This HTML5 document contains 48 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n14http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n22http://purl.org/net/nknouf/ns/bibtex#
n11http://localhost/temp/predkladatel/
n20http://linked.opendata.cz/resource/domain/vavai/projekt/
n15http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n13http://linked.opendata.cz/resource/domain/vavai/subjekt/
n12http://linked.opendata.cz/ontology/domain/vavai/
n18https://schema.org/
n9http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F68407700%3A21110%2F13%3A00206798%21RIV14-GA0-21110___/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n8http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n16http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n19http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n6http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n21http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n4http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n10http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F68407700%3A21110%2F13%3A00206798%21RIV14-GA0-21110___
rdf:type
n12:Vysledek skos:Concept
dcterms:description
This paper is focused on multi-objective reliability-based design optimization. A weight of a structure and a probability of failure represented by a reliability index form two competing objectives. Since the probability of failure of realistic structures is low, e.g. 10-4 - 10-5 for ultimate limit states in the case of civil engineering structures, the required number of samples for crude Monte Carlo is overwhelming. Even the application of quasi Monte Carlo methods such as Latin Hypercube Sampling does not bring substantial reduction of the required computational time and therefore, an approximate method called asymptotic sampling for prediction of the probability of failure and/or the reliability index is used. Here, an application of multi-objective optimization to reliability-based design optimization is twofold: (i) single-objective optimization is usually not able to find all optima in a multimodal problem thus the multi-objective algorithm based on a non-dominated sorting genetic algorithm is employed to show the vicinity of the trade-off near the limit value of the prescribed reliability index and (ii) by inspecting the Pareto-front obtained, an estimation of the sensitivities of individual variables can be judged. This paper is focused on multi-objective reliability-based design optimization. A weight of a structure and a probability of failure represented by a reliability index form two competing objectives. Since the probability of failure of realistic structures is low, e.g. 10-4 - 10-5 for ultimate limit states in the case of civil engineering structures, the required number of samples for crude Monte Carlo is overwhelming. Even the application of quasi Monte Carlo methods such as Latin Hypercube Sampling does not bring substantial reduction of the required computational time and therefore, an approximate method called asymptotic sampling for prediction of the probability of failure and/or the reliability index is used. Here, an application of multi-objective optimization to reliability-based design optimization is twofold: (i) single-objective optimization is usually not able to find all optima in a multimodal problem thus the multi-objective algorithm based on a non-dominated sorting genetic algorithm is employed to show the vicinity of the trade-off near the limit value of the prescribed reliability index and (ii) by inspecting the Pareto-front obtained, an estimation of the sensitivities of individual variables can be judged.
dcterms:title
Multi-Objective Optimization with Asymptotic Sampling for RBDO Multi-Objective Optimization with Asymptotic Sampling for RBDO
skos:prefLabel
Multi-Objective Optimization with Asymptotic Sampling for RBDO Multi-Objective Optimization with Asymptotic Sampling for RBDO
skos:notation
RIV/68407700:21110/13:00206798!RIV14-GA0-21110___
n12:predkladatel
n13:orjk%3A21110
n3:aktivita
n19:P
n3:aktivity
P(GAP105/12/1146)
n3:dodaniDat
n10:2014
n3:domaciTvurceVysledku
n15:9081429 n15:1476602
n3:druhVysledku
n4:D
n3:duvernostUdaju
n16:S
n3:entitaPredkladatele
n9:predkladatel
n3:idSjednocenehoVysledku
90118
n3:idVysledku
RIV/68407700:21110/13:00206798
n3:jazykVysledku
n6:eng
n3:klicovaSlova
Reliability-Based Design Optimization; Asymptotic Sampling; Latin Hypercube Sampling; Crude Monte Carlo Sampling; Nondominated Sorting Genetic Algorithm II; Multi-Objective Optimization; Limit State Function; Probability of Failure
n3:klicoveSlovo
n8:Multi-Objective%20Optimization n8:Latin%20Hypercube%20Sampling n8:Nondominated%20Sorting%20Genetic%20Algorithm%20II n8:Reliability-Based%20Design%20Optimization n8:Asymptotic%20Sampling n8:Limit%20State%20Function n8:Probability%20of%20Failure n8:Crude%20Monte%20Carlo%20Sampling
n3:kontrolniKodProRIV
[F78C2CA6E2CF]
n3:mistoKonaniAkce
Cagliari
n3:mistoVydani
Stirling
n3:nazevZdroje
Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering
n3:obor
n21:JD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n20:GAP105%2F12%2F1146
n3:rokUplatneniVysledku
n10:2013
n3:tvurceVysledku
Pospíšilová, Adéla Lepš, Matěj
n3:typAkce
n14:WRD
n3:zahajeniAkce
2013-09-03+02:00
s:issn
1759-3433
s:numberOfPages
12
n22:hasPublisher
Civil-Comp Press Ltd
n18:isbn
978-1-905088-58-4
n11:organizacniJednotka
21110