Developing fragility curves based on neural network IDA predictions

Mitropoulou, CC; Papadrakakis, M

dc.contributor.author	Mitropoulou, CC	en
dc.contributor.author	Papadrakakis, M	en
dc.date.accessioned	2014-03-01T01:35:30Z
dc.date.available	2014-03-01T01:35:30Z
dc.date.issued	2011	en
dc.identifier.issn	0141-0296	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21083
dc.subject	Fragility analysis	en
dc.subject	Harmony search	en
dc.subject	Incremental dynamic analysis	en
dc.subject	Neural networks	en
dc.subject	Reinforced concrete buildings	en
dc.subject	Vertical statistics	en
dc.subject.classification	Engineering, Civil	en
dc.subject.other	3D reinforced concrete	en
dc.subject.other	Accurate prediction	en
dc.subject.other	Algorithmic solutions	en
dc.subject.other	Computational effort	en
dc.subject.other	Computational time	en
dc.subject.other	Computing system	en
dc.subject.other	Fragility analysis	en
dc.subject.other	Fragility assessment	en
dc.subject.other	Fragility curves	en
dc.subject.other	Harmony search	en
dc.subject.other	Incremental dynamic analysis	en
dc.subject.other	Structural response	en
dc.subject.other	Structural systems	en
dc.subject.other	Vertical statistics	en
dc.subject.other	Algorithms	en
dc.subject.other	Concrete buildings	en
dc.subject.other	Concrete construction	en
dc.subject.other	Dynamic analysis	en
dc.subject.other	Forecasting	en
dc.subject.other	Reinforced concrete	en
dc.subject.other	Soft computing	en
dc.subject.other	Three dimensional	en
dc.subject.other	Neural networks	en
dc.subject.other	architectural design	en
dc.subject.other	artificial neural network	en
dc.subject.other	concrete structure	en
dc.subject.other	dynamic analysis	en
dc.subject.other	prediction	en
dc.subject.other	reinforced concrete	en
dc.subject.other	statistical analysis	en
dc.subject.other	structural response	en
dc.title	Developing fragility curves based on neural network IDA predictions	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.engstruct.2011.07.005	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.engstruct.2011.07.005	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	A Soft Computing (SC) based framework for the fragility assessment of 3D buildings is proposed in this work. The computational effort required for a fragility analysis of structural systems can become excessive, far beyond the capability of modern computing systems, especially when dealing with real-world structures. For the purpose of making attainable fragility analyses, a Neural Network (NN) implementation is presented in this work, which can provide accurate predictions of the structural response at a fraction of computational time required by a conventional analysis. The main advantage of using NN predictions is that they can deal with problems, without having an algorithmic solution or with an algorithmic solution that is too complex to be found. The proposed methodology is applied to 3D reinforced concrete buildings where it was found that with the proposed implementation of NN, a reduction of one order of magnitude is achieved in the computational effort for performing a full fragility analysis. (C) 2011 Elsevier Ltd. All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	Engineering Structures	en
dc.identifier.doi	10.1016/j.engstruct.2011.07.005	en
dc.identifier.isi	ISI:000297823200028	en
dc.identifier.volume	33	en
dc.identifier.issue	12	en
dc.identifier.spage	3409	en
dc.identifier.epage	3421	en