Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks
Mining Engineering Department, Mahallat Branch, Islamic Azad University, Iran.
Mining Engineering Department, Mahallat Branch, Islamic Azad University, Iran.
Surface Science Western, University of Western Ontario, Canada.ORCID iD: 0000-0002-2265-6321
Mining Engineering Department, Mahallat Branch, Islamic Azad University, Iran.
2010 (English)In: Mining Science and Technology, ISSN 1674-5264, Vol. 20, no 1, p. 41-46Article in journal (Refereed) Published
Abstract [en]

Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both methods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear relations obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression results. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.

Place, publisher, year, edition, pages
Elsevier, 2010. Vol. 20, no 1, p. 41-46
Keywords [en]
uniaxial compressive strength, modulus of elasticity, artificial neural networks, regression, travertine
National Category
Mineral and Mine Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-72293DOI: 10.1016/S1674-5264(09)60158-7Scopus ID: 2-s2.0-76249108297OAI: oai:DiVA.org:ltu-72293DiVA, id: diva2:1272032
Available from: 2018-12-18 Created: 2018-12-18 Last updated: 2019-02-25

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Chelgani, Saeed Chehreh

Search in DiVA

By author/editor
Chelgani, Saeed Chehreh
Mineral and Mine Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 204 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf