The Development of an Experimental Framework to Explore the Generative Design Preference of a Machine Learning-Assisted Residential Site Plan Layout
Pei Sun,
Fengying Yan (),
Qiwei He and
Hongjiang Liu
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Pei Sun: School of Architecture, Tianjin University, Tianjin 300072, China
Fengying Yan: School of Architecture, Tianjin University, Tianjin 300072, China
Qiwei He: School of Architecture, Tianjin University, Tianjin 300072, China
Hongjiang Liu: China Architecture Design & Research Group, Beijing 100044, China
Land, 2023, vol. 12, issue 9, 1-22
Abstract:
Generative design based on machine learning has become an important area of application for artificial intelligence. Regarding the generative design process for residential site plan layouts (hereafter referred to as “RSPLs”), the lack of experimental demonstration begs the question: what are the design preferences of machine learning? In this case, all design elements of the target object need to be extracted as much as possible to conduct experimental studies to produce scientific experimental results. Based on this, the Pix2pix model was used as the test case for Chinese residential areas in this study. An experimental framework of “extract-translate-machine-learning-evaluate” is proposed, combining different machine and manual computations, as well as quantitative and qualitative evaluation techniques, to jointly determine which design elements and their characteristic representations are machine learning design preferences in the field of RSPL. The results show that machine learning can assist in optimizing the design of two particular RSPL elements to conform to residential site layout plans: plaza paving and landscaped green space. In addition, two other major elements, public facilities and spatial structures, were also found to exhibit more significant design preferences, with the largest percentage increase in the number of changes required after machine learning. Finally, the experimental framework established in this study compensates for the lack of consideration that all design elements of a residential area simultaneously utilize the same methodological framework. This can also assist planners in developing solutions that better meet the expectations of residents and can clarify the potential and advantageous directions for the application of machine learning-assisted RSPL.
Keywords: machine learning; generative design preference; planning design elements; Pix2pix model; residential site layout planning; experimental framework (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2023:i:9:p:1776-:d:1239219
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