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Neural Tree for Estimating the Uniaxial Compressive Strength of Rock Materials
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SYSNO ASEP 0506924 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Neural Tree for Estimating the Uniaxial Compressive Strength of Rock Materials Author(s) Ojha, V. K. (IN)
Mishra, Deepak Amban (UGN-S)Number of authors 2 Source Title Hybrid Intelligent Systems - HIS 2017, 17. - Cham : Springer, 2018 / Abraham A. ; Muhuri P. K. ; Muda A. K. ; Ghandi N. - ISSN 2194-5357 - ISBN 978-3-319-76351-4 Pages s. 1-10 Number of pages 10 s. Publication form Medium - C Action International Conference on Hybrid Intelligent Systems (HIS) /17./ Event date 14.12.2017 - 16.12.2017 VEvent location New Dehli Country IN - India Event type WRD Language eng - English Country CH - Switzerland Keywords uniaxial compressive strength ; index tests ; rock materials ; heterogeneous flexible neural tree ; feature analysis Subject RIV DH - Mining, incl. Coal Mining OECD category Mechanical engineering Institutional support UGN-S - RVO:68145535 UT WOS 000456078600001 EID SCOPUS 85044456260 DOI 10.1007/978-3-319-76351-4_1 Annotation 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. Workplace Institute of Geonics Contact Lucie Gurková, lucie.gurkova@ugn.cas.cz, Tel.: 596 979 354 Year of Publishing 2020
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