Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-16T05:23:49.901Z Has data issue: false hasContentIssue false

Evolutionary structural and spatial adaptation of topologically differentiated tensile systems in architectural design

Published online by Cambridge University Press:  07 October 2015

Sean Ahlquist*
Affiliation:
Taubman College of Architecture and Urban Planning, University of Michigan, Ann Arbor, Michigan, USA Institute for Computational Design, Stuttgart University, Stuttgart, Germany
Dillon Erb
Affiliation:
Taubman College of Architecture and Urban Planning, University of Michigan, Ann Arbor, Michigan, USA
Achim Menges
Affiliation:
Institute for Computational Design, Stuttgart University, Stuttgart, Germany
*
Reprint requests to: Sean Ahlquist, Taubman College of Architecture and Urban Planning, University of Michigan, 2000 Bonisteel Boulevard, Ann Arbor, MI 48109, USA. E-mail: [email protected]

Abstract

This paper presents research in the development of heuristic evolutionary algorithms (EAs) for generating and exploring differentiated force-based structures. The algorithm is weighted toward design exploration of topological differentiation while including specific structural and material constraints. An embryological EA model is employed to “grow” networks of mass-spring elements achieving desired mesh densities that resolve themselves in tensile force (form-active) equilibrium. The primal quadrilateral quadrisection method serves as the foundation for a range of extensible subdivision methods. Unique to this research, the quad is addressed as a “cell” rather than a topological or geometric construct, allowing for the contents of the cell to vary in number of mass-spring elements and orientation. In this research, this approach has been termed the quadrilateral quadrisection with n variable topological transformation method. This research culminates with the introduction of a method for grafting meshes where emergent features from the evolved meshes can be transposed and replicated in an explicit yet informed manner. The EA and grafting methods function within a Java-based software called springFORM, developed in previous research, which utilizes a mass-spring based library for solving force equilibrium and allows for both active (manual) and algorithmic topology manipulation. In application to a specific complex tensile mesh, the design framework, which combines the generative EA and mesh grafting method, is shown to produce emergent and highly differentiated topological arrangements that negotiate the specific relationships among a desired maximal mesh density, geometric patterning, and equalized force distribution.

