Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • 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 (engelsk)Inngår i: Mining Science and Technology, ISSN 1674-5264, Vol. 20, nr 1, s. 41-46Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Elsevier, 2010. Vol. 20, nr 1, s. 41-46
Emneord [en]
uniaxial compressive strength, modulus of elasticity, artificial neural networks, regression, travertine
HSV kategori
Identifikatorer
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
Tilgjengelig fra: 2018-12-18 Laget: 2018-12-18 Sist oppdatert: 2020-03-09bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstScopus

Søk i DiVA

Av forfatter/redaktør
Chelgani, Saeed Chehreh

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 215 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf