Prediction of blast-induced ground vibration using artificial neural network

• Published in: International journal of rock mechanics and mining sciences (Oxford, England : 1997) Vol. 46; no. 7; pp. 1214 - 1222
• Main Authors: Khandelwal, Manoj, Singh, T.N.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-10-2009; Elsevier
• Subjects: Applied sciences; Artificial neural network; Back propagation; Blast vibration; Buildings. Public works; Computation methods. Tables. Charts; Conventional predictors; Exact sciences and technology; Frequency; Geotechnics; Multivariate regression analysis; PPV; Soil mechanics. Rocks mechanics; Structural analysis. Stresses; PPV; Back propagation; Blast vibration; Frequency; Conventional predictors; Artificial neural network; Multivariate regression analysis; Rock mechanics; Network architecture; Explosions; Regression analysis; Neural network; Multivariate analysis; Forecast model; Vibration effect; Blast wave; Comparative study
• ISSN: 1365-1609; 1873-4545
• DOI: 10.1016/j.ijrmms.2009.03.004

An attempt has been made to evaluate and predict the blast-induced ground vibration and frequency by incorporating rock properties, blast design and explosive parameters using the artificial neural network (ANN) technique. A three-layer, feed-forward back-propagation neural network having 15 hidden neurons, 10 input parameters and two output parameters were trained using 154 experimental and monitored blast records from one of the major producing surface coal mines in India. Twenty new blast data sets were used for the validation and comparison of the peak particle velocity (PPV) and frequency by ANN and other predictors. To develop more confidence in the proposed method, same data sets have also been used for the prediction of PPV by commonly used vibration predictors as well as by multivariate regression analysis (MVRA). Results were compared based on correlation and mean absolute error (MAE) between monitored and predicted values of PPV and frequency.