Innovative seismic design optimization with reliability constraints

Lagaros, ND; Garavelas, ATh; Papadrakakis, M

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