Neural Tree for Estimating the Uniaxial Compressive Strength of Rock Materials

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Neural Tree for Estimating the Uniaxial Compressive Strength of Rock Materials

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Neural Tree for Estimating the Uniaxial Compressive Strength of Rock Materials

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Hybrid Intelligent Systems - HIS 2017

ISBN

978-3-319-76351-4

ISSN

2194-5357

e-ISSN

2194-5365

Počet stran výsledku

10

Strana od-do

1-10

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

New Dehli

Datum konání akce

14. 12. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000456078600001