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dc.rights.licenseAtribución-NoComercial 4.0 Internacional
dc.contributor.advisorColmenares Montañez, Julio Esteban
dc.contributor.authorBaquero Ramirez, David Andres
dc.date.accessioned2020-03-03T16:59:45Z
dc.date.available2020-03-03T16:59:45Z
dc.date.issued2020-01-30
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/75801
dc.description.abstractEn esta investigación se evaluaron los parámetros de comportamiento mecánico, mediante retro-análisis, de una excavación situada en la ciudad de Jakarta. Se obtuvieron los resultados de cohesión no drenada y módulo de Elasticidad para tres (3) capas de suelo cuyas simulaciones numéricas se ajustaban a los desplazamientos del muro diafragma de la excavación. Se emplearon dos (2) tipos de algoritmo de inteligencia artificial (redes neuronales y algoritmos genéticos). Se describe la construcción del modelo numérico a partir de la información antecedente y posteriormente se efectúa la aplicación de ambos algoritmos al modelo numérico realizado. Se presenta una discusión de los elementos de mayor efecto en los análisis y la dispersión de los resultados obtenidos con cada uno de los algoritmos. Los parámetros obtenidos, mediante el retro análisis, fueron comparados con los resultados de los ensayos de campo y laboratorio. Las curvas obtenidas por simulaciones numéricas con los parámetros conseguidos por inteligencia artificial presentan una alta correlación con los desplazamientos monitoreados del muro, principalmente con los parámetros obtenidos por algoritmos genéticos, por otra parte, los resultados obtenidos con las redes nueronales presentaron una tendencia similar con una mayor eficiencia, pero con un grado de exactitud menor.
dc.description.abstractThe engineering parameters of mechanical behavior were evaluated for a big excavation, located in the city of Jakarta, through a back-analysis process. The results of undrained shear strength and modulus of elasticity were obtained for three layers of soil whose numerical simulations were adjusted to the displacements of the diaphragm wall of the excavation. Two (2) types of artificial intelligence algorithm (neural networks and genetic algorithms) were used. The construction of the numerical model based on the antecedent information is described. The application of both algorithms to the numerical model was implemented. A discussion of the elements with the greatest effect in the analysis and the dispersion of the results obtained with each of the algorithms is presented. The parameters obtained by means of the back-analysis process were compared with field and laboratory tests. The curves obtained by numerical simulations with the obtained parameters by artificial intelligence show a high correlation with the measured movements of the wall, mainly with the results obtained by genetic algorithm. Additionally, results obtained with neural network showed a similar tendency, with less accuracy but greater efficiency.
dc.format.extent152
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subject.ddcIngenieria Civil
dc.subject.ddcInteligencia Artificial
dc.titleAnálisis de métodos retrogresivos mediante uso de Redes Neuronales y Algoritmos Geneticos
dc.typeOtro
dc.rights.spaAcceso abierto
dc.description.additionalMagister en Ingeniería Geotecnia. Línea de Investigación: Modelación y análisis en geotecnia
dc.type.driverinfo:eu-repo/semantics/other
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.contributor.researchgroupGrupo de Investigacion: Geotechnical Engineering Knowledge and Innovation: GENKI
dc.description.degreelevelMaestría
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotá
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.proposalRetro-análisis
dc.subject.proposalBack analysis, deep excavation, neural networks, genetic algorithms
dc.subject.proposalBack analysis
dc.subject.proposalExcavaciones profundas
dc.subject.proposalDeep excavation
dc.subject.proposalRedes neuronales
dc.subject.proposalNeural networks
dc.subject.proposalAlgoritmos genéticos
dc.subject.proposalGenetic algorithms
dc.type.coarhttp://purl.org/coar/resource_type/c_1843
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
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oaire.accessrightshttp://purl.org/coar/access_right/c_abf2


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Atribución-NoComercial 4.0 InternacionalThis work is licensed under a Creative Commons Reconocimiento-NoComercial 4.0.This document has been deposited by the author (s) under the following certificate of deposit