Generating BIM model from structural and architectural plans using Artificial Intelligence

Autores
Urbieta, Martín; Urbieta, Mario Matías; Laborde, Tomás; Villarreal, Guillermo; Rossi, Gustavo Héctor
Año de publicación
2023
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Over the last few decades, building development has been recorded using hand-made blueprints before CAD tools appeared and later with digital building plans. As a consequence, there is alarge amount of information in millions of assets that are hard to process because of their analog nature. Since adopting the Building Information Model (BIM) approach, any new building plan can be subject to sophisticated validations and analysis. However, legacy analog plans cannot profit from sophisticated BIM analysis, and it is not feasible to manually generate BIM representations at low cost. There is a demand for BIM models of existing buildings that are feasible to be integrated into a workflow for building energy retrofitting. This paper presents a novel approach to generating BIM Models based on artificial intelligence algorithms by parsing architectural and structural drawings. To identify elements from blueprints and generate the model, we first trained the Mask R-CNN framework with our dataset of 9 concrete buildings composed of architectural and structural blueprints. The outcome of the process is a BIM model corresponding to one of the multi-storey buildings using the Industry Foundation Classes (IFC) format. Building development has been recorded using hand-made blueprints before CAD tools appeared and later with digital building plans.
Laboratorio de Investigación y Formación en Informática Avanzada
Materia
Informática
BIM
Machine-Learning
Blueprints
Floor plans
As-build
Building
Model generation
IFC
2D drawing
Architectural plans
Structural Plans
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/160243

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oai_identifier_str oai:sedici.unlp.edu.ar:10915/160243
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Generating BIM model from structural and architectural plans using Artificial IntelligenceUrbieta, MartínUrbieta, Mario MatíasLaborde, TomásVillarreal, GuillermoRossi, Gustavo HéctorInformáticaBIMMachine-LearningBlueprintsFloor plansAs-buildBuildingModel generationIFC2D drawingArchitectural plansStructural PlansOver the last few decades, building development has been recorded using hand-made blueprints before CAD tools appeared and later with digital building plans. As a consequence, there is alarge amount of information in millions of assets that are hard to process because of their analog nature. Since adopting the Building Information Model (BIM) approach, any new building plan can be subject to sophisticated validations and analysis. However, legacy analog plans cannot profit from sophisticated BIM analysis, and it is not feasible to manually generate BIM representations at low cost. There is a demand for BIM models of existing buildings that are feasible to be integrated into a workflow for building energy retrofitting. This paper presents a novel approach to generating BIM Models based on artificial intelligence algorithms by parsing architectural and structural drawings. To identify elements from blueprints and generate the model, we first trained the Mask R-CNN framework with our dataset of 9 concrete buildings composed of architectural and structural blueprints. The outcome of the process is a BIM model corresponding to one of the multi-storey buildings using the Industry Foundation Classes (IFC) format. Building development has been recorded using hand-made blueprints before CAD tools appeared and later with digital building plans.Laboratorio de Investigación y Formación en Informática Avanzada2023-09-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/160243enginfo:eu-repo/semantics/altIdentifier/issn/2352-7102info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jobe.2023.107672info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:13:55Zoai:sedici.unlp.edu.ar:10915/160243Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:13:55.684SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Generating BIM model from structural and architectural plans using Artificial Intelligence
title Generating BIM model from structural and architectural plans using Artificial Intelligence
spellingShingle Generating BIM model from structural and architectural plans using Artificial Intelligence
Urbieta, Martín
Informática
BIM
Machine-Learning
Blueprints
Floor plans
As-build
Building
Model generation
IFC
2D drawing
Architectural plans
Structural Plans
title_short Generating BIM model from structural and architectural plans using Artificial Intelligence
title_full Generating BIM model from structural and architectural plans using Artificial Intelligence
title_fullStr Generating BIM model from structural and architectural plans using Artificial Intelligence
title_full_unstemmed Generating BIM model from structural and architectural plans using Artificial Intelligence
title_sort Generating BIM model from structural and architectural plans using Artificial Intelligence
dc.creator.none.fl_str_mv Urbieta, Martín
Urbieta, Mario Matías
Laborde, Tomás
Villarreal, Guillermo
Rossi, Gustavo Héctor
author Urbieta, Martín
author_facet Urbieta, Martín
Urbieta, Mario Matías
Laborde, Tomás
Villarreal, Guillermo
Rossi, Gustavo Héctor
author_role author
author2 Urbieta, Mario Matías
Laborde, Tomás
Villarreal, Guillermo
Rossi, Gustavo Héctor
author2_role author
author
author
author
dc.subject.none.fl_str_mv Informática
BIM
Machine-Learning
Blueprints
Floor plans
As-build
Building
Model generation
IFC
2D drawing
Architectural plans
Structural Plans
topic Informática
BIM
Machine-Learning
Blueprints
Floor plans
As-build
Building
Model generation
IFC
2D drawing
Architectural plans
Structural Plans
dc.description.none.fl_txt_mv Over the last few decades, building development has been recorded using hand-made blueprints before CAD tools appeared and later with digital building plans. As a consequence, there is alarge amount of information in millions of assets that are hard to process because of their analog nature. Since adopting the Building Information Model (BIM) approach, any new building plan can be subject to sophisticated validations and analysis. However, legacy analog plans cannot profit from sophisticated BIM analysis, and it is not feasible to manually generate BIM representations at low cost. There is a demand for BIM models of existing buildings that are feasible to be integrated into a workflow for building energy retrofitting. This paper presents a novel approach to generating BIM Models based on artificial intelligence algorithms by parsing architectural and structural drawings. To identify elements from blueprints and generate the model, we first trained the Mask R-CNN framework with our dataset of 9 concrete buildings composed of architectural and structural blueprints. The outcome of the process is a BIM model corresponding to one of the multi-storey buildings using the Industry Foundation Classes (IFC) format. Building development has been recorded using hand-made blueprints before CAD tools appeared and later with digital building plans.
Laboratorio de Investigación y Formación en Informática Avanzada
description Over the last few decades, building development has been recorded using hand-made blueprints before CAD tools appeared and later with digital building plans. As a consequence, there is alarge amount of information in millions of assets that are hard to process because of their analog nature. Since adopting the Building Information Model (BIM) approach, any new building plan can be subject to sophisticated validations and analysis. However, legacy analog plans cannot profit from sophisticated BIM analysis, and it is not feasible to manually generate BIM representations at low cost. There is a demand for BIM models of existing buildings that are feasible to be integrated into a workflow for building energy retrofitting. This paper presents a novel approach to generating BIM Models based on artificial intelligence algorithms by parsing architectural and structural drawings. To identify elements from blueprints and generate the model, we first trained the Mask R-CNN framework with our dataset of 9 concrete buildings composed of architectural and structural blueprints. The outcome of the process is a BIM model corresponding to one of the multi-storey buildings using the Industry Foundation Classes (IFC) format. Building development has been recorded using hand-made blueprints before CAD tools appeared and later with digital building plans.
publishDate 2023
dc.date.none.fl_str_mv 2023-09-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/160243
url http://sedici.unlp.edu.ar/handle/10915/160243
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/2352-7102
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jobe.2023.107672
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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