Modeling of hysteretic structural systems using multilayer perceptrons and physics-guiding techniques
| dc.contributor.advisor | Álvarez Marín, Diego Andrés | |
| dc.contributor.advisor | Bedoya Ruíz, Daniel Alveiro | |
| dc.contributor.author | Delgado Trujillo, Juan Sebastián | |
| dc.contributor.orcid | Delgado Trujillo, Juan Sebastián [0000-0001-7135-0460] | spa |
| dc.contributor.researchgroup | Ingeniería Sísmica y Sismología | spa |
| dc.date.accessioned | 2023-02-27T13:39:58Z | |
| dc.date.available | 2023-02-27T13:39:58Z | |
| dc.date.issued | 2022 | |
| dc.description | graficas, tablas | spa |
| dc.description.abstract | This research develops a framework for the modeling and identification of hysteretic structural systems, which employs multilayer perceptrons and physical principles of structures. This framework consists of three hysteretic models and their training algorithms, and it is based on two models of the scientific machine learning field, called universal ordinary differential equations (UODEs) and physics-guided neural networks (PGNNs). The proposed hysteretic models are UODEs and correspond to equations of motion with system state dynamics, where multilayer perceptrons approximate the unknown components of the dynamics. The training of the models uses the theory of PGNNs and considers data and the physical principles of structures in order to identify the system dynamics and enforce such principles into the models. The proposed framework is validated on experimental data of ferrocement and recycled plastic lumber walls. (Texto tomado de la fuente) | eng |
| dc.description.abstract | Esta investigación desarrolla una metodología para la modelación e identificación de sistemas estructurales histeréticos, la cual emplea perceptrones multicapa y principios físicos de las estructuras. Esta metodología consiste en tres modelos histeréticos y su algoritmo de entrenamiento, y se basa en dos modelos del machine learning científico llamados ecuaciones diferenciales ordinarias universales (UODEs) y redes neuronales guiadas por la física (PGNNs). Los modelos histeréticos propuestos son UODEs y corresponden a ecuaciones de movimiento con dinámicas del estado del sistema, en donde los perceptrones multicapa aproximan los componentes desconocidos de la dinámica. El entrenamiento usa la teoría de las PGNNs y considera los datos y los principios físicos de las estructuras para identificar la dinámica del sistema e inducir dichos principios en los modelos. La metodología propuesta es validada con datos experimentales de muros de ferrocemento y madera plástica reciclada. | spa |
| dc.description.curriculararea | Ingeniería Civil.Sede Manizales | spa |
| dc.description.degreelevel | Maestría | spa |
| dc.description.degreename | Magíster en Ingeniería - Estructuras | spa |
| dc.description.researcharea | Nonlinear Dynamics Modelling | spa |
| dc.format.extent | xv, 110 páginas | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.identifier.instname | Universidad Nacional de Colombia | spa |
| dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia | spa |
| dc.identifier.repourl | https://repositorio.unal.edu.co/ | spa |
| dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/83560 | |
| dc.language.iso | eng | spa |
| dc.publisher | Universidad Nacional de Colombia | spa |
| dc.publisher.branch | Universidad Nacional de Colombia - Sede Manizales | spa |
| dc.publisher.faculty | Facultad de Ingeniería y Arquitectura | spa |
| dc.publisher.place | Manizales, Colombia | spa |
| dc.publisher.program | Manizales - Ingeniería y Arquitectura - Maestría en Ingeniería - Estructuras | spa |
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| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.license | Atribución-NoComercial-SinDerivadas 4.0 Internacional | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | spa |
| dc.subject.ddc | 620 - Ingeniería y operaciones afines::624 - Ingeniería civil | spa |
| dc.subject.proposal | Hysteresis modeling | eng |
| dc.subject.proposal | Nonlinear structural identification | eng |
| dc.subject.proposal | Physical consistency | eng |
| dc.subject.proposal | Scientific machine learning | eng |
| dc.subject.proposal | Universal ordinary differential equations | eng |
| dc.subject.proposal | Physics-guided neural networks | eng |
| dc.subject.proposal | Modelación histerética | spa |
| dc.subject.proposal | Identificación estructural no lineal | spa |
| dc.subject.proposal | Consistencia física | spa |
| dc.subject.proposal | Machine learning científico | spa |
| dc.subject.proposal | Ecuaciones diferenciales ordinarias universales | spa |
| dc.subject.proposal | Redes neuronales guiadas por la física | spa |
| dc.subject.unesco | Ingeniería de estructuras | spa |
| dc.subject.unesco | Construction engineering | eng |
| dc.title | Modeling of hysteretic structural systems using multilayer perceptrons and physics-guiding techniques | eng |
| dc.title.translated | Modelación de sistemas estructurales histeréticos usando perceptrones multicapa y técnicas de guiado físico | spa |
| dc.type | Trabajo de grado - Maestría | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_bdcc | spa |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
| dc.type.content | Image | spa |
| dc.type.content | Text | spa |
| dc.type.driver | info:eu-repo/semantics/masterThesis | spa |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
| dcterms.audience.professionaldevelopment | Estudiantes | spa |
| dcterms.audience.professionaldevelopment | Investigadores | spa |
| dcterms.audience.professionaldevelopment | Maestros | spa |
| dcterms.audience.professionaldevelopment | Público general | spa |
| oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |
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