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Article: ModulePacking: A Top-Down Generative Design Approach for Modular Key Plans

TitleModulePacking: A Top-Down Generative Design Approach for Modular Key Plans
Authors
KeywordsGenerative design
Genetic algorithm
Modular building
Modularization
Issue Date9-Oct-2024
PublisherAmerican Society of Civil Engineers
Citation
Journal of Computing in Civil Engineering, 2025, v. 39, n. 1 How to Cite?
AbstractModular construction is increasingly recognized for its efficiency in production and assembly. Modularization, the design process that segments floor plans into discrete modular units to create a modular key plan, has been identified as being crucial for creating an economical modular construction plan. However, solving the modularization problem is often daunting, requiring extensive trial-and-error to navigate the cast array of possible configurations. To address this challenge, this study introduces ModulePacking, a top-down generative design approach aimed specifically at the modularization problem. A hierarchical design methodology is proposed, encompassing a twofold process: partition and merging, where the optimized modular key plan is merged from the partition result. A genetic algorithm is utilized to accelerate the optimization process. In the case study, ModulePacking can generate modular key plans that rival those created by human designers as well as satisfactory results for large-scale buildings where human designers might struggle. ModulePacking showcases considerable potential in assisting designers and manufacturers in optimizing the modular construction plan, ultimately contributing to the advancement of modular construction.
Persistent Identifierhttp://hdl.handle.net/10722/355792
ISSN
2023 Impact Factor: 4.7
2023 SCImago Journal Rankings: 1.137
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLin, Xiao-
dc.contributor.authorSu, Peiyang-
dc.contributor.authorLu, Weisheng-
dc.contributor.authorGuo, Hongling-
dc.date.accessioned2025-05-14T00:35:11Z-
dc.date.available2025-05-14T00:35:11Z-
dc.date.issued2024-10-09-
dc.identifier.citationJournal of Computing in Civil Engineering, 2025, v. 39, n. 1-
dc.identifier.issn0887-3801-
dc.identifier.urihttp://hdl.handle.net/10722/355792-
dc.description.abstractModular construction is increasingly recognized for its efficiency in production and assembly. Modularization, the design process that segments floor plans into discrete modular units to create a modular key plan, has been identified as being crucial for creating an economical modular construction plan. However, solving the modularization problem is often daunting, requiring extensive trial-and-error to navigate the cast array of possible configurations. To address this challenge, this study introduces ModulePacking, a top-down generative design approach aimed specifically at the modularization problem. A hierarchical design methodology is proposed, encompassing a twofold process: partition and merging, where the optimized modular key plan is merged from the partition result. A genetic algorithm is utilized to accelerate the optimization process. In the case study, ModulePacking can generate modular key plans that rival those created by human designers as well as satisfactory results for large-scale buildings where human designers might struggle. ModulePacking showcases considerable potential in assisting designers and manufacturers in optimizing the modular construction plan, ultimately contributing to the advancement of modular construction.-
dc.languageeng-
dc.publisherAmerican Society of Civil Engineers-
dc.relation.ispartofJournal of Computing in Civil Engineering-
dc.subjectGenerative design-
dc.subjectGenetic algorithm-
dc.subjectModular building-
dc.subjectModularization-
dc.titleModulePacking: A Top-Down Generative Design Approach for Modular Key Plans-
dc.typeArticle-
dc.identifier.doi10.1061/JCCEE5.CPENG-6078-
dc.identifier.scopuseid_2-s2.0-85206093036-
dc.identifier.volume39-
dc.identifier.issue1-
dc.identifier.eissn1943-5487-
dc.identifier.isiWOS:001365140800006-
dc.identifier.issnl0887-3801-

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