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Haidar, A, Underwood, J and Coates, P (2019) Smart processes for smart buildings: ‘sustainable processes’, ‘recyclable processes’ and ‘building seeds’ in parametric design. Architectural Engineering and Design Management, 15(05), 402–29.

Jalilzadehazhari, E, Johansson, J and Mahapatra, K (2019) Applying a decision-making framework for resolving conflicts when selecting windows and blinds. Architectural Engineering and Design Management, 15(05), 382–401.

Jalilzadehazhari, E, Johansson, P, Johansson, J and Mahapatra, K (2019) Developing a decision-making framework for resolving conflicts when selecting windows and blinds. Architectural Engineering and Design Management, 15(05), 357–81.

Moloney, J, Globa, A, Wang, R, Khoo, C K and Tokede, O (2019) Hybrid environmental-media facades: rationale and feasibility. Architectural Engineering and Design Management, 15(05), 313–33.

Petrova, E, Pauwels, P, Svidt, K and Jensen, R L (2019) Towards data-driven sustainable design: decision support based on knowledge discovery in disparate building data. Architectural Engineering and Design Management, 15(05), 334–56.

  • Type: Journal Article
  • Keywords: BIM; sustainability; building design; semantics; data mining; pattern recognition; knowledge discovery; information retrieval;
  • ISBN/ISSN: 1745-2007
  • URL: https://doi.org/10.1080/17452007.2018.1530092
  • Abstract:
    Sustainable building design requires an interplay between multidisciplinary input and fulfilment of diverse criteria to align into one high-performing whole. BIM has already brought a profound change in that direction, by allowing execution of efficient collaborative workflows. However, design decision-making still relies heavily on rules of thumb and previous experiences, and not on sound evidence. To improve the design process and effectively build towards a sustainable future, we need to rely on the multiplicity of data available from our existing building stock. The objective of this research is, therefore, to transform existing data, discover new knowledge and inform future design decision-making in an evidence-based manner. This article looks specifically into this task by (1) outlining and distinguishing between the diverse building data sources and types, (2) indicating how the data can be analysed, (3) demonstrating how the discovered knowledge can be implemented in a semantic integration layer and (4) how it can be brought back to design professionals through the design aids they use. We, therefore, propose a performance-oriented design decision support system, relying on BIM, data mining and semantic data modelling, thereby allowing customized information retrieval according to a defined goal.