Hostname: page-component-76d6cb85b7-rxvq6 Total loading time: 0 Render date: 2026-07-16T23:50:59.737Z Has data issue: false hasContentIssue false
  • English
  • Français

Coevolutionary and genetic algorithm based building spatial and structural design

Published online by Cambridge University Press:  07 October 2015

Hèrm Hofmeyer  and
Juan Manuel Davila Delgado
Hèrm Hofmeyer*
Affiliation:
Department of the Built Environment, Unit Structural Design, Eindhoven University of Technology, Eindhoven, The Netherlands
Juan Manuel Davila Delgado
Affiliation:
Department of Engineering, Centre for Smart Infrastructure and Construction, University of Cambridge, Cambridge, United Kingdom
*
Reprint requests to: H. Hofmeyer, Department of the Built Environment, Unit Structural Design, Eindhoven University of Technology, PO Box 513, VRT 9.32, Eindhoven 5600 MB, The Netherlands. E-mail: h.hofmeyer@tue.nl
Get access Rights & Permissions [Opens in a new window]

Abstract

In this article, two methods to develop and optimize accompanying building spatial and structural designs are compared. The first, a coevolutionary method, applies deterministic procedures, inspired by realistic design processes, to cyclically add a suitable structural design to the input of a spatial design, evaluate and improve the structural design via the finite element method and topology optimization, adjust the spatial design according to the improved structural design, and modify the spatial design such that the initial spatial requirements are fulfilled. The second method uses a genetic algorithm that works on a population of accompanying building spatial and structural designs, using the finite element method for evaluation. If specific performance indicators and spatial requirements are used (i.e., total strain energy, spatial volume, and number of spaces), both methods provide optimized building designs; however, the coevolutionary method yields even better designs in a faster and more direct manner, whereas the genetic algorithm based method provides more design variants. Both methods show that collaborative design, for example, via design modification in one domain (here spatial) to optimize the design in another domain (here structural) can be as effective as monodisciplinary optimization; however, it may need adjustments to avoid the designs becoming progressively unrealistic. Designers are informed of the merits and disadvantages of design process simulation and design instance exploration, whereas scientists learn from a first fully operational and automated method for design process simulation, which is verified with a genetic algorithm and subject to future improvements and extensions in the community.

Keywords

Coevolutionary DesignGenetic AlgorithmSpatial DesignStructural DesignTopology Optimization

Information

Type
Special Issue Articles
Information
AI EDAM , Volume 29 , Special Issue 4: Generative and evolutionary design exploration , November 2015 , pp. 351 - 370
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.)

