| id |
acadia12_67 |
| authors |
Gerber, Dr. David Jason ; Lin, Shih-Hsin |
| year |
2012 |
| title |
Synthesizing Design Performance: An Evolutionary Approach to Multidisciplinary Design Search |
| doi |
https://doi.org/10.52842/conf.acadia.2012.067
|
| source |
ACADIA 12: Synthetic Digital Ecologies [Proceedings of the 32nd Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-1-62407-267-3] San Francisco 18-21 October, 2012), pp. 67-75 |
| summary |
Design is a goal oriented decision-making activity. Design is ill defined and requiring of synthetic approaches to weighing and understanding tradeoffs amongst soft and hard objectives, and the imprecise and or computationally explicit criteria and goals. In this regard designers in contemporary practice face a crisis of sorts. How do we achieve performance under large degrees of uncertainty and limited design cycle time? How do we better design for integrating performance? Fundamentally design teams, are not typically given enough time nor the best tools to design explore, to generate design alternatives, and then evolve solution quality to search for best fit through expansive design solution spaces. Given the complex criteria for defining performance in architecture our research approach experiments upon an evolutionary and integrative computational strategy to expand the solution space of a design problem as well as pre-sort and qualify candidate designs. We present technology and methodology that supports rapid development of design problem solution spaces in which three design domains objectives have multi-directional impact on each other. The research describes the use of an evolutionary approach in which a genetic algorithm is used as a means to automate the design alternative population as well as to facilitate multidisciplinary design domain optimization. The paper provides a technical description of the prototype design, one that integrates associative parametric modeling with an energy use intensity evaluation and with a financial pro forma. The initial results of the research are presented and analyzed including impacts on design process; the impacts on design uncertainty and design cycle latency; and the affordances for ‘designing-in’ performance and managing project complexity. A summary discussion is developed which describes a future cloud implementation and the future extensions into other domains, scales, tectonic and system detail. |
| keywords |
Parametric Design , Domain Integration , Design Methods , Multidisciplinary Design Optimization (MDO) , Evolutionary Algorithms , Design Decision Support , Generative Design |
| series |
ACADIA |
| type |
normal paper |
| email |
|
| full text |
 file.pdf (269,770 bytes) |
| references |
Content-type: text/plain
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Aish, R., and A. Marsh (2011)
 An Integrated Approach to Algorithmic Design and Environmental Analysis
, 2011 Proceedings of the Symposium on Simulation for Architecture and Urban Design, Boston, MA
|
|
|
|
|
Aish, R., and R. Woodbury (2005)
Multi-Level Interaction in Parametric Design
, Smart Graphics, eds. A. Butz, B. Fisher, A. Krüger, and P. Olivier, 924–924. Berlin and Heidelberg: Springer
|
|
|
|
|
Alfaris, A., and R. Merello (2008)
The Generative Multi-Performance Design System
, Proceedings of the 28th Annual Conference of the Association for Computer Aided Design in Architecture, Minneapolis, MN
|
|
|
|
|
Caldas, L. G (2008)
Generation of Energy-Efficient Architecture Solutions Applying GENE_ARCH: An Evolution-Based Generative Design System
, Advanced Engineering Informatics 22 (1): 59–70
|
|
|
|
|
Caldas, L. G., and L. K. Norford (2002)
A Design Optimization Tool Based on a Genetic Algorithm
, Automation in Construction 11 (2): 173–84
|
|
|
|
|
Coello Coello, C. A., G. B. Lamont, and D. A. Van Veldhuisen (2007)
Evolutionary Algorithms for Solving Multi-Objective Problems, eds. D. E. Goldberg and J. R. Koza
, 2nd ed. Genetic and Evolutionary Computation Series. New York: Springer
|
|
|
|
|
Crawley, D. B., J. W. Hand, M. Kummert, and B. T. Griffith (2008)
Contrasting the Capabilities of Building Energy Performance Simulation Programs
, Building and Environment 43 (4): 661–73
|
|
|
|
|
Fabrizio, E., M. Filippi, and J. Virgone (2009)
Trade-Off Between Environmental and Economic Objectives in the Optimization of Multi-Energy Systems
, Building Simulation 2 (1): 29–40
|
|
|
|
|
Flager, F., B. Welle, P. Bansal, G. Soremekun, and J. Haymaker (2009)
Multidisciplinary Process Integration and Design Optimization of a Classroom Building
, Information Technology in Construction 14 (38): 595–612
|
|
|
|
|
Frazer, J (1995)
An Evolutionary Architecture
, London: Architectural Association
|
|
|
|
|
Gerber, D. J., and F. Flager (2011)
Teaching Design Optioneering: A Method for Multidisciplinary Design Optimization
, Proceedings of the 2011 ASCE International Workshop on Computing in Civil Engineering 416 (41182): 109
|
|
|
|
|
Gerber, D. J., and S.-H. Lin (2012)
Designing-in Performance in Early Stage Design Through Parameterization, Automation, and Evolutionary Algorithms
, CAADRIA 2012, Chennai, India
|
|
|
|
|
Haupt, R. L., and S. E. Haupt (2004)
Practical Genetic Algorithms
, 2nd ed. Hoboken, NJ: John Wiley
|
|
|
|
|
Holland, J. H (1975)
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence
, Ann Arbor: University of Michigan Press
|
|
|
|
|
Holzer, D., R. Hough, and M. Burry (2007)
Parametric Design and Structural Optimisation for Early Design Exploration
, International Journal of Architectural Computing 5 (4): 625–43
|
|
|
|
|
Holzer, D., Y. Tengono, and S. Downing (2007)
Developing a Framework for Linking Design Intelligence from Multiple Professions in the AEC Industry
, Computer-Aided Architectural Design Futures (CAADFutures) 2007, eds. A. Dong, A. V. Moere, and J. S. Gero, 303–16. Springer Netherlands
|
|
|
|
|
Kalay, Y. E (1999)
Performance-Based design
, Automation in Construction 8 (4): 395–409
|
|
|
|
|
Keough, I., and D. Benjamin (2010)
Multi-Objective Optimization in Architectural Design
, 2011 Proceedings of the Symposium on Simulation for Architecture and Urban Design, Orlando, FL
|
|
|
|
|
Malkawi, A (2005)
Performance Simulation: Research and Tools
, Performative Architecture: Beyond Instrumentality, eds. B. Kolarevic and A. Malkawi, 85–96. New York: Spon Press
|
|
|
|
|
Miller, B. L., and D. E. Goldberg (1995)
Genetic Algorithms, Tournament Selection, and the Effects of Noise
, Complex Systems 9: 193–212
|
|
|
|
| last changed |
2022/06/07 07:51 |
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