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dc.contributor.author
Ojha, Varun Kumar
dc.contributor.author
Mishra, Deepak Amban
dc.date.accessioned
2018-03-16T15:09:54Z
dc.date.available
2018-03-16T09:55:47Z
dc.date.available
2018-03-16T10:04:53Z
dc.date.available
2018-03-16T15:09:54Z
dc.date.issued
2018
dc.identifier.isbn
9783319763507
en_US
dc.identifier.isbn
9783319763514
en_US
dc.identifier.other
10.1007/978-3-319-76351-4_1
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/250542
dc.identifier.doi
10.3929/ethz-b-000250542
dc.description.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.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Springer
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
neural network
en_US
dc.subject
ROCK MECHANICS (CIVIL ENGINEERING)
en_US
dc.subject
Compressive strength
en_US
dc.subject
Optimization
en_US
dc.title
Neural Tree for Estimating the Uniaxial Compressive Strength of Rock Materials
en_US
dc.type
Book Chapter
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2018-03-16
ethz.book.title
Hybrid Intelligent Systems
en_US
ethz.size
10 p.
en_US
ethz.version.deposit
submittedVersion
en_US
ethz.identifier.wos
ethz.publication.place
Heidelberg
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02100 - Dep. Architektur / Dep. of Architecture::02602 - Inst. f. Technologie in der Architektur / Institute for Technology in Architecture::03276 - Schmitt, Gerhard (emeritus) / Schmitt, Gerhard (emeritus)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02100 - Dep. Architektur / Dep. of Architecture::02602 - Inst. f. Technologie in der Architektur / Institute for Technology in Architecture::03276 - Schmitt, Gerhard (emeritus) / Schmitt, Gerhard (emeritus)
en_US
ethz.tag
machine learning
en_US
ethz.tag
rock mechanics
en_US
ethz.date.deposited
2018-03-16T09:55:48Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2018-04-01T10:06:04Z
ethz.rosetta.lastUpdated
2021-02-14T22:55:42Z
ethz.rosetta.versionExported
true
ethz.COinS
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