From patterns to evidence: Enhancing sustainable building design with pattern recognition and information retrieval approaches

Petrova, Ekaterina Aleksandrova; Svidt, Kjeld; Jensen, Rasmus Lund; Pauwels, Pieter

doi:10.1201/9780429506215-49

Cited by 3 publications

(3 citation statements)

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“…One of the key challenges identified in this work is the implementation of a semantic integration layer, which combines data from various sources in a semantic graph, yet still allows to deploy data mining and geometric feature matching techniques. Although it is possible to include explicit results from these approaches in a graph (Petrova et al, 2018b), this might compromise the flexibility and modularity of the DDSS. By deploying the proposed web-based system architecture, we hope to overcome this challenge and make the data analysis and information retrieval userdriven.…”

Section: Resultsmentioning

confidence: 99%

“…This system relies on three main approaches targeting knowledge discovery, namely data mining, geometric feature matching and direct semantic queries. We investigate to what extent the results of geometric similarity matching and data mining can be represented in semantic graphs, thereby relying on earlier work (Petrova et al, 2018a(Petrova et al, , 2018b. The resulting framework would therefore be able to successfully combine these approaches in support of AEC domain specialists working towards improving the built environment.…”

Section: Methodological Approachmentioning

confidence: 99%

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Towards data-driven sustainable design: decision support based on knowledge discovery in disparate building data

Petrova

Pauwels

Svidt

et al. 2018

Architectural Engineering and Design Management

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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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Section: Resultsmentioning

confidence: 99%

Section: Methodological Approachmentioning

confidence: 99%

Towards data-driven sustainable design: decision support based on knowledge discovery in disparate building data

Petrova

Pauwels

Svidt

et al. 2018

Architectural Engineering and Design Management

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“…However, the application of AI techniques in the sustainable design remains to be developed. Recently, effort has been made [98] to combine BIM, data mining (knowledge discovery), and semantic technique to enable BIM-based information retrieval in support of evidence-based sustainable design in the early design phase.…”

Section: Discussionmentioning

confidence: 99%

Special Issue “Digital Twin Technology in the AEC Industry”

Zhang

Zhao

Ren

et al. 2020

Advances in Civil Engineering

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Sustainable building design has become a hot topic over the past decades. Many standards, databases, and tools have been developed for achieving a sustainable building. Not until recently have the importance of structural engineering and its contribution to sustainable building design been full recognised. However, due to the highly fragmented and diversity of knowledge across building and infrastructure domains, there is a lack of approach that can address all the sustainable issues within the structural design. This paper reviews the sustainable design from the perspective of structural engineering: (1) reviewing the current situation; (2) identifying the gaps and difficulties; and (3) making recommendations for future improvements. The strategies and indicators, as well as BIM-enabled methodology, for sustainable structural design (SSD) are also discussed in a holistic way. The results of this investigation show that most of the methods are not doing well in terms of delivering a successful sustainable structural design. It is expected that the future BIM could probably provide such a platform to address these issues.

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Pattern Recognition: An Outline of Literature Review that Taps into Machine Learning to Achieve Sustainable Development Goals

Sharma,

Arumugam

2024

Lecture Notes in Networks and Systems

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From patterns to evidence: Enhancing sustainable building design with pattern recognition and information retrieval approaches

Cited by 3 publications

References 1 publication

Towards data-driven sustainable design: decision support based on knowledge discovery in disparate building data

Towards data-driven sustainable design: decision support based on knowledge discovery in disparate building data

Special Issue “Digital Twin Technology in the AEC Industry”

Pattern Recognition: An Outline of Literature Review that Taps into Machine Learning to Achieve Sustainable Development Goals

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