Please use this identifier to cite or link to this item:
Title: Prediction of peak particle velocity using multi regression analysis: case studies
Authors: Ram Chandar, K.
Sastry, V.R.
Hegde, C.
Shreedharan, S.
Issue Date: 2017
Citation: Geomechanics and Geoengineering, 2017, Vol.12, 3, pp.207-214
Abstract: Ground vibrations produced from blasting operations cause structural vibrations, which may weaken structure if it occurs at the resonant frequency. Measurable parameters associated with ground vibrations are peak particle velocity (PPV), amplitude and dominant frequency (frequency of highest PPV amongst translational, vertical and horizontal vibrations). In this paper, an attempt is made to correlate measurable parameters associated with ground vibrations with scaled distance. Using the correlated data, it was found that a predictor equation can be determined for the amplitude and PPV, but not for dominant frequency as it is dynamic and depends upon infinitesimal changes that occur within a number of other parameters. Another analysis of the same is made using multiple linear regression analysis. This included predicting the PPV using scaled distance, maximum charge per delay, amplitude as predictors. A considerable improvement is seen in the prediction on adding the interaction of the predictors in multiple regressions. A comparison of different combination of predictors is made so as to assess the best combination giving the best R2 value for the given mine. Frequency is also plotted using the aforementioned method. However, it was found that the dominant frequency cannot be predicted with high accuracy even with this method. 2016 Informa UK Limited, trading as Taylor & Francis Group.
Appears in Collections:1. Journal Articles

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.