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Neural Tree for Estimating the Uniaxial Compressive Strength of Rock Materials

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Book cover Hybrid Intelligent Systems (HIS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 734))

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Abstract

Uniaxial Compressive Strength (UCS) is the most important parameter that quantifies the rock strength. However, determination of the UCS in laboratory is very expensive and time-consuming. Therefore, common index tests like point load (Is-50), ultrasonic velocity test (Vp), block punch index (BPI) test, rebound hardness (SRH) test, physical properties have been used to predict the UCS. The objective of this work is to develop a predictive model using a neural tree predictor that estimates the UCS with high accuracy and assess the effectiveness of different index tests in predicting the UCS of rock materials. UCS and indices such as BPI, Is-50, SRH, Vp, effective porosity and density were determined for the granite, schist, and sandstone. The constructed model predicted the UCS with a high accuracy and in a quick time (9 s). Additionally, the destructive mechanical rock indices BPI and Is-50 proved to be the best index tests to estimate the UCS.

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Author information

Authors and Affiliations

  1. ETH Zürich, Zürich, Switzerland

    Varun Kumar Ojha

  2. Indian Institute of Petroleum and Energy, Visakhapatnam, India

    Deepak Amban Mishra

  3. Institute of Geonics of CAS, Ostrava-Poruba, Czech Republic

    Deepak Amban Mishra

Authors
  1. Varun Kumar Ojha
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  2. Deepak Amban Mishra
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Corresponding author

Correspondence to Varun Kumar Ojha .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science, South Asian University, Chanakyapuri, Delhi, India

    Pranab Kr. Muhuri

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Ojha, V.K., Mishra, D.A. (2018). Neural Tree for Estimating the Uniaxial Compressive Strength of Rock Materials. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2017. Advances in Intelligent Systems and Computing, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-319-76351-4_1

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  • DOI: https://doi.org/10.1007/978-3-319-76351-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76350-7

  • Online ISBN: 978-3-319-76351-4

  • eBook Packages: EngineeringEngineering (R0)

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