Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto

El proyecto presenta el desarrollo e implementación de dos algoritmos para la segmentación de la zona no carbonatada en probetas de estructuras de concreto con acero reforzado partiendo de una fotografía del ensayo de fenolftaleína aplicado a la probeta. En primer lugar, se implementó un algoritmo b...

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Autores:
Ruiz Madera, Christian Camilo
Tipo de recurso:
Fecha de publicación:
2019
Institución:
Universidad Militar Nueva Granada
Repositorio:
Repositorio UMNG
Idioma:
spa
OAI Identifier:
oai:repository.unimilitar.edu.co:10654/21214
Acceso en línea:
http://hdl.handle.net/10654/21214
Palabra clave:
ALGORITMOS
INTELIGENCIA ARTIFICIAL
PROCESAMIENTO DIGITAL DE IMAGENES
Carbonation
Segmentation
Concrete
Artificial intelligence
Carbonatación
Segmentación
Concreto
Inteligencia artificial
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License
Derechos Reservados - Universidad Militar Nueva Granada, 2019
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oai_identifier_str oai:repository.unimilitar.edu.co:10654/21214
network_acronym_str UNIMILTAR2
network_name_str Repositorio UMNG
repository_id_str
dc.title.spa.fl_str_mv Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto
dc.title.translated.spa.fl_str_mv Implementation of artificial intelligence algorithms and digital image processing in the determination of carbonation depth in concrete structures
title Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto
spellingShingle Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto
ALGORITMOS
INTELIGENCIA ARTIFICIAL
PROCESAMIENTO DIGITAL DE IMAGENES
Carbonation
Segmentation
Concrete
Artificial intelligence
Carbonatación
Segmentación
Concreto
Inteligencia artificial
title_short Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto
title_full Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto
title_fullStr Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto
title_full_unstemmed Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto
title_sort Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concreto
dc.creator.fl_str_mv Ruiz Madera, Christian Camilo
dc.contributor.advisor.spa.fl_str_mv Caballero Gomez, Jose Luis
dc.contributor.author.spa.fl_str_mv Ruiz Madera, Christian Camilo
dc.subject.lemb.spa.fl_str_mv ALGORITMOS
INTELIGENCIA ARTIFICIAL
PROCESAMIENTO DIGITAL DE IMAGENES
topic ALGORITMOS
INTELIGENCIA ARTIFICIAL
PROCESAMIENTO DIGITAL DE IMAGENES
Carbonation
Segmentation
Concrete
Artificial intelligence
Carbonatación
Segmentación
Concreto
Inteligencia artificial
dc.subject.keywords.spa.fl_str_mv Carbonation
Segmentation
Concrete
Artificial intelligence
dc.subject.proposal.spa.fl_str_mv Carbonatación
Segmentación
Concreto
Inteligencia artificial
description El proyecto presenta el desarrollo e implementación de dos algoritmos para la segmentación de la zona no carbonatada en probetas de estructuras de concreto con acero reforzado partiendo de una fotografía del ensayo de fenolftaleína aplicado a la probeta. En primer lugar, se implementó un algoritmo basado en procesamiento digital de imágenes (PDI) usando técnicas como operaciones morfológicas y apoyándose en la información de los canales RGB como herramienta principal de segmentación. El segundo algoritmo está basado en inteligencia artificial, más específicamente en redes de Kohonen, aunque usando también algunas técnicas de PDI para refinar los resultados. En este proceso, el algoritmo por inteligencia artificial evidenció mejores resultados que el algoritmo usando solo PDI en aproximadamente un 20% en la segmentación de la zona no carbonatada de la probeta de concreto.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2019-05-17T16:39:31Z
2019-12-26T22:10:39Z
dc.date.available.none.fl_str_mv 2019-05-17T16:39:31Z
2019-12-26T22:10:39Z
dc.date.issued.none.fl_str_mv 2019-04-11
dc.type.spa.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_7a1f
dc.type.local.spa.fl_str_mv Trabajo de grado
dc.type.dcmi-type-vocabulary.spa.fl_str_mv Text
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10654/21214
url http://hdl.handle.net/10654/21214
dc.language.iso.spa.fl_str_mv spa
language spa
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E. I. Serrano Ixtepan, D.; Moreno, “Películas barrera: Una opción contra la carbonatación del concreto reforzado,” Ing. Rev. Académica la FI-UADY, vol. 10, no. 2, pp. 37–45, 2006.
P. Garcés Terradillos, M. Á. Llorca, and E. Gómez, CORROSIÓN DE ARMADURAS EN ESTRUCTURAS DE HORMIGÓN ARMADO, 1st ed. España, 2008.
D. J. Prada Campos, “Evaluación de la presencia de carbonatación en puentes vehiculares de concreto, localidades de Usaquén y Fontibón,” 2014.
G. R. Rodriguez, W. A. A. Chaparro, and R. V. Aravena, “SOFTWARE PARA EL CÁLCULO DE LA VELOCIDAD DE DETERIORO DE LOS HORMIGONES SOMETIDOS A CARBONATACIÓN,” Rev. Latinoam. Metal. y Mater., vol. 0, no. 0, pp. 45–54, Feb. 2014.
