FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2018909738> ?p ?o ?g. }

• W2018909738 endingPage "60" @default.
• W2018909738 startingPage "51" @default.
• W2018909738 abstract "Abstract Mechanical rock excavation projects require uniaxial compressive strength (UCS) and static modulus of elasticity (E) of the intact rock material. High-quality core specimens of proper geometry are needed for the direct determination of these parameters. However, it is not always possible to obtain suitable specimens from highly fractured and/or weathered rocks for this purpose. Therefore, models predicting UCS and E based on rock index tests and intact rock properties have become alternative methods. For this reason, this paper is concerned with the applications of artificial neural networks (ANN) and regression trees technique along with the principal components and factor analyses on the predictions of UCS and E of intact rocks from their other properties. For that purpose, data obtained from three different rock cutting projects have been subjected to a series of statistical analyses. Results of the principal components analysis have shown that the most of the variance in the data set can be explained by only three principal components. The principal component with the highest variance is mainly weighted on the dependent variables of UCS and E. Factor analysis has revealed that two common factors may summarize the data set instead of original seven variables. Subsequent bivariate correlation and curve fitting analyses revealed that NCB cone indenter test was the most reliable indirect test to estimate UCS and E for the rocks that were employed in this study. For this reason, NCB cone indenter hardness was used in building predictive models for UCS and E along with dry density and Shore scleroscope hardness. Two different pairs of models for UCS and E were developed using ANN and regression trees technique, which were then compared to those built by multiple nonlinear regression method. Evaluation of the graphical and numerical measures of the goodness of the fit statistics clearly indicated that respective ANN models were more acceptable for predicting UCS and E than multiple nonlinear regression models. However, regression tree technique has appeared to be the best in building predictive models for UCS and E amongst the other methods." @default.
• W2018909738 created "2016-06-24" @default.
• W2018909738 creator A5008766019 @default.
• W2018909738 date "2008-06-01" @default.
• W2018909738 modified "2023-09-30" @default.
• W2018909738 title "Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees" @default.
• W2018909738 cites W1971404883 @default.
• W2018909738 cites W1972471185 @default.
• W2018909738 cites W1986507289 @default.
• W2018909738 cites W1994083857 @default.
• W2018909738 cites W1997229560 @default.
• W2018909738 cites W2014304657 @default.
• W2018909738 cites W2028326222 @default.
• W2018909738 cites W2035965144 @default.
• W2018909738 cites W2051005367 @default.
• W2018909738 cites W2055621492 @default.
• W2018909738 cites W2057951725 @default.
• W2018909738 cites W2061001005 @default.
• W2018909738 cites W2061199522 @default.
• W2018909738 cites W2061668175 @default.
• W2018909738 cites W2071494270 @default.
• W2018909738 cites W2081439519 @default.
• W2018909738 cites W2082589723 @default.
• W2018909738 cites W2085030517 @default.
• W2018909738 cites W2094199595 @default.
• W2018909738 cites W2116940203 @default.
• W2018909738 cites W2121067334 @default.
• W2018909738 cites W2124090653 @default.
• W2018909738 cites W2130301492 @default.
• W2018909738 cites W2166186246 @default.
• W2018909738 doi "https://doi.org/10.1016/j.enggeo.2008.02.003" @default.
• W2018909738 hasPublicationYear "2008" @default.
• W2018909738 type Work @default.
• W2018909738 sameAs 2018909738 @default.
• W2018909738 citedByCount "151" @default.
• W2018909738 countsByYear W20189097382012 @default.
• W2018909738 countsByYear W20189097382013 @default.
• W2018909738 countsByYear W20189097382014 @default.
• W2018909738 countsByYear W20189097382015 @default.
• W2018909738 countsByYear W20189097382016 @default.
• W2018909738 countsByYear W20189097382017 @default.
• W2018909738 countsByYear W20189097382018 @default.
• W2018909738 countsByYear W20189097382019 @default.
• W2018909738 countsByYear W20189097382020 @default.
• W2018909738 countsByYear W20189097382021 @default.
• W2018909738 countsByYear W20189097382022 @default.
• W2018909738 countsByYear W20189097382023 @default.
• W2018909738 crossrefType "journal-article" @default.
• W2018909738 hasAuthorship W2018909738A5008766019 @default.
• W2018909738 hasConcept C105795698 @default.
• W2018909738 hasConcept C127313418 @default.
• W2018909738 hasConcept C154945302 @default.
• W2018909738 hasConcept C161584116 @default.
• W2018909738 hasConcept C187320778 @default.
• W2018909738 hasConcept C31858485 @default.
• W2018909738 hasConcept C33923547 @default.
• W2018909738 hasConcept C41008148 @default.
• W2018909738 hasConcept C50644808 @default.
• W2018909738 hasConcept C83546350 @default.
• W2018909738 hasConceptScore W2018909738C105795698 @default.
• W2018909738 hasConceptScore W2018909738C127313418 @default.
• W2018909738 hasConceptScore W2018909738C154945302 @default.
• W2018909738 hasConceptScore W2018909738C161584116 @default.
• W2018909738 hasConceptScore W2018909738C187320778 @default.
• W2018909738 hasConceptScore W2018909738C31858485 @default.
• W2018909738 hasConceptScore W2018909738C33923547 @default.
• W2018909738 hasConceptScore W2018909738C41008148 @default.
• W2018909738 hasConceptScore W2018909738C50644808 @default.
• W2018909738 hasConceptScore W2018909738C83546350 @default.
• W2018909738 hasIssue "1-2" @default.
• W2018909738 hasLocation W20189097381 @default.
• W2018909738 hasOpenAccess W2018909738 @default.
• W2018909738 hasPrimaryLocation W20189097381 @default.
• W2018909738 hasRelatedWork W2007006185 @default.
• W2018909738 hasRelatedWork W2037582106 @default.
• W2018909738 hasRelatedWork W2355395437 @default.
• W2018909738 hasRelatedWork W2362709610 @default.
• W2018909738 hasRelatedWork W2386886284 @default.
• W2018909738 hasRelatedWork W2387626015 @default.
• W2018909738 hasRelatedWork W3203892851 @default.
• W2018909738 hasRelatedWork W4200240746 @default.
• W2018909738 hasRelatedWork W4243579405 @default.
• W2018909738 hasRelatedWork W2048136123 @default.
• W2018909738 hasVolume "99" @default.
• W2018909738 isParatext "false" @default.
• W2018909738 isRetracted "false" @default.
• W2018909738 magId "2018909738" @default.
• W2018909738 workType "article" @default.