M. Monjezi, K. Goshtasbi: Prediction of Rock Fragmentation Due to Blasting Using Artificial Neural Network. Engineering with Computers. 27, 177-181 (2011)

DOI: 10.1007/s00366-010-0187-5

Google Scholar

[8] I. Baheer: Selection of Methodology for Modeling Hysteresis Behavior of Soils Using Neural Networks. J. Comput Aided Civ. Infrastruct Eng. Vol. 6, pp.445-463 (2000)

Google Scholar

[9] R. Hecht-Nielsen: Kolmogorov's Mapping Neural Network Existence Theorem. In: Proceedings of First IEEE International Conference on Neural Network, San Diego, pp.11-14 (1987)

Google Scholar

[10] T. Masters: Practical Neural Network Recipes in C++. Academic Press, Boston (1994)

Google Scholar

[11] R. P. Lippmann: An Introduction to Computing with Neural Nets. In: IEEE ASSP Mag 4, pp.4-22. IEEE Press, New York (1987)

DOI: 10.1109/massp.1987.1165576

Google Scholar

[12] D. E. Rumelhart, G. E. Hinton, R. J. Williams: Learning Internal Representation by Error Propogation. In: Parallel Distributed Processing, vol. 1, pp.318-362 (1986)

Google Scholar