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id caadria2023_292
authors Langenhan, Christoph, Bielski, Jessica, Ziegler, Christoph, Eisenstadt, Viktor, Althoff, Klaus-Dieter and Dengel, Andreas
year 2023
title Cross-Disciplinary Semantic Building Fingerprints ‚ AI Knowledge Graphs to Store Topological Building Information Derived From Semantic Building Models (BIM) to Apply Methods of Artificial Intelligence (AI)
doi https://doi.org/10.52842/conf.caadria.2023.1.129
source Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 129–138
summary 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 this paper, Semantic Building Fingerprints (SBFs) and Semantic Urban Fingerprints (SUFs), as well as use cases for AI methods are described.
keywords Conceptual design, building information modelling, knowledge graph, artificial intelligence
series CAADRIA
email langenhan@tum.de
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