C. Jiang, Q. Huang, X. Gu, and W. Zhang, “Experimental investigation on carbonation in fatigue-damaged concrete,” Cem. Concr. Res., vol. 99, pp. 38–52, Sep. 2017.
H. Xu, Z. Chen, S. Li, W. Huang, and D. Ma, “Carbonation Test Study on Low Calcium Fly Ash Concrete,” Appl. Mech. Mater., vol. 34, pp. 327–331, 2010.
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J. He, Q. Gao, Y. Wu, J. He, and X. Pu, “Study on improvement of carbonation resistance of alkali-activated slag concrete,” Constr. Build. Mater., vol. 176, pp. 60–67, Jul. 2018.
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J. Tang, J. Wu, Z. Zou, A. Yue, and A. Mueller, “Influence of axial loading and carbonation age on the carbonation resistance of recycled aggregate concrete,” Constr. Build. Mater., vol. 173, pp. 707–717, Jun. 2018.
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spelling Caballero Gomez, Jose LuisRuiz Madera, Christian CamiloIngeniero en MecatrónicaCalle 1002019-05-17T16:39:31Z2019-12-26T22:10:39Z2019-05-17T16:39:31Z2019-12-26T22:10:39Z2019-04-11http://hdl.handle.net/10654/21214El proyecto presenta el desarrollo e implementación de dos algoritmos para la segmentación de la zona no carbonatada en probetas de estructuras de concreto con acero reforzado partiendo de una fotografía del ensayo de fenolftaleína aplicado a la probeta. En primer lugar, se implementó un algoritmo basado en procesamiento digital de imágenes (PDI) usando técnicas como operaciones morfológicas y apoyándose en la información de los canales RGB como herramienta principal de segmentación. El segundo algoritmo está basado en inteligencia artificial, más específicamente en redes de Kohonen, aunque usando también algunas técnicas de PDI para refinar los resultados. En este proceso, el algoritmo por inteligencia artificial evidenció mejores resultados que el algoritmo usando solo PDI en aproximadamente un 20% en la segmentación de la zona no carbonatada de la probeta de concreto.1. Capítulo 1 16 Introducción 16 1.1 Planteamiento del problema 16 1.2 Justificación 18 1.3 Objetivos 19 1.3.1 Objetivo general 19 1.3.2 Objetivos específicos 19 1.4 Antecedentes 20 2. Capítulo 2 24 Marco referencial 24 2.1 Carbonatación 24 2.2 Procesamiento digital de imágenes 25 2.2.1 Segmentación de imágenes 25 2.2.2 Operaciones morfológicas 27 2.3 Inteligencia artificial 29 2.3.1 Neuronas biológicas 29 2.3.2 Neurona artificial 30 2.3.3 Redes neuronales 31 2.3.4 Mapa autoorganizativo de Kohonen (KSOM) 33 2.3.5 Funcionamiento del KSOM 38 3. Capítulo 3 43 Diseño de cámara de iluminación 43 3.1 Matriz QFD 43 3.2 Diseño conceptual 45 3.3 Diseño CAD 47 3.4 Comparación de brillo y contraste 48 4. Capítulo 4 49 Metodología 49 4.1 Metodología PDI 49 4.2 Metodología IA 51 4.3 Procesamiento digital de imágenes 51 4.3.1 Escala de grises 51 4.3.2 Umbralización 53 4.3.3 Operaciones morfológicas 54 4.3.4 Selección área más grande no carbonatada 56 4.3.5 Resultados adicionales 57 4.3.6 Algoritmo para probetas circulares 58 4.4 Implementación KSOM 63 4.5 Calidad de la segmentación 69 4.6 Medición de la profundidad de carbonatación 70 4.6.1 Comparación de resultados 75 4.7 Ensayos experimentales 78 4.7.1 Interfaz grafica 79 5. Análisis de resultados 80 6. Conclusiones y recomendaciones 82 6.1 Conclusiones 82 6.2 Recomendaciones 83The project presents the development and implementation of two algorithms for the segmentation of the non-carbonated zone in specimens of concrete structures with reinforced steel starting from a photograph of the phenolphthalein test applied to the specimen. First, an algorithm based on digital image processing (PDI) was implemented using techniques such as morphological operations and relying on RGB channel information as the main segmentation tool. The second algorithm is based on artificial intelligence, more specifically on Kohonen networks, but also using some PDI techniques to refine the results. In this process, the artificial intelligence algorithm showed better results than the algorithm using only PDI by approximately 20% in the segmentation of the non-carbonated zone of the concrete test tube.Pregradoapplication/pdfspaDerechos Reservados - Universidad Militar Nueva Granada, 2019https://creativecommons.org/licenses/by-nc-nd/2.5/co/Atribución-NoComercial-SinDerivadashttp://purl.org/coar/access_right/c_abf2Implementación