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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/160243
Ver los metadatos del registro completo
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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 |
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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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
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