Cross-disciplinary Semantic Building Fingerprints – Knowledge graphs to store topological building information derived from semantic building models (BIM) to apply methods of artificial intelligence (AI) throughout the life cycle of buildings

Christoph Langenhan; Jessica Bielski; Christoph Ziegler; Viktor Eisenstadt; Klaus-Dieter Althoff; Andreas Dengel

Cross-disciplinary Semantic Building Fingerprints – Knowledge graphs to store topological building information derived from semantic building models (BIM) to apply methods of artificial intelligence (AI) throughout the life cycle of buildings

Christoph Langenhan, Jessica Bielski, Christoph Ziegler, Viktor Eisenstadt, Klaus-Dieter Althoff, Andreas Dengel

Lehrstuhl für Architekturinformatik

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

Abstract

The advancing digitalization in the building sector with the possibility to store and retrieve large amounts of data has the potential to digitally support planners with extensive design and construction information. Large amounts of semi-structured three-dimensional geometric data of buildings are usually available today, but the topological relationships are rarely explicitly described and thus not directly usable with computational methods of AI. To this end, we propose methods for indexing spatial configurations inspired by the similarity analysis of incomplete human fingerprints, since the early design stage of architectural design is characterized by incomplete information. For this, the topology of spatial configurations is extracted from Building Information Modelling (BIM) data and represented as graphs. In the paper, semantic building fingerprints (SBFs) and semantic urban fingerprints (SUFs), as well as options to use them with AI methods are described.

Originalsprache	Deutsch
Titel	International Conference of the Association for Computer-Aided Architectural Design Research in Asia
Herausgeber (Verlag)	The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA)
Publikationsstatus	Veröffentlicht - 2023

Publikationsreihe

Name	CAADRIA
Herausgeber (Verlag)	Association for Computer-Aided Architectural Design Research in Asia

Schlagwörter

LOCenter
Conceptual design
building information modelling
knowledge graph
artificial intelligence

Dieses zitieren

Langenhan, C., Bielski, J., Ziegler, C., Eisenstadt, V., Althoff, K.-D., & Dengel, A. (2023). Cross-disciplinary Semantic Building Fingerprints – Knowledge graphs to store topological building information derived from semantic building models (BIM) to apply methods of artificial intelligence (AI) throughout the life cycle of buildings. in International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA). The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA).

Langenhan, Christoph ; Bielski, Jessica ; Ziegler, Christoph et al. / Cross-disciplinary Semantic Building Fingerprints – Knowledge graphs to store topological building information derived from semantic building models (BIM) to apply methods of artificial intelligence (AI) throughout the life cycle of buildings. International Conference of the Association for Computer-Aided Architectural Design Research in Asia. The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), 2023. (CAADRIA).

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title = "Cross-disciplinary Semantic Building Fingerprints – Knowledge graphs to store topological building information derived from semantic building models (BIM) to apply methods of artificial intelligence (AI) throughout the life cycle of buildings",

abstract = "The advancing digitalization in the building sector with the possibility to store and retrieve large amounts of data has the potential to digitally support planners with extensive design and construction information. Large amounts of semi-structured three-dimensional geometric data of buildings are usually available today, but the topological relationships are rarely explicitly described and thus not directly usable with computational methods of AI. To this end, we propose methods for indexing spatial configurations inspired by the similarity analysis of incomplete human fingerprints, since the early design stage of architectural design is characterized by incomplete information. For this, the topology of spatial configurations is extracted from Building Information Modelling (BIM) data and represented as graphs. In the paper, semantic building fingerprints (SBFs) and semantic urban fingerprints (SUFs), as well as options to use them with AI methods are described.",

keywords = "LOCenter, Conceptual design, building information modelling, knowledge graph, artificial intelligence",

author = "Christoph Langenhan and Jessica Bielski and Christoph Ziegler and Viktor Eisenstadt and Klaus-Dieter Althoff and Andreas Dengel",

year = "2023",

language = "Deutsch",

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Langenhan, C, Bielski, J, Ziegler, C, Eisenstadt, V, Althoff, K-D & Dengel, A 2023, Cross-disciplinary Semantic Building Fingerprints – Knowledge graphs to store topological building information derived from semantic building models (BIM) to apply methods of artificial intelligence (AI) throughout the life cycle of buildings. in International Conference of the Association for Computer-Aided Architectural Design Research in Asia. CAADRIA, The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA).

Cross-disciplinary Semantic Building Fingerprints – Knowledge graphs to store topological building information derived from semantic building models (BIM) to apply methods of artificial intelligence (AI) throughout the life cycle of buildings. / Langenhan, Christoph; Bielski, Jessica; Ziegler, Christoph et al.
International Conference of the Association for Computer-Aided Architectural Design Research in Asia. The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), 2023. (CAADRIA).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

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T1 - Cross-disciplinary Semantic Building Fingerprints – Knowledge graphs to store topological building information derived from semantic building models (BIM) to apply methods of artificial intelligence (AI) throughout the life cycle of buildings

AU - Langenhan, Christoph

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AU - Ziegler, Christoph

AU - Eisenstadt, Viktor

AU - Althoff, Klaus-Dieter

AU - Dengel, Andreas

PY - 2023

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N2 - The advancing digitalization in the building sector with the possibility to store and retrieve large amounts of data has the potential to digitally support planners with extensive design and construction information. Large amounts of semi-structured three-dimensional geometric data of buildings are usually available today, but the topological relationships are rarely explicitly described and thus not directly usable with computational methods of AI. To this end, we propose methods for indexing spatial configurations inspired by the similarity analysis of incomplete human fingerprints, since the early design stage of architectural design is characterized by incomplete information. For this, the topology of spatial configurations is extracted from Building Information Modelling (BIM) data and represented as graphs. In the paper, semantic building fingerprints (SBFs) and semantic urban fingerprints (SUFs), as well as options to use them with AI methods are described.

AB - The advancing digitalization in the building sector with the possibility to store and retrieve large amounts of data has the potential to digitally support planners with extensive design and construction information. Large amounts of semi-structured three-dimensional geometric data of buildings are usually available today, but the topological relationships are rarely explicitly described and thus not directly usable with computational methods of AI. To this end, we propose methods for indexing spatial configurations inspired by the similarity analysis of incomplete human fingerprints, since the early design stage of architectural design is characterized by incomplete information. For this, the topology of spatial configurations is extracted from Building Information Modelling (BIM) data and represented as graphs. In the paper, semantic building fingerprints (SBFs) and semantic urban fingerprints (SUFs), as well as options to use them with AI methods are described.

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Langenhan C, Bielski J, Ziegler C, Eisenstadt V, Althoff KD, Dengel A. Cross-disciplinary Semantic Building Fingerprints – Knowledge graphs to store topological building information derived from semantic building models (BIM) to apply methods of artificial intelligence (AI) throughout the life cycle of buildings. in International Conference of the Association for Computer-Aided Architectural Design Research in Asia. The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA). 2023. (CAADRIA).