On the use of random sets in geotechnical engineering

ilustraciones, gráficas, tablas

Autores:: Sepúlveda García, Juan José

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv	On the use of random sets in geotechnical engineering
dc.title.translated.spa.fl_str_mv	Sobre el uso de los conjuntos aleatorios en la ingeniería geotécnica
title	On the use of random sets in geotechnical engineering
spellingShingle	On the use of random sets in geotechnical engineering 620 - Ingeniería y operaciones afines Geotecnia Engineering geology Uncertainty Reliability analysis Monte Carlo simulation Subset Simulation Copulas Random Sets theory Imprecise probabilities Lower and upper probabilities of failure Incertidumbre Análisis de confiabilidad Simulación de Subconjuntos Copulas Teoría de conjuntos aleatorios Probabilidades imprecisas Probabilidades de falla superior e inferior Simulación Monte Carlo Análisis numérico Numerical analysis
title_short	On the use of random sets in geotechnical engineering
title_full	On the use of random sets in geotechnical engineering
title_fullStr	On the use of random sets in geotechnical engineering
title_full_unstemmed	On the use of random sets in geotechnical engineering
title_sort	On the use of random sets in geotechnical engineering
dc.creator.fl_str_mv	Sepúlveda García, Juan José
dc.contributor.advisor.none.fl_str_mv	Álvarez Marín, Diego Andrés
dc.contributor.author.none.fl_str_mv	Sepúlveda García, Juan José
dc.contributor.researchgroup.spa.fl_str_mv	Ingeniería Sísmica y Sismología
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines
topic	620 - Ingeniería y operaciones afines Geotecnia Engineering geology Uncertainty Reliability analysis Monte Carlo simulation Subset Simulation Copulas Random Sets theory Imprecise probabilities Lower and upper probabilities of failure Incertidumbre Análisis de confiabilidad Simulación de Subconjuntos Copulas Teoría de conjuntos aleatorios Probabilidades imprecisas Probabilidades de falla superior e inferior Simulación Monte Carlo Análisis numérico Numerical analysis
dc.subject.other.spa.fl_str_mv	Geotecnia
dc.subject.other.eng.fl_str_mv	Engineering geology
dc.subject.proposal.eng.fl_str_mv	Uncertainty Reliability analysis Monte Carlo simulation Subset Simulation Copulas Random Sets theory Imprecise probabilities Lower and upper probabilities of failure
dc.subject.proposal.spa.fl_str_mv	Incertidumbre Análisis de confiabilidad Simulación de Subconjuntos Copulas Teoría de conjuntos aleatorios Probabilidades imprecisas Probabilidades de falla superior e inferior Simulación Monte Carlo
dc.subject.spines.spa.fl_str_mv	Análisis numérico
dc.subject.spines.eng.fl_str_mv	Numerical analysis
description	ilustraciones, gráficas, tablas
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-10-12T22:28:03Z
dc.date.available.none.fl_str_mv	2021-10-12T22:28:03Z
dc.date.issued.none.fl_str_mv	2021-10
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/80527
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/80527 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
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dc.rights.license.spa.fl_str_mv	Atribución-NoComercial 4.0 Internacional
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dc.format.extent.spa.fl_str_mv	ix, 221 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Geotecnia
dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería Civil y Agrícola
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
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dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
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spelling	Atribución-NoComercial 4.0 InternacionalDerechos reservados al autor, 2021http://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Álvarez Marín, Diego Andrés6c57df69d044b4c987e99e5449a87401Sepúlveda García, Juan José43be918ae52b627fb98e60b9d76ce557Ingeniería Sísmica y Sismología2021-10-12T22:28:03Z2021-10-12T22:28:03Z2021-10https://repositorio.unal.edu.co/handle/unal/80527Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasGeotechnics is subject to two major types of uncertainty: aleatory and epistemic. Aleatory uncertainty refers to the inherent variability of materials and their external agents, while epistemic uncertainty refers to the lack of information and knowledge. A comprehensive geotechnical model should take into account these two types of uncertainties in light of a reliability analysis. However, the reliability analyses that are conventionally performed in geotechnical engineering have a series of gaps and approximations that may diverge model estimations from reality. In the first place, reliability analyses have been commonly based on probability theory, which is capable of modeling aleatory but not epistemic uncertainty, so the latter is usually ignored. Second, assumptions are made about the dependence between the basic variables of the models, which are not validated and in most cases are unfounded. Third, obtaining accurate modeling results requires excessive computational costs, which in many cases are unfeasible. In order to fill these gaps in the \emph{state-of-the-art}, this thesis proposes a methodology for the evaluation of reliability in geotechnics, which is computationally efficient, takes into account the dependence between the basic variables and also their aleatory and epistemic uncertainty. Specifically, subset simulation is employed for efficient and accurate calculation of probabilities of failure, copula theory is used to model dependence