de algoritmos de inteligencia artificial y procesamiento digital de imágenes en la determinación de la profundidad de carbonatación en estructuras de concretoImplementation of artificial intelligence algorithms and digital image processing in the determination of carbonation depth in concrete structuresinfo:eu-repo/semantics/bachelorThesisTrabajo de gradoTexthttp://purl.org/coar/resource_type/c_7a1fALGORITMOSINTELIGENCIA ARTIFICIALPROCESAMIENTO DIGITAL DE IMAGENESCarbonationSegmentationConcreteArtificial intelligenceCarbonataciónSegmentaciónConcretoInteligencia artificialFacultad de IngenieríadIngeniería en MecatrónicaIngeniería - Ingeniería en MecatrónicaUniversidad Militar Nueva GranadaA. M. Carvajal, C. Silva, J. Valiente, and A. Venegas, “Efectos de la Carbonatación Acelerada en Distintos Tipos de Cemento y Hormigones,” Rev. la construcción, vol. 6, no. 1, 2007.E. I. Serrano Ixtepan, D.; Moreno, “Películas barrera: Una opción contra la carbonatación del concreto reforzado,” Ing. Rev. Académica la FI-UADY, vol. 10, no. 2, pp. 37–45, 2006.P. Garcés Terradillos, M. Á. Llorca, and E. Gómez, CORROSIÓN DE ARMADURAS EN ESTRUCTURAS DE HORMIGÓN ARMADO, 1st ed. España, 2008.D. J. Prada Campos, “Evaluación de la presencia de carbonatación en puentes vehiculares de concreto, localidades de Usaquén y Fontibón,” 2014.G. R. Rodriguez, W. A. A. Chaparro, and R. V. Aravena, “SOFTWARE PARA EL CÁLCULO DE LA VELOCIDAD DE DETERIORO DE LOS HORMIGONES SOMETIDOS A CARBONATACIÓN,” Rev. Latinoam. Metal. y Mater., vol. 0, no. 0, pp. 45–54, Feb. 2014.C. Jiang, Q. Huang, X. Gu, and W. Zhang, “Experimental investigation on carbonation in fatigue-damaged concrete,” Cem. Concr. Res., vol. 99, pp. 38–52, Sep. 2017.H. Xu, Z. Chen, S. Li, W. Huang, and D. Ma, “Carbonation Test Study on Low Calcium Fly Ash Concrete,” Appl. Mech. Mater., vol. 34, pp. 327–331, 2010.S. Makridakis, “The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms,” Futures, vol. 90, pp. 46–60, Jun. 2017.L. Antonio, B. Mehul, O. Alessandro, and V. David, “The role of cognitive architectures in general artificial intelligence,” Cogn. Syst. Res., vol. 48, pp. 1–3, May 2018.I. Janotka, M. Bačuvčík, and P. Paulík, “Low carbonation of concrete found on 100-year-old bridges,” Case Stud. Constr. Mater., vol. 8, pp. 97–115, Jun. 2018.D. Li et al., “Evaluating the effect of external and internal factors on carbonation of existing concrete building structures,” Constr. Build. Mater., vol. 167, pp. 73–81, Apr. 2018.S. R. da Silva and J. J. de Oliveira Andrade, “Investigation of mechanical properties and carbonation of concretes with construction and demolition waste and fly ash,” Constr. Build. Mater., vol. 153, pp. 704–715, Oct. 2017.J. He, Q. Gao, Y. Wu, J. He, and X. Pu, “Study on improvement of carbonation resistance of alkali-activated slag concrete,” Constr. Build. Mater., vol. 176, pp. 60–67, Jul. 2018.S. C. Paul, B. Panda, Y. Huang, A. Garg, and X. Peng, “An empirical model design for evaluation and estimation of carbonation depth in concrete,” Measurement, vol. 124, pp. 205–210, Aug. 2018.J. Tang, J. Wu, Z. Zou, A. Yue, and A. Mueller, “Influence of axial loading and carbonation age on the carbonation resistance of recycled aggregate concrete,” Constr. Build. Mater., vol. 173, pp. 707–717, Jun. 2018.G. Li and X. Zhou, “The Research of Predicting the Carbonation Depth of Concrete with Time-series Analysis.”R. 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Mater., vol. 162, pp. 847–856, 2018.ORIGINALRuizMaderaChristianCamilo2019.pdfTesisapplication/pdf3761556http://repository.unimilitar.edu.co/bitstream/10654/21214/1/RuizMaderaChristianCamilo2019.pdffd1c1b1a7335c17635aa5dbd4c5fd0dbMD51LICENSElicense.txttext/plain2898http://repository.unimilitar.edu.co/bitstream/10654/21214/2/license.txt520e8f0b4e8d2d5c25366f2f78f584b0MD52THUMBNAILRuizMaderaChristianCamilo2019.pdf.jpgIM Thumbnailimage/jpeg6145http://repository.unimilitar.edu.co/bitstream/10654/21214/3/RuizMaderaChristianCamilo2019.pdf.jpg06e9761b6f7afb7594dc75eefe02b971MD5310654/21214oai:repository.unimilitar.edu.co:10654/212142020-06-30 13:05:00.128Repositorio Institucional UMNGbibliodigital@unimilitar.edu.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