Neural Tree for Estimating the Uniaxial Compressive Strength of Rock Materials

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68145535%3A_____%2F18%3A00506924" target="_blank" >RIV/68145535:_____/18:00506924 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-76351-4_1" target="_blank" >http://dx.doi.org/10.1007/978-3-319-76351-4_1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-76351-4_1" target="_blank" >10.1007/978-3-319-76351-4_1</a>

Result language
angličtina
Original language name
Neural Tree for Estimating the Uniaxial Compressive Strength of Rock Materials
Original language description
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.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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