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

Chair of Architectural Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	German
Title of host publication	International Conference of the Association for Computer-Aided Architectural Design Research in Asia
Publisher	The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA)
State	Published - 2023

Publication series

Name	CAADRIA
Publisher	Association for Computer-Aided Architectural Design Research in Asia

Cite this

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).

@inproceedings{aed5834bab3b4ce9b2bb3f31a8ce52e5,

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",

series = "CAADRIA",

publisher = "The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA)",

booktitle = "International Conference of the Association for Computer-Aided Architectural Design Research in Asia",

}

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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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

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AU - Bielski, Jessica

AU - Ziegler, Christoph

AU - Eisenstadt, Viktor

AU - Althoff, Klaus-Dieter

AU - Dengel, Andreas

PY - 2023

Y1 - 2023

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.

KW - LOCenter

KW - Conceptual design

KW - building information modelling

KW - knowledge graph

KW - artificial intelligence

M3 - Konferenzbeitrag

T3 - CAADRIA

BT - International Conference of the Association for Computer-Aided Architectural Design Research in Asia

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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).