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

    Source: Journal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 001::page 04024047-1
    Author:
    Xiao Lin
    ,
    Peiyang Su
    ,
    Weisheng Lu
    ,
    Hongling Guo
    DOI: 10.1061/JCCEE5.CPENG-6078
    Publisher: American Society of Civil Engineers
    Abstract: Modular 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.
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      ModulePacking: A Top-Down Generative Design Approach for Modular Key Plans

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4304962
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    contributor authorXiao Lin
    contributor authorPeiyang Su
    contributor authorWeisheng Lu
    contributor authorHongling Guo
    date accessioned2025-04-20T10:33:48Z
    date available2025-04-20T10:33:48Z
    date copyright10/9/2024 12:00:00 AM
    date issued2025
    identifier otherJCCEE5.CPENG-6078.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4304962
    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.
    publisherAmerican Society of Civil Engineers
    titleModulePacking: A Top-Down Generative Design Approach for Modular Key Plans
    typeJournal Article
    journal volume39
    journal issue1
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/JCCEE5.CPENG-6078
    journal fristpage04024047-1
    journal lastpage04024047-13
    page13
    treeJournal of Computing in Civil Engineering:;2025:;Volume ( 039 ):;issue: 001
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian