Browsing by Author "Shreedharan, S."

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Acoustic fingerprinting for rock identification during drilling
(2014) Shreedharan, S.; Hegde, C.; Sharma, S.; Vardhan, H.
During the process of mining, it is imperative to know the type and properties of the rocks being handled. The current technology for this involves core drilling, and subsequently subjecting the drilled cores to various tests in the laboratory, to identify the rocks and establish their properties. In many cases, obtaining a sample may be cumbersome and/or non-profitable. This paper presents a novel method to monitor and evaluate the sounds produced as undesirable by-products, at the drill-bit and rock interface, to predict the type of rock being drilled. A rotary drill was fabricated in the laboratory and vertical drilling was carried out on cubical rock samples, keeping various drilling parameters constant. The results obtained are promising and reinforce that it may be possible to extend the proposed methodology in the field as well, with appropriate modifications. This method may be extrapolated further in the estimation of rock properties as well. Copyright 2014 Inderscience Enterprises Ltd.
Acoustic fingerprinting for rock identification during drilling
(Inderscience Publishers, 2014) Shreedharan, S.; Hegde, C.; Sharma, S.; Vardhan, H.
During the process of mining, it is imperative to know the type and properties of the rocks being handled. The current technology for this involves core drilling, and subsequently subjecting the drilled cores to various tests in the laboratory, to identify the rocks and establish their properties. In many cases, obtaining a sample may be cumbersome and/or non-profitable. This paper presents a novel method to monitor and evaluate the sounds produced as undesirable by-products, at the drill-bit and rock interface, to predict the type of rock being drilled. A rotary drill was fabricated in the laboratory and vertical drilling was carried out on cubical rock samples, keeping various drilling parameters constant. The results obtained are promising and reinforce that it may be possible to extend the proposed methodology in the field as well, with appropriate modifications. This method may be extrapolated further in the estimation of rock properties as well. Copyright © 2014 Inderscience Enterprises Ltd.
Prediction of peak particle velocity using multi regression analysis: case studies
(2017) Ram Chandar, K.; Sastry, V.R.; Hegde, C.; Shreedharan, S.
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.
Prediction of peak particle velocity using multi regression analysis: case studies
(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2017) Ram Chandar, K.; Sastry, V.R.; Hegde, C.; Shreedharan, S.
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.