Journal: CAADRIA

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Association for Computer-Aided Architectural Design Research in Asia

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2022 - 20242
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Publications 1 - 2 of 2
  • Automated Semantic SWOT Analysis for City Planning Targets
    Item type: Conference Paper
    Grisiute, Ayda; Shi, Zhongming; Chadzynski, Arkadiusz; et al. (2022)
    CAADRIA ~ POST-CARBON – Proceedings of the 27th CAADRIA Conference
    Singapore’s urban planning and management is cross-domain in nature and need to be assessed using multi-domain indicators — such as SDGs. However, urban planning processes are often confronted with data interoperability issues. In this paper, we demonstrate how a Semantic Web Technology-based approach combined with a SWOT analysis framework can be used to develop an architecture for automated multi-domain evaluations of SDG-related planning targets. This paper describes an automated process of storing heterogeneous data in a semantic data store, deriving planning metrics and integrating a SWOT framework for the multi-domain evaluation of on-site solar energy potential across plots in Singapore. Our goal is to form the basis for a more comprehensive planning support tool that is based on a reciprocal relationship between innovations in SWT and a versatile SWOT framework. The presented approach has many potential applications beyond the presented energy potential evaluation.
  • ArchiSearch: A Text-Image Integrated Case-Based Search Engine in Architecture Using Self-Organizing Maps
    Item type: Conference Paper
    Cai, Chenyi; Wang, Xiao; Li, Biao; et al. (2024)
    CAADRIA ~ Accelerated Design: Proceedings of the 29th International Conference of the Association for Computer Aided Architectural Design Research in Asia (CAADRIA 2024), Volume 3
    Case-based study and reasoning are considered fundamental meta-practices of architectural design. There are many online platforms to share architectural projects, which serve as data sources for case studies. However, search and retrieval capabilities offered by such platforms often do not cater to the professional needs of architects and do not integrate or synthesize the semantics present in text and images. We propose a systematic methodology to develop a search engine for architectural and urban projects using text and image data from one such online project-sharing platform: Chinese platform Gooood. Our approach automatically collects and extracts features from data, and integrates figurative and descriptive retrieving methods using word2vec, deep learning, and clustering algorithms. Our methodology provides a flexible approach for developing case-based search engines for architectural projects that take into consideration both images and texts and could be applied to any (Chinese language) platform. It offers architects augmented case-based workflows, which enrich design inspiration and accelerate decision-making meta-practices by unlocking the semantic search and retrieval of existing projects in novel ways.
Publications 1 - 2 of 2