Generative methods for Urban design and rapid solution space exploration
نویسندگان
چکیده
Rapid population growth and climate change drive urban renewal urbanization at massive scales. New computational methods are needed to better support designers in developing sustainable, resilient, livable environments. Urban design space exploration multi-objective optimization of masterplans can be used expedite planning while achieving outcomes by incorporating generative parametric modeling considering different stakeholder requirements simulation-based performance feedback. However, a lack generalizable integrative for form generation that coupled with simulation various analysis constrains the extensibility workflows. This research introduces an implementation tensor-field–based toolkit facilitates rapid integrating Rhino/Grasshopper ecosystem its environmental tools. Our tensor-field method provides users generalized way encode contextual constraints such as waterfront edges, terrain, view-axis, existing streets, landmarks, non-geometric inputs network directionality, desired densities amenities, buildings, people forces modelers weigh. allows generate many, diverse fabric configurations resemble real-world cities very few model inputs. We present case study demonstrate proposed framework's flexibility applicability show how identify synergies would hard find otherwise.
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ژورنال
عنوان ژورنال: Environment And Planning B: Urban Analytics And City Science
سال: 2022
ISSN: ['2399-8083', '2399-8091']
DOI: https://doi.org/10.1177/23998083221142191