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Item Explosive energy is the most commonly used form of energy to fragment rock mass/overburden and mineral deposits in the mines. Fragmentation obtained in the blasting process influences the downstream costs like loading cost, transportation cost, processing cost, etc. Among the various factors which influence the rock fragmentation, initiation system is one of the most important because presently much research is going on in this area of rock blasting. Some field studies were taken up with conventional detonating cord initiation and shock-tube-based NONEL initiation systems to study the influence of initiation systems on rock fragmentation. Fragmentation analysis was done using the boulder count method and image analysis. It was found that the shock-tube initiation gives 33% less boulders and 31% lesser K50 value compared to detonating cord initiation. © 2004 Taylor & Francis Ltd.(Taylor and Francis Ltd., Shock tube initiation for better fragmentation: A case study) Sastry, V.R.; Ram Chandar, K.2004Item A Critical Comparison of Regression Models and Artificial Neural Networks to Predict Ground Vibrations(Springer International Publishing, 2017) Ram Chandar, K.; Sastry, V.R.; Hegde, C.Blasting is important and an essential prerequisite in any opencast mine for fragmenting hard deposits. Blasting always produces unwanted effects like ground vibrations, noise and fly rock; among which ground vibrations effect is more on surrounding structures. Propagation of ground vibrations can lead to destruction of surrounding structures. Prediction of ground vibrations especially in terms of peak particle velocity is beneficial as opposed to conventional data monitoring techniques which can be expensive as well as time consuming. This paper uses predictors to estimate the intensity of ground vibrations and compares different methods of prediction methods like linear regression, multiple linear regression, non linear regression (NLR) and artificial neural networks. Intensity of ground vibrations generated from blasting operations was monitored in three different mines of limestone, dolomite and coal; obtaining about 168 ground vibration recordings in total. The statistical modelling or data-driven modeling has shown promise in the prediction of blast vibrations. Proposed a system of introducing site specific rock parameters like poison’s ratio, uniaxial compressive strength of rock and Young’s modulus to improve the correlation coefficient using statistical modelling (commonly called feature engineering in machine learning circles). © 2016, Springer International Publishing Switzerland.