Browsing by Author "Vijaya Kumar, C.V."

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    Artificial neural network for prediction of rock properties using acoustic frequencies recorded during rock drilling operations
    (Springer Science and Business Media Deutschland GmbH, 2022) Vijaya Kumar, C.V.; Vardhan, H.; Murthy, C.S.N.
    Determining properties of rocks in rock mechanics/engineering applications such as underground tunnelling, slope stability, foundations, dam design and rock blasting is often difficult due to the requirements of high quality of core rock samples and accurate test apparatus. Prediction of the geomechanical properties of rock material has been an area of interest for rock mechanics for several years now. Nowadays, soft computing methods are used as a powerful tool to estimate the rock properties, cost and duration of the project. This has led to a lack of necessity to develop a model to predict rock properties in the field of rock mechanics. ANN (artificial neural network) models were developed to predict geomechanical properties of the sedimentary rock types using dominant frequencies of an acoustic signal during rock drilling operations. A set of experimental drilling operations test conditions around 875 were used as input parameters including drill bit spindle speeds (rpm), drill bit penetration rates (mm/min), drill bit diameters (mm) and dominant frequencies of the acoustic signal (Hz). The response (output) was uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), density (ρ) and abrasivity (%). The developed models were checked using various performance indices. The results from the analysis show that the suggested ANN model approach is efficient, fits the data and accurately reflects the experimental results. The ANN models predicted physico-mechanical rock properties through the dominant frequency of acoustic signals during rock drilling operations. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature.
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    Estimating rock properties using sound signal dominant frequencies during diamond core drilling operations
    (Chinese Academy of Sciences rockgeotech@whrsm.ac.cn, 2019) Vijaya Kumar, C.V.; Vardhan, H.; Murthy, C.S.N.; Karmakar, N.C.
    In many engineering applications such as mining, geotechnical and petroleum industries, drilling operation is widely used. The drilling operation produces sound by-product, which could be helpful for preliminary estimation of the rock properties. Nevertheless, determination of rock properties is very difficult by the conventional methods in terms of high accuracy, and thus it is expensive and time-consuming. In this context, a new technique was developed based on the estimation of rock properties using dominant frequencies from sound pressure level generated during diamond core drilling operations. First, sound pressure level was recorded and sound signals of these sound frequencies were analyzed using fast Fourier transform (FFT). Rock drilling experiments were performed on five different types of rock samples using computer numerical control (CNC) drilling machine BMV 45 T20. Using simple linear regression analysis, mathematical equations were developed for various rock properties, i.e. uniaxial compressive strength, Brazilian tensile strength, density, and dominant frequencies of sound pressure level. The developed models can be utilized at early stage of design to predict rock properties. © 2019 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences