Blasting is still an economical and viable method for rock excavation in mining and civil works projects. Ground vibration generated due to blasting is an undesirable phenomenon which is harmful for the nearby inhabitants and dwellings and should be prevented. In this study, an attempt has been made to predict the blast-induced ground vibration and frequency by incorporating rock properties, blast design and explosive parameters using the general regression neural network (GRNN) technique. To validate this methodology, the predictions obtained were compared with those obtained using the artificial neural network (ANN) model as well as by multivariate regression analysis (MVRA). Among all the methods, GRNN provides excellent predictions with a high degree of correlation.
BakhshandehHASiamakiASoltaniS (2012) Design of blasting pattern in proportion to the peak particle velocity (PPV): Artificial neural networks approach. Safety Science, 50: 1913–1916.
2.
CacoullosT (1966) Estimation of a multivariate density. Annals of the Institute of Statistical Mathematics, 18(2): 179–189.
3.
ChassiakosAGMasriSF (1996) Modeling unknown structural systems through the use of neural networks. Earthquake Engineering and Structural Dynamics, 25(2): 117–128.
4.
CichockiAZdunekR (2007) Multilayer nonnegative matrix factorization using projected gradient approaches. International Journal of Neural Systems, 17(6): 431–446.
5.
ClayGHuagSL(2000) Analysis of ground vibrations caused by open pit production blast. In: HolmberdA (ed.) Explosive and Blasting Technique, Rotterdam: Balkema, pp. 63–70.
6.
de LautourOROmenzetterP (2009) Prediction of seismic-induced structural damage using artificial neural networks. Engineering Structures, 31(2): 600–606.
7.
Dick R, Fletcher LR and D’Andrea DV (1983) Explosives and blasting procedures manual. USBM RI-8925.
8.
Duvall WI and Fogelson DE (1962) Reviews of criteria for estimation damage to residences from blasting vibrations. USBM, RI-5968.
9.
Hagan TN (1973) Rock breakage by explosive, In: National symposium on rock fragmentation. Adelaide, Australia, pp. 1–17.
10.
KamaliMAtaeiM (2011) Prediction of blast-induced vibrations in the structures of Karoun III power plant and dam. Journal of Vibration and Control, 17(4): 541–548.
11.
KhandelwalMKankarPKHarshaSP (2010) Evaluation and prediction of blast-induced ground vibration using support vector machine. Mining Science and Technology, 20: 64–70.
12.
Khandelwal M and Singh TN (2006) Prediction of blast-induced ground vibrations and frequency in opencast mine: A neural network approach. Journal of Sound and Vibration 289(4–5): 711–725.
13.
Khandelwal M and Singh TN (2007) Evaluation of blast-induced ground vibration predictors. Soil Dynamics and Earthquake Engineering 27(2): 116–125.
14.
Kiehl JR and Werfling J (2003) Measurement and analysis of blasting vibrations. In: ISRM 2003-technology roadmap for rock mechanics, SAIMM.
15.
LakshmiSSTiwariRK (2009) Model dissection from earthquake time series: a comparative analysis using modern nonlinear forecasting and artificial neural network approaches. Computers and Geosciences, 35(2): 191–204.
16.
Langefors U and Kihlstrom B (1978) The Modern Technique of Rock Blasting. New York: John Wiley.
17.
Nicholls HR, Johnson CF and Duwall WI (1971) Blasting vibrations and their effects on structures. USBM Bulletin, 656.
18.
ParzenE (1962) On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33: 1065–1076.
19.
PerssonPHolmergRLeeJ (1994) Rock Blasting and Explosive Engineering, New York: CRC Press.
20.
Pugliese JM (1972) Designing Blast Patterns Using Empirical Formulas: A comparison of calculated patterns with plans used in Quarrying Limestone and Dolomite, with Geologic Considerations. US Bureau of Mines, Information circular 8550.
21.
Rosenthal MF and Morlock GL (1987) Blasting guidance manual. US Office of Surface Mining Reclamation and Enforcement.
22.
SamanYSHingHT (2011) A new site classification approach based on neural networks. Soil Dynamics and Earthquake Engineering, 31: 974–981.
23.
SenouciABAdeliH (2001) Resource scheduling using neural dynamics model of Adeli and Park. Journal of Construction Engineering and Management, ASCE, 127(1): 28–34.
24.
SinghRBBagchiASinghB (1993) Blasting in ground excavations and mines, Rotterdam: Balkema.
25.
Singh TN and Singh V (2005) An intelligent approach to prediction and control ground vibration in mines. Geotechnical and Geological Engineering 23(3): 249–262.
26.
Specht DF (1967) Generation of polynomial discriminant functions for pattern recognition. IEEE Transactions on Electronic Computers (EC-16): 308–319.
27.
SpechtDF (1991) A general regression neural network. IEEE Transactions on Neural Network, 2(6): 568–576.
Torano J and Rodriguez R (2003) Simulation for the vibration produced during the rock excavation by different methods. In: XI international conference on computer methods and experimental measures, WIT Press, pp. 343–349.
30.
ToranoJRodriguezRDiegoI (2006) FEM models including randomness and its application to the blasting vibrations prediction. Computers and Geotechnics, 33(2): 121–131.
31.
YangYRosenbaumMS (2002) The artificial neural network as a tool for assessing geotechnical properties. Geotechnical and Geological Engineering, 20: 149–168.