Article purchase

Temporarily unavailable

References

REFERENCES

Abrishami, S., Goulding, J. S., Rahimian, F. P., & Ganah, A. (2014). Integration of BIM and generative design to exploit AEC conceptual design innovation. Journal of Information Technology in Construction 19, 350–359.Google Scholar
Batoz, J.-L., & Tahar, M.B. (1982). Evaluation of a new quadrilateral thin plate bending element. International Journal for Numerical Methods in Engineering 18(11), 1655–1677.CrossRefGoogle Scholar
Björk, B.-C. (1992). A conceptual model of spaces, space boundaries, and enclosing structures. Automation in Construction 1(3), 193–214.CrossRefGoogle Scholar
Bletzinger, K.-U., Wüchner, R., Daoud, F., & Camprubi, N. (2005). Computational methods for form finding and optimization of shells and membranes. Computer Methods in Applied Mechanics and Engineering 194(30–33), 3438–3452.CrossRefGoogle Scholar
Booch, G., Rumbaugh, J., & Jacobson, I. (2005). The Unified Modeling Language User Guide, 2nd ed.Reading, MA: Addison–Wesley.Google Scholar
Borrmann, A., & Rank, E. (2009 a). Specification and implementation of directional operators in a 3-D spatial query language for building information models. Advanced Engineering Informatics 23(1), 32–44.CrossRefGoogle Scholar
Borrmann, A., & Rank, E. (2009 b). Topological analysis of 3D building models using a spatial query language. Advanced Engineering Informatics 23(4), 370–385.CrossRefGoogle Scholar
Cook, R.D., Malkus, D.S., Plesha, M.E., & Witt, R.J. (2002). Concepts and Applications of Finite Element Analysis, 4th ed.New York: Wiley.Google Scholar
Davila Delgado, J.M. (2014). Building structural design generation and optimization including spatial modification. PhD Thesis. Eindhoven University of Technology, Department of the Built Environment. Accessed at http://alexandria.tue.nl/extra2/784742.pdfGoogle Scholar
Eastman, C., Teicholz, P., Sacks, R., & Liston, K. (2011). BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors, 2nd ed. Hoboken, NJ: Wiley.Google Scholar
Fenves, S.J., Rivard, H., & Gomez, N. (2000). SEED-Config: a tool for conceptual structural design in a collaborative building design environment. Artificial Intelligence in Engineering 14(3), 233–247.CrossRefGoogle Scholar
Fuyama, H., Law, K.H., & Krawinkler, H. (1997). An interactive computer assisted system for conceptual structural design of steel buildings. Computers & Structures 63(4), 647–662.CrossRefGoogle Scholar
Geyer, P. (2008). Multidisciplinary grammars supporting design optimization of buildings. Research in Engineering Design 18(4), 197–216.CrossRefGoogle Scholar
Glover, F. (1989). Tabu Search—Part 1. ORSA Journal on Computing 1(2), 190–206.CrossRefGoogle Scholar
Guennebaud, G., & Benoit, J. (2014). Eigen C++ Template Library for Linear Algebra. Accessed at http://eigen.tuxfamily.orgGoogle Scholar
Haymaker, J., Fischer, M., Kunz, J., & Suter, B. (2004). Engineering test cases to motivate the formalization of an AEC project model as a directed acyclic graph of views and dependencies. ITcon 9, 419–441.Google Scholar
Hemmerling, M., & Nether, U. (2014). Generico: a case study on performance-based design. Blucher Design Proceedings 1(8), 126–129.Google Scholar
Hesselgren, L., Charitou, R., & Dritsas, S. (2007). The Bishopsgate Tower case study. International Journal of Architectural Computing 5(1), 61–81.CrossRefGoogle Scholar
Hofmeyer, H., & Bakker, M.C.M. (2008). Spatial to kinematically determined structural transformations. Advanced Engineering Informatics 22(3), 393–409.CrossRefGoogle Scholar
Hofmeyer, H., Van Roosmalen, M., & Gelbal, F. (2011). Pre-processing parallel and orthogonal geometrical design components to be used within the finite element method. Advanced Engineering Informatics 25(2), 245–258.CrossRefGoogle Scholar
Huang, L., Breit, M., & Mensinger, M. (2012). Approach to handle architectural flexibility requirements for automated structural design proposals of steel concrete office buildings in early design phases. Proc. 2012 EG-ICE Workshop, Herrsching, Germany, July 4–6.Google Scholar
Janssen, P.H.T. (2009). An evolutionary system for design exploration. In Joining Languages, Cultures and Visions: CAAD Futures 2009 (Tidafi, T., & Dorta, T., Eds.). Montréal: Les Presses de l'Université de Montréal.Google Scholar
Knight, T.W. (1992). Designing with grammars. In CAAD Futures 1991, Computer Aided Architectural Design Futures 91: Education, Research, Applications (Schmitt, G., Ed). Braunschweig/Wiesbaden: Friedrich Vieweg & Sohn Verlagsgesellschaft GmbH.Google Scholar
Königseder, C., & Shea, K. (2014). Systematic rule analysis of generative design grammars. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28(3), 227–238.CrossRefGoogle Scholar
Kotsopoulos, S.D. (2008). From design concepts to design descriptions. International Journal of Architectural Computing 6(3), 335–359.CrossRefGoogle Scholar
Krish, S. (2011). A practical generative design method. Computer-Aided Design 43(1), 88–100.CrossRefGoogle Scholar
