Artificial intelligence-aided design: Smart Design for sustainable city development

Steven Jige Quan, James Park, Athanassios Economou and Sugie Lee
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Steven Jige Quan: Seoul National University, Republic of Korea
Sugie Lee: Hanyang University, Republic of Korea

Environment and Planning B, 2019, vol. 46, issue 8, 1581-1599

Abstract: Current planning and design decision support systems show limitations in the integration of design, science, and computation. Planning support systems with manual design and post-design evaluations impose major challenges in exploring huge design spaces. Generative design systems largely neglect the wicked nature of design problems and lack appropriate representation methods and simulation tools at the urban scale. To tackle those challenges, this research developed a Smart Design framework featuring urban design decision-making reinforced by artificial intelligence-aided design (AIAD). The Smart Design framework treats urban design as an emergent pattern formation processes with contextualized and dynamic objectives. The framework integrates design thinking, advanced artificial intelligence search techniques (e.g. genetic algorithms), urban scale performance simulations, and participation to better inform decision-making. Through four major stages, the framework combines the ideas of Science for Design and Design in Science. The significance and potential of the Smart Design framework are demonstrated in an urban design study of Gangnam superblocks in Seoul, South Korea. The study explores sustainable urban forms in the high-density, super-complex, and hyper-consumptive environment of Gangnam, which can also be found in many other Asian contexts. The case study illustrates how the framework identifies design solutions for sustainable city development in the process of participatory decision-making through the co-evolution of design problems and solutions.

Keywords: Smart Design; artificial intelligence-aided design (AIAD); genetic algorithms; energy efficient urban form; planning support systems; generative design (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:46:y:2019:i:8:p:1581-1599

DOI: 10.1177/2399808319867946

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