| dc.contributor.author | Afanador García, Nelson | |
| dc.contributor.author | Guerrero Gómez, Gustavo | |
| dc.contributor.author | Nolasco Serna, Christian | |
| dc.coverage.spatial | Colombia | en_US |
| dc.date.accessioned | 2025-10-06T16:49:11Z | |
| dc.date.available | 2025-10-06T16:49:11Z | |
| dc.date.issued | 2021-07-01 | |
| dc.identifier.issn | 1742-6588 | en_US |
| dc.identifier.uri | https://repositorioinstitucional.ufpso.edu.co/xmlui/handle/20.500.14167/5666 | |
| dc.description.abstract | En 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.iso | en_US | en_US |
| dc.relation | https://iopscience.iop.org/article/10.1088/1742-6596/1981/1/012010/meta | en_US |
| dc.title | Expansión caótica polinomial aplicada a funciones de estado límite | en_US |
| dc.title.translated | Polinomial chaos expansion applied to limit state functions | en_US |
| dc.contributor.cvlac | Nelson Afanador García [0000226920] | en_US |
| dc.contributor.cvlac | Gustavo Guerrero Gómez [0001413037] | en_US |
| dc.contributor.cvlac | Christian Nolasco Serna [0000016699] | en_US |
| dc.contributor.googlescholar | Nelson Afanador García [8Uk1lqUAAAAJ] | en_US |
| dc.contributor.googlescholar | Gustavo Guerrero Gómez [La8nZsAAAAAJ] | en_US |
| dc.contributor.googlescholar | Christian Nolasco Serna [DriRZs4AAAAJ&hl] | en_US |
| dc.contributor.orcid | Nelson Afanador García [0000-0001-5463-2036] | en_US |
| dc.contributor.orcid | Gustavo Guerrero Gómez [0000-0002-4316-7113] | en_US |
| dc.contributor.orcid | Christian Nolasco Serna [0000-0001-6923-3388] | en_US |
| dc.contributor.researchgate | Nelson Afanador García [Nelson-Afanador] | en_US |
| dc.contributor.researchgate | Christian Nolasco Serna [Christian-Serna-2] | en_US |
| dc.contributor.scopus | Nelson Afanador García [57202680756] | en_US |
| dc.contributor.scopus | Gustavo Guerrero Gómez [57205119123] | en_US |
| dc.contributor.scopus | Christian Nolasco Serna [57212415000] | en_US |
| dc.description.abstractenglish | In 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.proposal | Expansión | en_US |
| dc.subject.proposal | Polinomial | en_US |
| dc.subject.proposal | Estado límite | en_US |
| dc.subject.proposal | Caos | en_US |
| dc.subject.keywords | Expansion | en_US |
| dc.subject.keywords | Polynomial | en_US |
| dc.subject.keywords | Limit state | en_US |
| dc.subject.keywords | Chaos | en_US |
| dc.publisher.faculty | Facultad ingenierías | en_US |
| dc.type.redcol | http://purl.org/redcol/resource_type/ART | en_US |
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| dc.identifier.DOI | 10.1088/1742-6596/1981/1/012010 | en_US |