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Assessing environmental performance in early building design stage: An integrated parametric design and machine learning method
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction. Department of Construction Management, Harbin Institute of Technology, Harbin, China.ORCID iD: 0000-0002-9310-9093
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.ORCID iD: 0000-0002-4695-5369
Department of Construction Management, Harbin Institute of Technology, Harbin, China. Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China. Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China.
2019 (English)In: Sustainable cities and society, ISSN 2210-6707, Vol. 50, article id 101596Article in journal (Refereed) Published
Abstract [en]

Decisions made at early design stage have major impacts on buildings’ life-cycle environmental performance. However, when only a few parameters are determined in early design stages, the detailed design decisions may still vary significantly. This may cause same early design to have quite different environmental impacts. Moreover, default settings for unknown detailed design parameters clearly cannot cover all possible variations in impact, and Monte Carlo analysis is sometimes not applicable as parameters’ probability distributions are usually unknown. Thus, uncertainties about detailed design make it difficult for existing environmental assessment methods to support early design decisions.

Thus, this study developed a quantitative method using parametric design technology and machine learning algorithms for assessing buildings’ environmental performance in early decision stages, considering uncertainty associated with detailed design decisions. The parametric design technology creates design scenarios dataset, then associated environmental performances are assessed using environmental assessment databases and building performance simulations. Based on the generated samples, a machine learning algorithm integrating fuzzy C-means clustering and extreme learning machine extracts the case-specific knowledge regarding designed buildings’ early design associated with environmental uncertainty. Proposed method is an alternative but more generally applicable method to previous approaches to assess building's environmental uncertainty in early design stages.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 50, article id 101596
Keywords [en]
Building early design, Parametric design, Machine learning, Environmental impact, Prediction intervals
National Category
Construction Management
Research subject
Construction Management and Building Technology
Identifiers
URN: urn:nbn:se:ltu:diva-75133DOI: 10.1016/j.scs.2019.101596Scopus ID: 2-s2.0-85067201337OAI: oai:DiVA.org:ltu-75133DiVA, id: diva2:1332869
Note

Validerad;2019;Nivå 2;2019-06-28 (johcin)

Available from: 2019-06-28 Created: 2019-06-28 Last updated: 2019-06-28Bibliographically approved

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Feng, KailunLu, Weizhuo

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