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Volume 52 Issue 7
Oct.  2022
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Article Contents
ZHOU Ziqian, GAO Wen, HE Qiushi, LIN Borong, HAN Yuqiao. Artificial Intelligence Exploration in Architectural Design-From Generative Design to Intelligent Decision-Making[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(7): 159-172,47. doi: 10.13204/j.gyjzG21090801
Citation: ZHOU Ziqian, GAO Wen, HE Qiushi, LIN Borong, HAN Yuqiao. Artificial Intelligence Exploration in Architectural Design-From Generative Design to Intelligent Decision-Making[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(7): 159-172,47. doi: 10.13204/j.gyjzG21090801

Artificial Intelligence Exploration in Architectural Design-From Generative Design to Intelligent Decision-Making

doi: 10.13204/j.gyjzG21090801
  • Received Date: 2021-09-08
    Available Online: 2022-10-28
  • Architectural design is a complex process, which not only needs knowledge and experience of architects but also their imagination and creativity. Artificial intelligence (AI) technology can digitize the architectural theory and design experience, improve the efficiency of the design process, and also provide more ideas for design. To solve different problems in architectural design, a variety of AI technologies have been applied and many methods have been proposed. Relevant achievements in the past five years were summarized and sorted out from the perspective of design problems, AI technologies and solutions. And the research status and development trends were analyzed. The summarization revealed distribution features of relevant researches in recent years. In the design task, the most popular issues were the layout and shape, which reached 42% and 26% of the reports respectively, and in AI technology, the most extensive application was optimization algorithms and neural networks, which accounted for 41% and 22% of the reports respectively. However, the application of AI in the field of architectural design has not formed a unified theoretical system yet, facing a crucial challenge of generality and standardization.
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  • [70]
    BOMFIM K, TAVARES F. Building facade optimization for maximizing the incident solar radiation[C]//Proceedings of the 37th eCAADe and 23rd SIGraDi Conference. 2019:171-180.
    [71]
    STEINØ N. Mapping the architectural genome:a preliminary study of facade syntax[C]//Proceedings of the 35th eCAADe Conference. 2017:453-462.
    [72]
    DE LUCA F, WORTMANN T. Multi-objective optimization for daylight retrofit[C]//Proceedings of the 38th eCAADe Conference. 2020:57-66.
    [73]
    李煜, 李玲玲, 刘滢. 光舒适导向的体育运动训练馆顶界面采光口参数化设计研究[C]//2020计算性设计国际学术论坛. 2020:413-429.
    [74]
    丁炜豪, 俞天琦, 吴玉冬, 等. 地下交通枢纽天窗采光模拟与形式优化研究[C]//2020计算性设计国际学术论坛. 2020:382-392.
    [75]
    GERBER D, PANTAZIS E. Design exploring complexity in architectural shells:interactive form finding of reciprocal frames through a multi-agent system[C]//Proceedings of the 34th eCAADe Conference. 2016:455-464.
    [76]
    孙澄,韩昀松.基于计算性思维的建筑绿色性能智能优化设计探索[J].建筑学报,2020(10):88-94.
    [77]
    SEBESTYEN A, TYC J. Machine learning methods in energy simulations for architects and designers:the implementation of supervised machine learning in the context of the computational design process[C]//Proceedings of the 38th eCAADe Conference. 2020:613-622.
    [78]
    SINGH M M, SCHNEIDER-MARIN P, HARTER H, et al. Applying Deep learning and databases for energy-efficient architectural design[C]//Proceedings of the 38th eCAADe Conference.2020:79-87.
    [79]
    TAKIZAWA A. Estimating potential event occurrence areas in small space based on semi-supervised learning[C]//Proceedings of the 34th eCAADe Conference.2016:169-178.
    [80]
    TARABISHY S, PSARRAS S, KOSICKI M, et al. Deep learning surrogate models for spatial and visual connectivity[J]. International Journal of Architectural Computing, 2020, 18(1):53-66.
    [81]
    BROWN L, YIP M, GARDNER N, et al. Drawing recognition-integrating machine learning systems into architectural design workflows[C]//Proceedings of the 38th eCAADe Conference. 2020:289-298.
    [82]
    HUANG W, HAO Z. Architectural drawings recognition and generation through machine learning[C]//Proceedings of the 38th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA). 2018.
    [83]
    UZUN C, ÇOLAKOGLU M B. Architectural drawing recognition:a case study for training the learning algorithm with architectural plan and section drawing images[C]//Proceedings of the 37th eCAADe and 23rd SIGraDi Conference.2019:29-34.
    [84]
    张荷花,顾明.BIM模型智能检查工具在审查平台及消防审查中的应用[J].土木建筑工程信息技术,2021,13(1):1-7.
    [85]
    张荷花,顾明.BIM模型智能检查工具研究与应用[J].土木建筑工程信息技术,2018,10(2):1-6.
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