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Article: Profit-sensitive generative design for high-rise building morphologies: innovations in 3D form generation and cost-revenue assessment

TitleProfit-sensitive generative design for high-rise building morphologies: innovations in 3D form generation and cost-revenue assessment
Authors
KeywordsBuilding morphology
construction cost and revenue
generative design
genetic algorithm
high-rise buildings
Issue Date1-Jan-2025
PublisherTaylor & Francis
Citation
Building Research & Information, 2025, v. 53, n. 4, p. 435-457 How to Cite?
Abstract

Generative design has been applied to facilitate architectural exploration and augment designers’ ability to consider building profits. However, the take-up of generative design instruments is slow due to the lack of considering practical needs. This paper reports a novel generative design methodology that meets the practical needs of profit-aware morphology for high-rise buildings. It follows a generation–evaluation–optimization workflow but is enriched with a novel shape generator; an evaluator estimating construction cost and selling revenue; and an optimizer using genetic algorithms. The methodology is prototyped in Grasshopper with Python programs embedded and then tested in two real cases in Hong Kong. We find that the methodology is effective in generating complex yet plausible morphologies for high-rises, evaluating their costs and revenues, and deriving profit-optimal buildings. This research contributes to the growing literature on generative design and could lead to a practical design tool that bridges designers and surveying professions.


Persistent Identifierhttp://hdl.handle.net/10722/359251
ISSN
2023 Impact Factor: 3.7
2023 SCImago Journal Rankings: 0.766

 

DC FieldValueLanguage
dc.contributor.authorZhang, Yi-
dc.contributor.authorPeng, Ziyu-
dc.contributor.authorLu, Weisheng-
dc.contributor.authorWebster, Chris-
dc.date.accessioned2025-08-26T00:30:25Z-
dc.date.available2025-08-26T00:30:25Z-
dc.date.issued2025-01-01-
dc.identifier.citationBuilding Research & Information, 2025, v. 53, n. 4, p. 435-457-
dc.identifier.issn0961-3218-
dc.identifier.urihttp://hdl.handle.net/10722/359251-
dc.description.abstract<p>Generative design has been applied to facilitate architectural exploration and augment designers’ ability to consider building profits. However, the take-up of generative design instruments is slow due to the lack of considering practical needs. This paper reports a novel generative design methodology that meets the practical needs of profit-aware morphology for high-rise buildings. It follows a generation–evaluation–optimization workflow but is enriched with a novel shape generator; an evaluator estimating construction cost and selling revenue; and an optimizer using genetic algorithms. The methodology is prototyped in Grasshopper with Python programs embedded and then tested in two real cases in Hong Kong. We find that the methodology is effective in generating complex yet plausible morphologies for high-rises, evaluating their costs and revenues, and deriving profit-optimal buildings. This research contributes to the growing literature on generative design and could lead to a practical design tool that bridges designers and surveying professions.</p>-
dc.languageeng-
dc.publisherTaylor & Francis-
dc.relation.ispartofBuilding Research & Information-
dc.subjectBuilding morphology-
dc.subjectconstruction cost and revenue-
dc.subjectgenerative design-
dc.subjectgenetic algorithm-
dc.subjecthigh-rise buildings-
dc.titleProfit-sensitive generative design for high-rise building morphologies: innovations in 3D form generation and cost-revenue assessment-
dc.typeArticle-
dc.identifier.doi10.1080/09613218.2024.2428804-
dc.identifier.scopuseid_2-s2.0-105002683293-
dc.identifier.volume53-
dc.identifier.issue4-
dc.identifier.spage435-
dc.identifier.epage457-
dc.identifier.eissn1466-4321-
dc.identifier.issnl0961-3218-

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