A new construction method for metro stations in dense urban areas in Shanghai soft ground: Open-cut shafts combined with quasi-rectangular jacking boxes

Yang, Yifeng; Liao, Shaoming; Liu, Mengbo; Wu, Dong-Peng; Pan, Weiqiang; Li, Hao

doi:10.1016/j.tust.2022.104530

Cited by 17 publications

(1 citation statement)

References 30 publications

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“…While Ma et al concluded that uniformly distributed spatial facility service components were crucial in shared rental housing [48], a thorough analysis of the generated sample set by machine learning yielded a programmatic surface effect that prefers a balanced layout for each design element. This confirms that the results align with the flat characteristics of an RSPL [49].…”

Section: Generative Preference Design Element Determinationsupporting

confidence: 89%

The Development of an Experimental Framework to Explore the Generative Design Preference of a Machine Learning-Assisted Residential Site Plan Layout

Sun,

Yan,

et al. 2023

Land

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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.

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Section: Generative Preference Design Element Determinationsupporting

confidence: 89%