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

Author

Listed:
  • Steven Jige Quan

    (Seoul National University, Republic of Korea)

  • James Park
  • Athanassios Economou
  • Sugie Lee

    (Hanyang University, Republic of Korea)

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.

Suggested Citation

  • Steven Jige Quan & James Park & Athanassios Economou & Sugie Lee, 2019. "Artificial intelligence-aided design: Smart Design for sustainable city development," Environment and Planning B, , vol. 46(8), pages 1581-1599, October.
  • Handle: RePEc:sae:envirb:v:46:y:2019:i:8:p:1581-1599
    DOI: 10.1177/2399808319867946
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    Citations

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    Cited by:

    1. Tan Yigitcanlar & Federico Cugurullo, 2020. "The Sustainability of Artificial Intelligence: An Urbanistic Viewpoint from the Lens of Smart and Sustainable Cities," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    2. Sesil Koutra & Christos S. Ioakimidis, 2022. "Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges," Land, MDPI, vol. 12(1), pages 1-19, December.
    3. Tan Yigitcanlar & Kevin C. Desouza & Luke Butler & Farnoosh Roozkhosh, 2020. "Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature," Energies, MDPI, vol. 13(6), pages 1-38, March.
    4. Dorota Kamrowska-Załuska, 2021. "Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities," Land, MDPI, vol. 10(11), pages 1-19, November.
    5. Tan Yigitcanlar & Nayomi Kankanamge & Massimo Regona & Andres Ruiz Maldonado & Bridget Rowan & Alex Ryu & Kevin C. Desouza & Juan M. Corchado & Rashid Mehmood & Rita Yi Man Li, 2020. "Artificial Intelligence Technologies and Related Urban Planning and Development Concepts: How Are They Perceived and Utilized in Australia?," JOItmC, MDPI, vol. 6(4), pages 1-21, December.
    6. Steven Jige Quan, 2022. "Urban-GAN: An artificial intelligence-aided computation system for plural urban design," Environment and Planning B, , vol. 49(9), pages 2500-2515, November.
    7. Hanna Obracht-Prondzyńska & Ewa Duda & Helena Anacka & Jolanta Kowal, 2022. "Greencoin as an AI-Based Solution Shaping Climate Awareness," IJERPH, MDPI, vol. 19(18), pages 1-25, September.
    8. Vijay Kumar Bansal, 2023. "A Road-Based 3D Navigation System in GIS: A Case Study of an Institute Campus," International Journal of Applied Geospatial Research (IJAGR), IGI Global, vol. 14(1), pages 1-20, January.

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