Type
Special Issue Articles
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Ahlquist, S., Kampowski, T., Oliyan Torghabehi, O., Menges, A., & Speck, T. (2014). Development of a digital framework for the computation of complex material and morphological behavior of biological and technological systems. Computer-Aided Design 60, 84104.CrossRefGoogle Scholar
Ahlquist, S., Lienhard, J., Knippers, J., & Menges, A. (2013). Physical and numerical prototyping for integrated bending and form-active textile hybrid structures. Rethinking Prototyping: Proc. Design Modelling Symp., DMS ’13. Berlin: Springer.Google Scholar
Ahlquist, S., & Menges, A. (2010). Realizing formal and functional complexity for structurally dynamic systems in rapid computational means. Proc. Advances in Architectural Geometry Conf., AGG ’10. Berlin: Springer.Google Scholar
Ahlquist, S., & Menges, A. (2013). Frameworks for computational design of textile micro-architectures and material behavior in forming complex force-active structures. Adaptive Architecture Proc. Association for Computer Aided Design in Architecture, ACADIA ’13. Cambridge: Riverside Press.Google Scholar
Aish, R. (2011). DesignScript: origins, explanation, illustration. Proc. Design Modeling Symp., DMS ’11, Berlin: Springer.CrossRefGoogle Scholar
Aish, R. (2013). DesignScript: a learning environment for design computation. Rethinking Prototyping: Proc. Design Modeling Symp., DMS ’13. Berlin: Springer.Google Scholar
Aish, R., Fisher, A., Joyce, S., & Marsh, A. (2012). Progress towards multi-criteria design optimisation using designscript with smart form, robot structural analysis and ecotect building performance analysis. Synthetic Digital Ecologies, Proc. Association for Computer Aided Design in Architecture, San Francisco, CA, October 18–21.CrossRefGoogle Scholar
Baraff, D., & Witkin, A. (1998). Large steps in cloth simulation. Proc. Computer Graphics and Interactive Techniques, SIGGRAPH ’98. Orlando, FL: ACM.Google Scholar
Bentley, P., & Corne, D. (2002). An introduction to creative evolutionary systems. In Creative Evolutionary Systems (Bentley, P.J., & Corne, D.W., Eds.), pp. 175. San Diego, CA: Academic Press.Google Scholar
Bentley, P., & Kumar, S. (1999). Three ways to grow designs: a comparison of embryogenies for an evolutionary design problem. Proc. Genetic and Evolutionary Computation Conf., GECCO ’99, Orlando, FL, July 14–17.Google Scholar
Crutchfield, J.P. (1994). The calculi of emergence: computation, dynamics, and induction. Physica D: Nonlinear Phenomena 75(1–3), 1154.CrossRefGoogle Scholar
Dianati, M., Song, I., & Treiber, M. (2002). An introduction to genetic algorithms and evolution strategies. Technical report, University of Waterloo.Google Scholar
Gerber, D. (2012). PARA—typing informing form and the making of difference. International Journal of Architectural Computing 10(4), 501520.CrossRefGoogle Scholar
Greenwold, S. (2009). Simong particles. Accessed at http://www.bitbucket.org/simong/simong-particles on October 3, 2011.Google Scholar
Holland, J.H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis With Applications to Biology, Control and Artificial Intelligence. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Jones, G. (2002). Genetic and evolutionary algorithms. In Encyclopedia of Computational Chemistry. London: Wiley.Google Scholar
Kilian, A., & Ochsendorf, J. (2005). Particle-spring systems for structural form finding. Journal of the International Association for Shell and Spatial Structures 46(148), 7784.Google Scholar
Kim, I.Y., & de Weck, O.L. (2005). Variable chromosome length genetic algorithm for progressive refinement in topology optimization. Structural and Multidisciplinary Optimization 29(6), 445456.CrossRefGoogle Scholar
Laumanns, M., Zitzler, E., & Thiele, L. (2000). A unified model for multi-objective evolutionary algorithms with elitism. Proc. Congr. Evolutionary Computation, La Jolla, CA, July 16–19, 2000.CrossRefGoogle Scholar
Manos, S., Large, M.C.J., & Poladian, L. (2007). Evolutionary design of single-mode microstructured polymer optical fibres using an artificial embryogeny representation. Proc. 9th Annual Conf. Genetic and Evolutionary Computation, GECCO ‘07. London: ACM.Google Scholar
Oliyan Torghabehi, O., & von Buelow, P. (2014). Performance oriented generative design of structural double skin facades inspired by cell morphologies. Proc. Shells, Membranes and Spatial Structures, IASS-SLTE ’14, Brasilia, September 15–19.Google Scholar
O'Reilly, U.M., & Hemberg, M. (2007). Integrating generative growth and evolutionary computation for form exploration. Genetic Programming and Evolvable Machines 8(2), 163186.CrossRefGoogle Scholar
Poli, R., Langdon, W., & McPhee, N. (2008). A field guide to genetic programming. Accessed at http://www.gp-field-guide.org.uk on June 15, 2014.Google Scholar
Rosenman, M.A., & Gero, J.S. (1999). Evolving designs by generating useful complex gene structures. In Evolutionary Design by Computers (Bentley, P., Ed.), pp. 345364. San Francisco, CA: Morgan Kaufmann.Google Scholar
Russell, P.J. (1992). Genetics. New York: Harper Collins.Google Scholar
Ryoo, J., & Hajela, P. (2004). Handling variable string lengths in GA-based structural topology optimization. Structural and Multidisciplinary Optimization 26(5), 318325.CrossRefGoogle Scholar
Schein, M., & Tessmann, O. (2008). Structural analysis as driver in surface-based design approaches. International Journal of Architectural Computing 6(1), 1939.CrossRefGoogle Scholar
Schmidt, R. (2012). Interactive modeling with mesh surfaces. Proc. ACM SIGGRAPH 2012. Los Angeles, August 5–9, 2012.Google Scholar
Sedgewick, R., & Wayne, K. (2011). Algorithms. Boston: Pearson Education.Google Scholar
Shiue, L.-J., & Peters, J. (2005). Mesh refinement based on Euler encoding. Proc. Shape Modeling and Applications SMI ’05, Cambridge, MA, June 13–17.CrossRefGoogle Scholar
Terzopoulos, D., Platt, J., Barr, A., & Fleischer, K. (1987). Elastically deformable models. Computer Graphics 21(4), 205214.CrossRefGoogle Scholar
Turner, J.S. (2007). The Tinkerer's Accomplice. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
von Buelow, P. (2007). Genetically Engineered Architecture: Design Exploration With Evolutionary Computation. Saarbrücken, Germany: AV Akademiker Verlag.Google Scholar
von Buelow, P. (2008). Breeding topology: special considerations for generative topology exploration using evolutionary computation. Proc. Association for Computer Aided Design in Architecture Conf., pp. 346–353, Minneapolis, MN, October 16–19.CrossRefGoogle Scholar
Welch, W., & Witkin, A. (1994). Free-form shape design using triangulated surfaces. Proc. Conf. Computer Graphics and Interactive Techniques, SIGGRAPH ’94. New York: ACM.Google Scholar