Neural Tree for Estimating the Uniaxial Compressive Strength of Rock Materials
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Author / Producer
Date
2018
Publication Type
Conference Paper
ETH Bibliography
yes
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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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Publication status
published
External links
Book title
Hybrid Intelligent Systems
Journal / series
Volume
734
Pages / Article No.
1 - 10
Publisher
Springer
Event
17th International Conference on Hybrid Intelligent Systems (HIS 2017)
Edition / version
Methods
Software
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Date collected
Date created
Subject
Uniaxial compressive strength; Index tests; Rock materials; Heterogeneous flexible neural tree; Feature analysis
Organisational unit
03276 - Schmitt, Gerhard (emeritus) / Schmitt, Gerhard (emeritus)