| id |
acadia16_106 |
| authors |
Das, Subhajit; Day, Colin; Hauck, John; Haymaker, John; Davis, Diana |
| year |
2016 |
| title |
Space Plan Generator: Rapid Generationn & Evaluation of Floor Plan Design Options to Inform Decision Making |
| doi |
https://doi.org/10.52842/conf.acadia.2016.106
|
| source |
ACADIA // 2016: POSTHUMAN FRONTIERS: Data, Designers, and Cognitive Machines [Proceedings of the 36th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-77095-5] Ann Arbor 27-29 October, 2016, pp. 106-115 |
| summary |
Design exploration in architectural space planning is often constrained by tight deadlines and a need to apply necessary expertise at the right time. We hypothesize that a system that can computationally generate vast numbers of design options, respect project constraints, and analyze for client goals, can assist the design team and client to make better decisions. This paper explains a research venture built from insights into space planning from senior planners, architects, and experts in the field, coupled with algorithms for evolutionary systems and computational geometry, to develop an automated computational framework that enables rapid generation and analysis of space plan layouts. The system described below automatically generates hundreds of design options from inputs typically provided by an architect, including a site outline and program document with desired spaces, areas, quantities, and adjacencies to be satisfied. We envision that this workflow can clarify project goals early in the design process, save time, enable better resource allocation, and assist key stakeholders to make informed decisions and deliver better designs. Further, the system is tested on a case study healthcare design project with set goals and objectives. |
| keywords |
healthcare spaces, facility layout design, design optimization, decision making, binary data tree structure, generative design, automated space plans |
| series |
ACADIA |
| type |
paper |
| email |
|
| full text |
 file.pdf (923,208 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:55 |
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