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ANN Models for Prediction of Sound and Penetration Rate in Percussive Drilling

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Abstract

In the recent years, new techniques such as; Artificial Neural Network (ANN) were employed for developing of the predictive models to estimate the needed parameters. Soft computing techniques are now being used as alternate statistical tool. In this study, ANN models were developed to predict rock properties of sedimentary rock, by using penetration and sound level produced during percussive drilling. The data generated in the laboratory investigation was utilized for the development of ANN models for predicting rock properties like, uniaxial compressive strength, abrasivity, tensile strength, and Schmidt rebound number using air pressure, thrust, bit diameter, penetration rate and sound level. Further, ANN models were also developed for predicting penetration rate and sound level using air pressure, thrust, bit diameter and rock properties as input parameters. The constructed models were checked using various prediction performance indices. ANN models were more acceptable for predicting rock properties.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Basavakalyan Engineering College, Basavakalyan, 585327, India

    Sangshetty B. Kivade (Professor and Head)

  2. Department of Mining Engineering, NITK, Suratkal, 575025, India

    Chivukula Surya Naryana Murthy (Professor) & Harsha Vardhan (Associate Professor)

Authors
  1. Sangshetty B. Kivade
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  2. Chivukula Surya Naryana Murthy
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  3. Harsha Vardhan
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Correspondence to Sangshetty B. Kivade.

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Kivade, S.B., Murthy, C.S.N. & Vardhan, H. ANN Models for Prediction of Sound and Penetration Rate in Percussive Drilling. J. Inst. Eng. India Ser. D 96, 93–103 (2015). https://doi.org/10.1007/s40033-015-0067-7

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  • DOI: https://doi.org/10.1007/s40033-015-0067-7

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