| Id | 2940 | |
| Author | Van Son N.A.; Prado M. | |
| Title | Computational Schematic Design Utilizing Self-Organizing Programmatic Agents: A novel approach to visualizing and organizing urban and architectural data | |
| Reference | Van Son N.A.; Prado M. Computational Schematic Design Utilizing Self-Organizing Programmatic Agents: A novel approach to visualizing and organizing urban and architectural data,Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe 2 |
|
| Keywords | ||
| Link to article | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139257981&partnerID=40&md5=48379284fab0c6de0792e4e447203b57 |
|
| Abstract | Architectural design requires the negotiation of a wide variety of often conflicting constraints and conditions. This puts a tremendous burden on designers to understand and evaluate all the design and site parameters in the conceptual phase of the project. Design methodologies that utilize conventional means of representation such as site diagrams, maps, or other orthographic projections may not be adequate to produce truly integrative design solutions. They often simplify conditions for user clarity or eliminate volumetric and temporal data entirely. As computational design tools develop and the mapping of georeferenced urban data becomes more commonplace, it becomes possible to integrate spatial information into design strategies and evaluate various relationships more effectively. Taking clues from medical imaging, voxel data is used to representvolumetric gradients in material properties and densities of spatial conditions. This method can be used to generate morphogenic spatial analysis of an existing site. The research presented here explores how self-organizing programmatic agents can use this analysis and embedded behaviors to visualize performative schematic design scenarios. These agents, which represent a variety of functional spaces, programmatic requirements, design constraints, and value sets, can negotiate the myriad of environmental and socioeconomic site conditions as well as interact with other adaptive programmatic spaces. Each agent can iteratively search for the space that best suits the desired conditions of its program. Various agents compete for space so the overall performance of the spatial arrangement is maximized. This self-organizing spatial system presents a novel and viable means for designers to more effectively implement both urban data and computational design methods into architectural design scenarios. © 2022, Education and research in Computer Aided Architectural Design in Europe. All rights reserved. |
|
| Metodology | Technique |
Find research methods used
Show Candidate Transition Variables for article (AI method)
Find category for article (AI method)
Find social impact for article (AI method)
Show WordCloud from article (AI method)
Find semantically similar articles (Semantic search)
