Matches in SemOpenAlex for { <https://semopenalex.org/work/W3087678045> ?p ?o ?g. }
Showing items 1 to 75 of
75
with 100 items per page.
- W3087678045 endingPage "309" @default.
- W3087678045 startingPage "295" @default.
- W3087678045 abstract "The construction of Reinforced Concrete (R/C) buildings with unreinforced masonry infills is part of the traditional building practice in many countries with regions of high seismicity throughout the world. When these buildings are subjected to seismic motions the presence of masonry infills and especially their configuration can highly influence the seismic damage state. The capability to avoid configurations of masonry infills prone to seismic damage at the stage of initial architectural concept would be significantly definitive in the context of Performance-Based Earthquake Engineering. Along these lines, the present paper investigates the potential of instant prediction of the damage response of R/C buildings with various configurations of masonry infills utilizing Artificial Neural Networks (ANNs). To this end, Multilayer Feedforward Perceptron networks are utilized and the problem is formulated as pattern recognition problem. The ANNs' training data-set is created by means of Nonlinear Time History Analyses of 5 R/C buildings with a large number of different masonry infills' distributions, which are subjected to 65 earthquakes. The structural damage is expressed in terms of the Maximum Interstorey Drift Ratio. The most significant conclusion which is extracted is that the ANNs can reliably estimate the influence of masonry infills' configurations on the seismic damage level of R/C buildings incorporating their optimum design." @default.
- W3087678045 created "2020-09-25" @default.
- W3087678045 creator A5026314529 @default.
- W3087678045 creator A5042062662 @default.
- W3087678045 date "2020-01-01" @default.
- W3087678045 modified "2023-09-28" @default.
- W3087678045 title "Optimization of the seismic performance of masonry infilled R/C buildings at the stage of design using artificial neural networks" @default.
- W3087678045 doi "https://doi.org/10.12989/sem.2020.75.3.295" @default.
- W3087678045 hasPublicationYear "2020" @default.
- W3087678045 type Work @default.
- W3087678045 sameAs 3087678045 @default.
- W3087678045 citedByCount "1" @default.
- W3087678045 countsByYear W30876780452022 @default.
- W3087678045 crossrefType "journal-article" @default.
- W3087678045 hasAuthorship W3087678045A5026314529 @default.
- W3087678045 hasAuthorship W3087678045A5042062662 @default.
- W3087678045 hasConcept C127313418 @default.
- W3087678045 hasConcept C127413603 @default.
- W3087678045 hasConcept C147176958 @default.
- W3087678045 hasConcept C151730666 @default.
- W3087678045 hasConcept C152006893 @default.
- W3087678045 hasConcept C154945302 @default.
- W3087678045 hasConcept C179717631 @default.
- W3087678045 hasConcept C2779343474 @default.
- W3087678045 hasConcept C2988805333 @default.
- W3087678045 hasConcept C41008148 @default.
- W3087678045 hasConcept C50644808 @default.
- W3087678045 hasConcept C535899295 @default.
- W3087678045 hasConcept C66938386 @default.
- W3087678045 hasConcept C83176761 @default.
- W3087678045 hasConceptScore W3087678045C127313418 @default.
- W3087678045 hasConceptScore W3087678045C127413603 @default.
- W3087678045 hasConceptScore W3087678045C147176958 @default.
- W3087678045 hasConceptScore W3087678045C151730666 @default.
- W3087678045 hasConceptScore W3087678045C152006893 @default.
- W3087678045 hasConceptScore W3087678045C154945302 @default.
- W3087678045 hasConceptScore W3087678045C179717631 @default.
- W3087678045 hasConceptScore W3087678045C2779343474 @default.
- W3087678045 hasConceptScore W3087678045C2988805333 @default.
- W3087678045 hasConceptScore W3087678045C41008148 @default.
- W3087678045 hasConceptScore W3087678045C50644808 @default.
- W3087678045 hasConceptScore W3087678045C535899295 @default.
- W3087678045 hasConceptScore W3087678045C66938386 @default.
- W3087678045 hasConceptScore W3087678045C83176761 @default.
- W3087678045 hasIssue "3" @default.
- W3087678045 hasLocation W30876780451 @default.
- W3087678045 hasOpenAccess W3087678045 @default.
- W3087678045 hasPrimaryLocation W30876780451 @default.
- W3087678045 hasRelatedWork W1610669201 @default.
- W3087678045 hasRelatedWork W1995736354 @default.
- W3087678045 hasRelatedWork W2028320161 @default.
- W3087678045 hasRelatedWork W2041122882 @default.
- W3087678045 hasRelatedWork W2350172212 @default.
- W3087678045 hasRelatedWork W2365703957 @default.
- W3087678045 hasRelatedWork W2387052086 @default.
- W3087678045 hasRelatedWork W2406290201 @default.
- W3087678045 hasRelatedWork W2464548723 @default.
- W3087678045 hasRelatedWork W2532827258 @default.
- W3087678045 hasRelatedWork W2613026976 @default.
- W3087678045 hasRelatedWork W2911058516 @default.
- W3087678045 hasRelatedWork W2988492635 @default.
- W3087678045 hasRelatedWork W3011602107 @default.
- W3087678045 hasRelatedWork W3013263687 @default.
- W3087678045 hasRelatedWork W3045045928 @default.
- W3087678045 hasRelatedWork W315719465 @default.
- W3087678045 hasRelatedWork W3200117302 @default.
- W3087678045 hasRelatedWork W2181790052 @default.
- W3087678045 hasRelatedWork W2625129024 @default.
- W3087678045 hasVolume "75" @default.
- W3087678045 isParatext "false" @default.
- W3087678045 isRetracted "false" @default.
- W3087678045 magId "3087678045" @default.
- W3087678045 workType "article" @default.