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

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 postdesign 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 decisionmaking. 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.

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

“…Furthermore, these systems can develop multiple paths to answer any given question and provide highly detailed feedback [140]. Advances in AI technology can increase the effectiveness of these, and similar systems by automatically collecting information from the web, ensuring the most up-to-date content, and using machine learning to increase the adaptability to individualized learner requirements [81,146]. There is potential to create systems that are far more effective than one-on-one tutoring [143], with improved communication between student and teacher, and superior assessment methods [85].…”

“…AI systems could be used to predict future household energy requirements which can help identify inefficiencies, faults, and control future energy use [81]. In addition, AI can contribute to reduced energy consumption in the construction process [102], and improved environmental outcomes in the design [146] and planning process [26].…”

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

“…Therefore, the general computing intelligence aided design framework is vital which could help us figure out whether our NPPHCPS framework work or not in simulated NPP environment and could give us some hints on how to make further improvements in NPPHCPS. We would not discuss too much about it, one could refer to [205]- [207] for further information on the general design framework.…”

Enabling Smarter Cities with Operations Management

“…Examples of analytics-enabled planning include the analysis of street network infrastructure using OpenStreetMap data [14], and the use of street view images and computer vision algorithms to measure visual quality of streets, which is otherwise difficult to measure objectively [89]. A related direction is to develop data-driven algorithmic tools for urban design, e.g., Seoul's use of genetic algorithms to optimize the layout of pedestrian-only superblocks [68].…”