among basic variables, and random set theory is employed to model both epistemic and aleatory uncertainties. The proposed methodology manages to integrate the three aforementioned developments in a single approach for the analysis of the reliability in geotechnical models. The applicability of this procedure is proved through different practical examples of geotechnical engineering. The results show the efficiency of the proposed algorithm, the importance of dependence in reliability analyses, and the impact that aleatory and epistemic uncertainties have on the final results of the modeling. In conclusion, the proposed method proves to be a fairly complete tool with a very wide application range, so that geotechnical engineers will have the possibility of implementing it in their designs and modeling (in fact, they should).La geotecnia está sujeta a dos grandes tipos de incertidumbre: aleatorias y epistémicas. La incertidumbre aleatoria se manifiesta a través de la variabilidad inherente de los materiales y de sus agentes externos, mientras que la incertidumbre epistémica se manifiesta a través de la escasez de la información y de la falta de conocimiento. Un modelo geotécnico integral debería de tener en cuenta estos dos tipos de incertidumbre a la luz de un análisis de confiabilidad. No obstante, los análisis de confiabilidad que se desarrollan convencionalmente en la ingeniería geotécnica tienen una serie de vacíos y aproximaciones que pueden generar que los resultados de los modelos disten de la realidad. En primer lugar, los análisis de confiabilidad se han basado comúnmente en la teoría de la probabilidad, la cual es capaz de modelar la incertidumbre aleatoria pero no la epistémica, por lo que esta última se suele obviar. En segundo lugar, se realizan supuestos sobre la dependencia entre las variables básicas de los modelos, las cuales no son validadas y en la mayoría de los casos carecen de fundamentos. En tercer lugar, obtener resultados precisos requiere de un costo computacional excesivo, que en muchos casos puede ser inviable. Con el objetivo de suplir estos vacíos en el estado del arte, esta tesis propone una metodología para la evaluación de la confiabilidad en geotecnia, la cual es eficiente computacionalmente, tiene en cuenta la dependencia entre las variables básicas y también su incertidumbre aleatoria y epistémica. Específicamente, se usa el algoritmo subset simulation para el cálculo eficiente y preciso de las probabilidades de falla, se hace uso de la teoría de copulas para modelar la dependencia entre las variables, y se emplea la teoría de random sets para modelar la incertidumbre epistémica y aleatoria. La metodología propuesta logra integrar los tres desarrollos anteriormente mencionados en un único enfoque para el análisis de la confiabilidad de modelos geotécnicos. La aplicabilidad de este enfoque se demuestra a través de diferentes ejemplos prácticos de la ingeniería geotécnica. Los resultados evidencian la eficiencia del algoritmo propuesto, la importancia de la dependencia en los análisis de confiabilidad, y el impacto que las incertidumbres aleatorias y epistémicas tienen en las modelaciones. En conclusión, el enfoque propuesto es una herramienta bastante completa y con una aplicación muy amplia para realizar análisis de confiabilidad, por lo que los geotecnistas tendrán la posibilidad de implementarla en sus diseños y modelaciones (de hecho, ellos deberían usarla). (Texto tomado de la fuente)MaestríaMagíster en Ingeniería - Geotecniaix, 221 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - GeotecniaDepartamento de Ingeniería Civil y AgrícolaFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afinesGeotecniaEngineering geologyUncertaintyReliability analysisMonte Carlo simulationSubset SimulationCopulasRandom Sets theoryImprecise probabilitiesLower and upper probabilities of failureIncertidumbreAnálisis de confiabilidadSimulación de SubconjuntosCopulasTeoría de conjuntos aleatoriosProbabilidades imprecisasProbabilidades de falla superior e inferiorSimulación Monte CarloAnálisis numéricoNumerical analysisOn the use of random sets in geotechnical engineeringSobre el uso de los conjuntos aleatorios en la ingeniería geotécnicaTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAas, K., Czado, C., Frigessi, A., and Bakken, H. 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Computers & structures, 92:283–296.InvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/80527/3/license.txtcccfe52f796b7c63423298c2d3365fc6MD53ORIGINAL1088322519.2021.pdf1088322519.2021.pdfTesis de Maestría en Ingeniería - Geotecniaapplication/pdf2847623https://repositorio.unal.edu.co/bitstream/unal/80527/4/1088322519.2021.pdfba9e1bff78bfe7a1af07e73edbbef971MD54THUMBNAIL1088322519.2021.pdf.jpg1088322519.2021.pdf.jpgGenerated Thumbnailimage/jpeg4324https://repositorio.unal.edu.co/bitstream/unal/80527/5/1088322519.2021.pdf.jpg09ba70d9ebb1e16ca521f306d744c81aMD55unal/80527oai:repositorio.unal.edu.co:unal/805272024-07-31 23:13:08.259Repositorio Institucional Universidad Nacional de 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GVyZWNob3MgZGUgYXV0b3IgcXVlIGNvbmxsZXZlIGxhIGRpc3RyaWJ1Y2nDs24gZGUgZXN0b3MgYXJjaGl2b3MgeSBtZXRhZGF0b3MuCkFsIGhhY2VyIGNsaWMgZW4gZWwgc2lndWllbnRlIGJvdMOzbiwgdXN0ZWQgaW5kaWNhIHF1ZSBlc3TDoSBkZSBhY3VlcmRvIGNvbiBlc3RvcyB0w6lybWlub3MuCgpVTklWRVJTSURBRCBOQUNJT05BTCBERSBDT0xPTUJJQSAtIMOabHRpbWEgbW9kaWZpY2FjacOzbiAyNy8yMC8yMDIwCg==

On the use of random sets in geotechnical engineering

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