FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2048796786> ?p ?o ?g. }

• W2048796786 endingPage "606" @default.
• W2048796786 startingPage "600" @default.
• W2048796786 abstract "Contemporary methods for estimating the extent of seismic-induced damage to structures include the use of nonlinear finite element method (FEM) and seismic vulnerability curves. FEM is applicable when a small number of predetermined structures is to be assessed, but becomes inefficient for larger stocks. Seismic vulnerability curves enable damage estimation for classes of similar structures characterised by a small number of parameters, and typically use only one parameter to describe ground motion. Hence, they are unable to extend damage prognosis to wider classes of structures, e.g. buildings with a different number of storeys and/or bays, or capture the full complexity of the relationship between damage and seismic excitation parameters. Motivated by these shortcomings, this study presents a general method for predicting seismic-induced damage using Artificial Neural Networks (ANNs). The approach was to describe both the structure and ground motion using a large number of structural and ground motion properties. The class of structures analysed were 2D reinforced concrete (RC) frames that varied in topology, stiffness, strength and damping, and were subjected to a suite of ground motions. Dynamic structural responses were simulated using nonlinear FEM analysis and damage indices describing the extent of damage calculated. Using the results of the numerical simulations, a mapping between the structural and ground motion properties and the damage indices was than established using an ANN. The performance of the ANN was assessed using several examples and the ANN was found to be capable of successfully predicting damage." @default.
• W2048796786 created "2016-06-24" @default.
• W2048796786 creator A5016177150 @default.
• W2048796786 creator A5066370154 @default.
• W2048796786 date "2009-02-01" @default.
• W2048796786 modified "2023-10-12" @default.
• W2048796786 title "Prediction of seismic-induced structural damage using artificial neural networks" @default.
• W2048796786 cites W1966332297 @default.
• W2048796786 cites W1967299871 @default.
• W2048796786 cites W1978136589 @default.
• W2048796786 cites W1979630897 @default.
• W2048796786 cites W1980654870 @default.
• W2048796786 cites W1982028025 @default.
• W2048796786 cites W1999716693 @default.
• W2048796786 cites W2000035517 @default.
• W2048796786 cites W2002292351 @default.
• W2048796786 cites W2003333626 @default.
• W2048796786 cites W2003561908 @default.
• W2048796786 cites W2004620726 @default.
• W2048796786 cites W2014712737 @default.
• W2048796786 cites W2017630868 @default.
• W2048796786 cites W2026080673 @default.
• W2048796786 cites W2037825001 @default.
• W2048796786 cites W2040396750 @default.
• W2048796786 cites W2043760160 @default.
• W2048796786 cites W2044828498 @default.
• W2048796786 cites W2047368706 @default.
• W2048796786 cites W2058121935 @default.
• W2048796786 cites W2075076760 @default.
• W2048796786 cites W2077200378 @default.
• W2048796786 cites W2080837090 @default.
• W2048796786 cites W2084437025 @default.
• W2048796786 cites W2087070363 @default.
• W2048796786 cites W2087602030 @default.
• W2048796786 cites W2088848761 @default.
• W2048796786 cites W2101283270 @default.
• W2048796786 cites W2103312173 @default.
• W2048796786 cites W2109563136 @default.
• W2048796786 cites W2143862548 @default.
• W2048796786 cites W2147010825 @default.
• W2048796786 cites W2164082899 @default.
• W2048796786 cites W2167975744 @default.
• W2048796786 cites W2467915989 @default.
• W2048796786 cites W4252016270 @default.
• W2048796786 cites W585754145 @default.
• W2048796786 doi "https://doi.org/10.1016/j.engstruct.2008.11.010" @default.
• W2048796786 hasPublicationYear "2009" @default.
• W2048796786 type Work @default.
• W2048796786 sameAs 2048796786 @default.
• W2048796786 citedByCount "130" @default.
• W2048796786 countsByYear W20487967862012 @default.
• W2048796786 countsByYear W20487967862013 @default.
• W2048796786 countsByYear W20487967862014 @default.
• W2048796786 countsByYear W20487967862015 @default.
• W2048796786 countsByYear W20487967862016 @default.
• W2048796786 countsByYear W20487967862017 @default.
• W2048796786 countsByYear W20487967862018 @default.
• W2048796786 countsByYear W20487967862019 @default.
• W2048796786 countsByYear W20487967862020 @default.
• W2048796786 countsByYear W20487967862021 @default.
• W2048796786 countsByYear W20487967862022 @default.
• W2048796786 countsByYear W20487967862023 @default.
• W2048796786 crossrefType "journal-article" @default.
• W2048796786 hasAuthorship W2048796786A5016177150 @default.
• W2048796786 hasAuthorship W2048796786A5066370154 @default.
• W2048796786 hasBestOaLocation W20487967862 @default.
• W2048796786 hasConcept C121332964 @default.
• W2048796786 hasConcept C127313418 @default.
• W2048796786 hasConcept C127413603 @default.
• W2048796786 hasConcept C135628077 @default.
• W2048796786 hasConcept C154945302 @default.
• W2048796786 hasConcept C158622935 @default.
• W2048796786 hasConcept C2779372316 @default.
• W2048796786 hasConcept C2988284105 @default.
• W2048796786 hasConcept C38652104 @default.
• W2048796786 hasConcept C41008148 @default.
• W2048796786 hasConcept C50644808 @default.
• W2048796786 hasConcept C62520636 @default.
• W2048796786 hasConcept C66938386 @default.
• W2048796786 hasConcept C95713431 @default.
• W2048796786 hasConceptScore W2048796786C121332964 @default.
• W2048796786 hasConceptScore W2048796786C127313418 @default.
• W2048796786 hasConceptScore W2048796786C127413603 @default.
• W2048796786 hasConceptScore W2048796786C135628077 @default.
• W2048796786 hasConceptScore W2048796786C154945302 @default.
• W2048796786 hasConceptScore W2048796786C158622935 @default.
• W2048796786 hasConceptScore W2048796786C2779372316 @default.
• W2048796786 hasConceptScore W2048796786C2988284105 @default.
• W2048796786 hasConceptScore W2048796786C38652104 @default.
• W2048796786 hasConceptScore W2048796786C41008148 @default.
• W2048796786 hasConceptScore W2048796786C50644808 @default.
• W2048796786 hasConceptScore W2048796786C62520636 @default.
• W2048796786 hasConceptScore W2048796786C66938386 @default.
• W2048796786 hasConceptScore W2048796786C95713431 @default.
• W2048796786 hasIssue "2" @default.
• W2048796786 hasLocation W20487967861 @default.
• W2048796786 hasLocation W20487967862 @default.
• W2048796786 hasOpenAccess W2048796786 @default.

• next