Liggett, R.S. (2000). Automated facilities layout: past, present and future. Automation in Construction 9(2), 197–215.CrossRefGoogle Scholar
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proc. 5th Berkeley Symp. Mathematical Statistics and Probability: Vol. 1. Statistics, pp. 281–297. Berkeley, CA: University of California Press.Google Scholar
Maher, M.L. (1985). HI-RISE and beyond: directions for expert systems in design. Computer-Aided Design 17(9), 420–427.CrossRefGoogle Scholar
Maher, M.L. (2000). A model of coevolutionary design. Engineering With Computers 16, 195–208.CrossRefGoogle Scholar
Matthews, K., Duff, S., & Corner, D. (1998). A model for integrated spatial and structural design of buildings. Proc. 3rd Conf. Computer-Aided Architectural Design Research in Asia CAADRIA (Sasada, T., Yamaguchi, S., Morozumi, M., Kaga, A., & Homma, R., Eds.), Osaka, Japan: Osaka University.Google Scholar
Mora, R., Rivard, H., & Bédard, C. (2008). A geometric modelling framework for conceptual structural design from early digital architectural models. Advanced Engineering Informatics 22(2), 254–270.CrossRefGoogle Scholar
National Institute of Standards and Technology. (1993). Draft Federal Information Processing Standards Publication 183: Integration Definition for Function Modeling (IDEF0). Gaithersburg, MD: National Institute of Standards and Technology. Accessed at http://www.idef.com/pdf/idef0.pdfGoogle Scholar
Peters, B., & Peters, T. (2013). Inside Smartgeometry, Expanding the Architectural Possibilities of Computational Design. Hoboken, NJ: Wiley.CrossRefGoogle Scholar
Rafiq, M.Y. (2000). A design support tool for optimum building concept generation using a structured genetic algorithm. International Journal of Computer-Integrated Design and Construction 2(2), 92–102.Google Scholar
Rafiq, M.Y., & MacLeod, I.A. (1988). Automatic structural component definition from a spatial geometry model. Engineering Structures 10(1), 37–40.CrossRefGoogle Scholar
Regateiro, F., Bento, J., & Dias, J. (2012). Floor plan design using block algebra and constraint satisfaction. Advanced Engineering Informatics 26(2), 361–382.CrossRefGoogle Scholar
Sacks, R., & Warszawski, A. (1997). A project model for an automated building system: design and planning phases. Automation in Construction 7(1), 21–34.CrossRefGoogle Scholar
Sacks, R., Warszawski, A., & Kirsch, U. (2000). Structural design in an automated building system. Automation in Construction 10(1), 181–197.CrossRefGoogle Scholar
Scherer, R.J., & Gehre, A. (2000). An approach to a knowledge-based design assistant system for conceptual structural system design. Proc. ECPPM 2000, Product and Process Modelling in Building and Construction (Goncalves, R., Steiger-Garcao, A., & Scherer, R.J., Eds.), pp. 323–328. Rotterdam: Balkema.Google Scholar
Shaw, D., Miles, J., & Gray, A. (2008). Determining the structural layout of orthogonal framed buildings. Computers and Structures 86(19–20), 1856–1864.CrossRefGoogle Scholar
Shea, K., & Cagan, J. (1999). The design of novel roof trusses with shape annealing: assessing the ability of a computational method in aiding structural designers with varying design intent. Design Studies 20, 3–23.CrossRefGoogle Scholar
Sigmund, O. (2001). A 99 line topology optimization code written in Matlab. Structural and Multidisciplinary Optimization 21, 120–127.CrossRefGoogle Scholar
Smulders, C.D.J., & Hofmeyer, H. (2012). An automated stabilization method for spatial to structural design transformations. Advanced Engineering Informatics 26(4), 653–950.CrossRefGoogle Scholar
Stiny, G. (1980). Introduction to shape and shape grammars. Environment and Planning 7(3), 343–351.CrossRefGoogle Scholar
Stiny, G. (2006). Shape: Talking About Seeing and Doing. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Torghabehi, O., & Buelow, P. von. (2014). Performance oriented generative design of structural double skin facades inspired by cell morphologies. Proc. IASS-SLTE 2014 Symp. Shells, Membranes and Spatial Structures: Footprints (Brazil, M.L.R.F., & Pauletti, R.M.O., Eds.), pp. 1–9, Brasila, Brazil, September 15–19.Google Scholar
Turrin, M., Von Buelow, P., & Stouffs, R. (2011). Design explorations of performance driven geometry in architectural design using parametric modelling and genetic algorithms. Advanced Engineering Informatics 25(4), 656–675.CrossRefGoogle Scholar
Wang, Y., & Pinto Duarte, J. (2002). Automatic generation and fabrication of designs. Automation in Construction 11(3), 291–302.CrossRefGoogle Scholar
Weise, W., Katranuschkov, P., & Scherer, R.J. (2000). A proposed extension of the IFC project model for structural systems. Proc. ECPPM 2000, Product and Process Modelling in Building and Construction (Goncalves, R., Steiger-Garcao, A., & Scherer, R.J., Eds.), pp. 1–8. Rotterdam: Balkema.Google Scholar
Xie, Y.M., & Steven, G.P. (1997). Evolutionary Structural Optimization. New York: Springer.CrossRefGoogle Scholar