Show simple item record

dc.contributor.authorAfanador García, Nelson
dc.contributor.authorGuerrero Gómez, Gustavo
dc.contributor.authorNolasco Serna, Christian
dc.coverage.spatialColombiaen_US
dc.date.accessioned2025-10-06T16:49:11Z
dc.date.available2025-10-06T16:49:11Z
dc.date.issued2021-07-01
dc.identifier.issn1742-6588en_US
dc.identifier.urihttps://repositorioinstitucional.ufpso.edu.co/xmlui/handle/20.500.14167/5666
dc.description.abstractEn los proyectos estructurales, garantizar el rendimiento del sistema dentro de las especificaciones establecidas con un nivel máximo de seguridad y teniendo en cuenta las limitaciones económicas del proyecto es uno de los principales objetivos del diseño estructural. Los riesgos a los que está sometido cualquier sistema físico se denominan probabilidad de fallo y se evalúan aplicando métodos de análisis de fiabilidad estructural. El objetivo del análisis de fiabilidad estructural es garantizar que la resistencia de los elementos de la estructura sea superior a la demanda de resistencia impuesta durante la vida útil de cada uno de los elementos estructurales. Las variables de diseño estructural son magnitudes físicas en la fiabilidad estructural y se consideran aleatorias, pudiendo representarse en un vector aleatorio. La probabilidad de fallo de una estructura se obtiene a partir de la evaluación de las incertidumbres inherentes a las variables físicas del proyecto, mediante las distribuciones de probabilidad de las variables aleatorias. El objetivo de este trabajo fue la aplicación de la expansión del caos polinomial para evaluar la probabilidad de fallo en las funciones de estado límite que se encuentran en la literatura utilizando simulación numérica, con el fin de disminuir el tamaño de la muestra para cada variable aleatoria en comparación con los necesarios utilizando la simulación de Monte Carlo. Esta investigación demostró que la diferencia entre el tamaño de la muestra entre la expansión del caos polinomial y la simulación de Monte Carlo es del 5 %, lo que supone un ahorro de tiempo y esfuerzo computacional.en_US
dc.language.isoen_USen_US
dc.relationhttps://iopscience.iop.org/article/10.1088/1742-6596/1981/1/012010/metaen_US
dc.titleExpansión caótica polinomial aplicada a funciones de estado límiteen_US
dc.title.translatedPolinomial chaos expansion applied to limit state functionsen_US
dc.contributor.cvlacNelson Afanador García [0000226920]en_US
dc.contributor.cvlacGustavo Guerrero Gómez [0001413037]en_US
dc.contributor.cvlacChristian Nolasco Serna [0000016699]en_US
dc.contributor.googlescholarNelson Afanador García [8Uk1lqUAAAAJ]en_US
dc.contributor.googlescholarGustavo Guerrero Gómez [La8nZsAAAAAJ]en_US
dc.contributor.googlescholarChristian Nolasco Serna [DriRZs4AAAAJ&hl]en_US
dc.contributor.orcidNelson Afanador García [0000-0001-5463-2036]en_US
dc.contributor.orcidGustavo Guerrero Gómez [0000-0002-4316-7113]en_US
dc.contributor.orcidChristian Nolasco Serna [0000-0001-6923-3388]en_US
dc.contributor.researchgateNelson Afanador García [Nelson-Afanador]en_US
dc.contributor.researchgateChristian Nolasco Serna [Christian-Serna-2]en_US
dc.contributor.scopusNelson Afanador García [57202680756]en_US
dc.contributor.scopusGustavo Guerrero Gómez [57205119123]en_US
dc.contributor.scopusChristian Nolasco Serna [57212415000]en_US
dc.description.abstractenglishIn structural projects, ensuring system performance within the established specifications with a maximum level of safety and taking into account the economic constraints of the project is one of the main objectives of structural design. The risks to which any physical system is subjected are called failure probability and are assessed by applying structural reliability analysis methods. The objective of the structural reliability analysis is to ensure that the strength of the elements of the structure is greater than the imposed strength demand over the service life of each of the structural elements. Structural design variables are physical quantities in structural reliability and are considered random, and can be represented in a random vector. The failure probability of a structure is obtained from the evaluation of the uncertainties inherent to the physical variables of the project, through the probability distributions of the random variables. The objective of this work was the application of polynomial chaos expansion to evaluate the failure probability in limit state functions found in the literature using numerical simulation, in order to decrease the sample size for each random variable compared to those needed using Monte Carlo simulation. This research showed that the difference between the sample size between polynomial chaos expansion and Monte Carlo simulation is 5%, saving time and computational effort.en_US
