Crossdomain analytical techniques have made the prediction of outcomes in building design more accurate. Yet, many decisions are based on rules of thumb and previous experiences, and not on documented evidence. That results in inaccurate predictions and a difference between predicted and actual building performance. This article aims to reduce the occurrence of such errors using a combination of data mining and semantic modelling techniques, by deploying these technologies in a use case, for which sensor data is collected. The results present a semantic building data graph enriched with discovered motifs and association rules in observed properties. We conclude that the combination of semantic modelling and data mining techniques can contribute to creating a repository of building data for design decision support.
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 customised information retrieval according to a defined goal.
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