Relevant intensity measures for seismic damage prediction with artificial neural networks

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dc.contributor.authorKonstantin Goldschmidt (Technische Universität Kaiserslautern, Paul-Ehrlich-Straße 14)
dc.contributor.authorHamid Sadegh-Azar (Technische Universität Kaiserslautern, Paul-Ehrlich-Straße 14)
dc.contributor.authorMani Mohtasham Miavaghi (Technische Universität Kaiserslautern, Paul-Ehrlich-Straße 14)
dc.date.accessioned2023-04-12T01:13:56Z
dc.date.available2023-04-12T01:13:56Z
dc.date.issued2022-07-10
dc.identifier.urihttps://www.lib.ncsu.edu/resolver/1840.20/40582
dc.publisherIASMiRT
dc.relation.ispartofseriesD07 - Safety, Reliability, Risk and Safety Margins
dc.relation.ispartofseriesD07-We.4.E - D07 - Seismic Fragility Assessment - Method Developments
dc.relation.ispartofseries00 - SMiRT 26 - Berlin/Potsdam, Germany. July 10-15, 2022
dc.titleRelevant intensity measures for seismic damage prediction with artificial neural networks
dc.typeConference Proceeding

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2022 - SMiRT 26 - Berlin/Potsdam, Germany