dc.subject.proposalExpansiónen_US
dc.subject.proposalPolinomialen_US
dc.subject.proposalEstado límiteen_US
dc.subject.proposalCaosen_US
dc.subject.keywordsExpansionen_US
dc.subject.keywordsPolynomialen_US
dc.subject.keywordsLimit stateen_US
dc.subject.keywordsChaosen_US
dc.publisher.facultyFacultad ingenieríasen_US
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTen_US
dc.relation.referencesFreudenthal A N 1956 Safety and the probability of structural failure Transactions of the American Society of Civil Engineers 121 1337-1397en_US
dc.relation.referencesMayer M 1926 Die Betriebseinrichtungen in Betriebswissenschaft (Berlin: Springer) 103-112en_US
dc.relation.referencesRzhanitzyn R 1957 A Statistical Method of Design of Building Structures (Canada: National Research Council of Canada) It is necessary to improve the standards of design of building structures 67-81en_US
dc.relation.referencesBasler B 1961 Untersuchungen über den sicherheitsbegriff von bauwerken Schweiz Arch Tierheilkd 27 133-160en_US
dc.relation.referencesCornell C A 1971 First-order Uncertainty Analysis of Soil Deformation and Stability (Hong Kong: University of Hong Kong Press) 129-144en_US
dc.relation.referencesHasofer A M and Lind N C 1974 Exact and invariant second moment code format J. Engrg. Mech. 100 111-121en_US
dc.relation.referencesRackwitz R and Fiessler B 1978 Structural reliability under combined load sequences Comput. Struct. 9 489-494en_US
dc.relation.referencesHohenbichler M and Rackwitz R 1985 Non-normal dependent vectors in structural safety J. Engineering Mechanics Div. 107 1227-1238en_US
dc.relation.referencesDitlevsen O 1981 Principle of normal tail approximation J. Engineering Mechanics Div. 107 1191-1208en_US
dc.relation.referencesDavies R B 1973 Numerical inversion of a characteristic function Biometrika 60 415-417 Google Scholaren_US
dc.relation.referencesBreitung K 1984 Asymptotic approximations for multinormal integrals J. Engrg. Mech. 100 357-366en_US
dc.relation.referencesTvedt L 1990 Distribution of quadratic forms in normal space Journal of Engineering Mechanics 116 1183-1197en_US
dc.relation.referencesField C 1996 Tail areas of linear combinations of chi-squares and non-central chi-squares J. of Statistical Computation and Simulation 15 243-248en_US
dc.relation.referencesMelchers R E and Beck A T 2018 Structural Reliability Analysis and Prediction (Hoboken: John wiley & sons) 1-506en_US
dc.relation.referencesAfanador G N and Noriega S C 2020 Quantification of uncertainty in steel plates subject to fatigue with variable load via chaos expansion polynomial Journal of Physics: Conference Series 1708 1-7en_US
dc.relation.referencesChoi S K, Grandhi R V and Canfield R A 2007 Reliability-based Structural Design (England: London) 1-306en_US
dc.relation.referencesEvangelista F and Afanador-García N 2016 A polynomial chaos expansion approach to the analysis of uncertainty in viscoelastic structural elements DYNA 83 172-182en_US
dc.relation.referencesAfanador N, Noriega Sanchez C J and Nolasco Serna C 2020 Evaluation of uncertainty in determining the physical properties of concrete using Bootstrap Journal of Physics: Conference Series 1645 1-7en_US
dc.relation.referencesBird G A 1981 Monte-Carlo simulation in an engineering context Progress in Astronautics and Aeronautics 74 239-255en_US
dc.relation.referencesBlatman G and Sudret B 2011 Adaptive sparse polynomial chaos expansion based on least angle regression Journal of Computational Physics 230 2345-2367en_US
dc.relation.referencesAfanador N, Sanjuan Y and Medina D 2012 Diseño sísmico de muros de contención en gravedad y voladizo Revista Colombiana de Tecnologias de Avanzada (RCTA) 2 97-104en_US
dc.relation.referencesKewlani G, Crawford J and Iagnemma K 2012 A polynomial chaos approach to the analysis of vehicle dynamics under uncertainty Vehicle System Dynamics 50 749-774en_US
dc.relation.referencesGrooteman F 2008 Adaptive radial-based importance sampling method for structural reliability Structural Safety 30 533-542en_US
dc.relation.referencesSantosh T, Saraf R, Ghosh A and Kushwaka H 2006 Optimum step length selection rule in modified hl-rf method for structural reliability International Journal of Pressure Vessels and Piping 83 742-748en_US
dc.relation.referencesHaldar A and Mahadevam S 2000 Reliability Assessment Using Stochastic Finite Element Analysis (New York: John wiley & sons) 1-312en_US
dc.identifier.DOI10.1088/1742-6596/1981/1/012010en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record