FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2401706795> ?p ?o ?g. }

• W2401706795 endingPage "720" @default.
• W2401706795 startingPage "711" @default.
• W2401706795 abstract "This study proposes an artificial intelligence (AI) model to predict the compressive strength and splitting tensile strength of rubberized concrete. This Evolutionary Multivariate Adaptive Regression Splines (EMARS) model is a hybrid of the Multivariate Adaptive Regression Splines (MARS) and Artificial Bee Colony (ABC) within which MARS addresses learning and curve fitting and ABC implements optimization to determine the fittest parameter settings with minimal prediction error. K-fold cross validation was utilized to compare EMARS performance against four other benchmark data mining techniques including MARS, Back-propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Genetic Programming (GP). Comparison results showed EMARS to be the best model for predicting rubberized concrete strength and study results demonstrated EMARS as a reliable tool for civil engineers in the concrete construction industry." @default.
• W2401706795 created "2016-06-24" @default.
• W2401706795 creator A5074387638 @default.
• W2401706795 creator A5079150256 @default.
• W2401706795 date "2016-05-17" @default.
• W2401706795 modified "2023-10-18" @default.
• W2401706795 title "ESTIMATING STRENGTH OF RUBBERIZED CONCRETE USING EVOLUTIONARY MULTIVARIATE ADAPTIVE REGRESSION SPLINES" @default.
• W2401706795 cites W132154760 @default.
• W2401706795 cites W1532221965 @default.
• W2401706795 cites W1978918172 @default.
• W2401706795 cites W1981093066 @default.
• W2401706795 cites W1982869555 @default.
• W2401706795 cites W1991957090 @default.
• W2401706795 cites W1993922907 @default.
• W2401706795 cites W1996029664 @default.
• W2401706795 cites W2009252994 @default.
• W2401706795 cites W2012316003 @default.
• W2401706795 cites W2015304908 @default.
• W2401706795 cites W2026478181 @default.
• W2401706795 cites W2030275442 @default.
• W2401706795 cites W2034654759 @default.
• W2401706795 cites W2042135388 @default.
• W2401706795 cites W2047265856 @default.
• W2401706795 cites W2052319596 @default.
• W2401706795 cites W2052938830 @default.
• W2401706795 cites W2056707911 @default.
• W2401706795 cites W2061933243 @default.
• W2401706795 cites W2064703303 @default.
• W2401706795 cites W2068438324 @default.
• W2401706795 cites W2079572060 @default.
• W2401706795 cites W2091190246 @default.
• W2401706795 cites W2092334295 @default.
• W2401706795 cites W2102201073 @default.
• W2401706795 cites W2150275984 @default.
• W2401706795 cites W2151021446 @default.
• W2401706795 cites W2152277880 @default.
• W2401706795 cites W2153689927 @default.
• W2401706795 cites W2169064301 @default.
• W2401706795 cites W2401706795 @default.
• W2401706795 doi "https://doi.org/10.3846/13923730.2014.897989" @default.
• W2401706795 hasPublicationYear "2016" @default.
• W2401706795 type Work @default.
• W2401706795 sameAs 2401706795 @default.
• W2401706795 citedByCount "21" @default.
• W2401706795 countsByYear W24017067952016 @default.
• W2401706795 countsByYear W24017067952017 @default.
• W2401706795 countsByYear W24017067952018 @default.
• W2401706795 countsByYear W24017067952019 @default.
• W2401706795 countsByYear W24017067952020 @default.
• W2401706795 countsByYear W24017067952021 @default.
• W2401706795 countsByYear W24017067952022 @default.
• W2401706795 countsByYear W24017067952023 @default.
• W2401706795 crossrefType "journal-article" @default.
• W2401706795 hasAuthorship W2401706795A5074387638 @default.
• W2401706795 hasAuthorship W2401706795A5079150256 @default.
• W2401706795 hasBestOaLocation W24017067951 @default.
• W2401706795 hasConcept C105795698 @default.
• W2401706795 hasConcept C105902424 @default.
• W2401706795 hasConcept C110332635 @default.
• W2401706795 hasConcept C119857082 @default.
• W2401706795 hasConcept C121332964 @default.
• W2401706795 hasConcept C124101348 @default.
• W2401706795 hasConcept C127313418 @default.
• W2401706795 hasConcept C127413603 @default.
• W2401706795 hasConcept C1276947 @default.
• W2401706795 hasConcept C13280743 @default.
• W2401706795 hasConcept C152877465 @default.
• W2401706795 hasConcept C154945302 @default.
• W2401706795 hasConcept C161584116 @default.
• W2401706795 hasConcept C185798385 @default.
• W2401706795 hasConcept C33923547 @default.
• W2401706795 hasConcept C41008148 @default.
• W2401706795 hasConcept C44882253 @default.
• W2401706795 hasConcept C50644808 @default.
• W2401706795 hasConcept C64946054 @default.
• W2401706795 hasConcept C66938386 @default.
• W2401706795 hasConcept C83260615 @default.
• W2401706795 hasConcept C83546350 @default.
• W2401706795 hasConcept C8880873 @default.
• W2401706795 hasConceptScore W2401706795C105795698 @default.
• W2401706795 hasConceptScore W2401706795C105902424 @default.
• W2401706795 hasConceptScore W2401706795C110332635 @default.
• W2401706795 hasConceptScore W2401706795C119857082 @default.
• W2401706795 hasConceptScore W2401706795C121332964 @default.
• W2401706795 hasConceptScore W2401706795C124101348 @default.
• W2401706795 hasConceptScore W2401706795C127313418 @default.
• W2401706795 hasConceptScore W2401706795C127413603 @default.
• W2401706795 hasConceptScore W2401706795C1276947 @default.
• W2401706795 hasConceptScore W2401706795C13280743 @default.
• W2401706795 hasConceptScore W2401706795C152877465 @default.
• W2401706795 hasConceptScore W2401706795C154945302 @default.
• W2401706795 hasConceptScore W2401706795C161584116 @default.
• W2401706795 hasConceptScore W2401706795C185798385 @default.
• W2401706795 hasConceptScore W2401706795C33923547 @default.
• W2401706795 hasConceptScore W2401706795C41008148 @default.
• W2401706795 hasConceptScore W2401706795C44882253 @default.
• W2401706795 hasConceptScore W2401706795C50644808 @default.
• W2401706795 hasConceptScore W2401706795C64946054 @default.

• next