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Innovative seismic design optimization with reliability constraints

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dc.contributor.author Lagaros, ND en
dc.contributor.author Garavelas, ATh en
dc.contributor.author Papadrakakis, M en
dc.date.accessioned 2014-03-01T01:28:41Z
dc.date.available 2014-03-01T01:28:41Z
dc.date.issued 2008 en
dc.identifier.issn 0045-7825 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/18917
dc.subject Earthquake resistant structures en
dc.subject Monte Carlo en
dc.subject Neural networks en
dc.subject Performance-Based Design en
dc.subject Reliability-based optimization en
dc.subject Response surface en
dc.subject Sizing-topology design variables en
dc.subject.classification Engineering, Multidisciplinary en
dc.subject.classification Mathematics, Interdisciplinary Applications en
dc.subject.classification Mechanics en
dc.subject.other Budget control en
dc.subject.other Cluster analysis en
dc.subject.other Computational methods en
dc.subject.other Design en
dc.subject.other Earthquakes en
dc.subject.other Monte Carlo methods en
dc.subject.other Network protocols en
dc.subject.other Neural networks en
dc.subject.other Optimization en
dc.subject.other Probability en
dc.subject.other Probability density function en
dc.subject.other Quality assurance en
dc.subject.other Random variables en
dc.subject.other Reliability analysis en
dc.subject.other Risk assessment en
dc.subject.other Seismic design en
dc.subject.other Seismology en
dc.subject.other Sensor networks en
dc.subject.other Shape optimization en
dc.subject.other Structural optimization en
dc.subject.other Topology en
dc.subject.other Uncertainty analysis en
dc.subject.other Vegetation en
dc.subject.other Wireless sensor networks en
dc.subject.other Earthquake resistant structures en
dc.subject.other Monte Carlo en
dc.subject.other Performance-Based Design en
dc.subject.other Response surface en
dc.subject.other Sizing-topology design variables en
dc.subject.other Reliability en
dc.title Innovative seismic design optimization with reliability constraints en
heal.type journalArticle en
heal.identifier.primary 10.1016/j.cma.2007.12.025 en
heal.identifier.secondary http://dx.doi.org/10.1016/j.cma.2007.12.025 en
heal.language English en
heal.publicationDate 2008 en
heal.abstract Performance-Based Design (PBD) methodologies is the contemporary trend in designing better and more economic earthquake-resistant structures where the main objective is to achieve more predictable and reliable levels of safety and operability against natural hazards. On the other hand, reliability-based optimization (RBO) methods directly account for the variability of the design parameters into the formulation of the optimization problem. The objective of this work is to incorporate PBD methodologies under seismic loading into the framework of RBO in conjunction with innovative tools for treating computational intensive problems of real-world structural systems. Two types of random variables are considered: Those which influence the level of seismic demand and those that affect the structural capacity. Reliability analysis is required for the assessment of the probabilistic constraints within the RBO formulation. The Monte Carlo Simulation (MCS) method is considered as the most reliable method for estimating the probabilities of exceedance or other statistical quantities albeit with excessive, in many cases, computational cost. First or Second Order Reliability Methods (FORM, SORM) constitute alternative approaches which require an explicit limit-state function. This type of limit-state function is not available for complex problems. In this study, in order to find the most efficient methodology for performing reliability analysis in conjunction with performance-based optimum design under seismic loading, a Neural Network approximation of the limit-state function is proposed and is combined with either MCS or with FORM approaches for handling the uncertainties. These two methodologies are applied in RBO problems with sizing and topology design variables resulting in two orders of magnitude reduction of the computational effort. (C) 2008 Elsevier B.V. All rights reserved. en
heal.publisher ELSEVIER SCIENCE SA en
heal.journalName Computer Methods in Applied Mechanics and Engineering en
dc.identifier.doi 10.1016/j.cma.2007.12.025 en
dc.identifier.isi ISI:000261249700004 en
dc.identifier.volume 198 en
dc.identifier.issue 1 en
dc.identifier.spage 28 en
dc.identifier.epage 41 en


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