Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/9581
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dc.contributor.authorRam Chandar, K.-
dc.contributor.authorSastry, V.R.-
dc.contributor.authorHegde, C.-
dc.date.accessioned2020-03-31T06:51:10Z-
dc.date.available2020-03-31T06:51:10Z-
dc.date.issued2017-
dc.identifier.citationGeotechnical and Geological Engineering, 2017, Vol.35, 2, pp.573-583en_US
dc.identifier.uri10.1007/s10706-016-0126-3-
dc.identifier.urihttps://idr.nitk.ac.in/jspui/handle/123456789/9581-
dc.description.abstractBlasting 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.en_US
dc.titleA Critical Comparison of Regression Models and Artificial Neural Networks to Predict Ground Vibrationsen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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