Generative methods for Urban design and rapid solution space exploration

Bibliographic Data

ID21247419
AuthorsYue Sun (0000-0003-3151-6774, Cornell University, corresponding author), Timur Dogan (0000-0003-0749-8465, Cornell University)
Year2023
Volume50
Issue6
Pages1577-1590
Publication date2023-07-01
Open AccessYes
TypeARTICLE
VenueEnvironment and Planning B Urban Analytics and City Science (JOURNAL)
Journal identifiersISSN: 2399-8083 • E-ISSN: 2399-8091
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/23998083221142191
OpenAlexW4309971525
LanguageEN
Citations received4
References cited23

Rapid population growth and climate change drive urban renewal and urbanization at massive scales. New computational methods are needed to better support urban designers in developing sustainable, resilient, and livable urban environments. Urban design space exploration and multi-objective optimization of masterplans can be used to expedite planning while achieving better design outcomes by incorporating generative parametric modeling considering different stakeholder requirements and simulation-based performance feedback. However, a lack of generalizable and integrative methods for urban form generation that can be coupled with simulation and various design performance analysis constrains the extensibility of workflows. This research introduces an implementation of a tensor-field–based generative urban modeling toolkit that facilitates rapid design space exploration and multi-objective optimization by integrating with Rhino/Grasshopper ecosystem and its urban analysis and environmental performance simulation tools. Our tensor-field modeling method provides users with a generalized way to encode contextual constraints such as waterfront edges, terrain, view-axis, existing streets, landmarks, and non-geometric design inputs such as network directionality, desired densities of streets, amenities, buildings, and people as forces that modelers can weigh. This allows users to generate many, diverse urban fabric configurations that resemble real-world cities with very few model inputs. We present a case study to demonstrate the proposed framework's flexibility and applicability and show how modelers can identify design and environmental performance synergies that would be hard to find otherwise

Civil engineering · Data science · Generative Design · Population · Systems engineering · Urban design · Urban planning · Architectural and Urban Studies · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Urban Design and Spatial Analysis

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