Subtractive Building Massing for Performance-Based Architectural Design Exploration: A Case Study of Daylighting Optimization

Likai Wang, Patrick Janssen, Kian Wee Chen, Ziyu Tong and Guohua Ji
Additional contact information
Likai Wang: School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China
Patrick Janssen: School of Design and Environment, National University of Singapore, Singapore 117566, Singapore
Kian Wee Chen: Andlinger Center for Energy and the Environment, Princeton University, Princeton, NJ 08544, USA
Ziyu Tong: School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China
Guohua Ji: School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China

Sustainability, 2019, vol. 11, issue 24, 1-20

Abstract: For sustainable building design, performance-based optimization incorporating parametric modelling and evolutionary optimization can allow architects to leverage building massing design to improve energy performance. However, two key challenges make such applications of performance-based optimization difficult in practice. First, due to the parametric modelling approaches, the topological variability in the building massing variants is often very limited. This, in turn, limits the scope for the optimization process to discover high-performing solutions. Second, for architects, the process of creating parametric models capable of generating the necessary topological variability is complex and time-consuming, thereby significantly disrupting the design processes. To address these two challenges, this paper presents a parametric massing algorithm based on the subtractive form generation principle. The algorithm can generate diverse building massings with significant topological variability by removing different parts from a predefined volume. Additionally, the algorithm can be applied to different building massing design scenarios without additional parametric modelling being required. Hence, using the algorithm can help architects achieve an explorative performance-based optimization for building massing design while streamlining the overall design process. Two case studies of daylighting performance optimizations are presented, which demonstrate that the algorithm can enhance the exploration of the potential in building massing design for energy performance improvements.

Keywords: parametric massing algorithm; building massing design; performance-based optimization; design exploration; subtractive form generation principle; passive energy savings; daylighting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/11/24/6965/pdf (application/pdf)
https://www.mdpi.com/2071-1050/11/24/6965/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:24:p:6965-:d:294932

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().