Crowdsourced Clustering of Computer Generated Floor Plans

  • David Sousa-Rodrigues
  • Mafalda Teixeira de Sampayo
  • Eugénio Rodrigues
  • Adélio Rodrigues Gaspar
  • Álvaro Gomes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9320)


This paper identifies the main criteria used by architecture specialists in the task of clustering alternative floor plan designs. It shows how collective actions of respondents lead to their clustering by carrying out an online exercise. The designs were randomly pre-generated by a hybrid evolutionary algorithm and a questionnaire was posed in the end for the respondents to indicate which similarity criteria they have used. A network of designs was then obtained and it was partitioned into clusters using a modularity optimization algorithm. The results show that the main criterion used was the internal arrangement of spaces, followed by overall shape and by external openings orientation. This work allows the future development of novel algorithms for automatic classification, clustering, and database retrieval of architectural floor plans.


Architecture Network theory Crowdsourcing Clustering Floor plan design Online survey 



Sousa-Rodrigues, D. was partially supported by project Topdrim FP7-ICT-2011-8/318121. Rodrigues, E., Gaspar, A.R., and Gomes, Á. were partially supported by project Automatic Generation of Architectural Floor Plans with Energy Optimization (GerAPlanO), QREN 38922, CENTRO-07-0402-FEDER-038922 and framed under the Energy for Sustainability Initiative at University of Coimbra.


  1. 1.
    Liggett, R.S.: Automated facilities layout: past, present and future. Autom. Constr. 9(2), 197–215 (2000)CrossRefGoogle Scholar
  2. 2.
    Steadman, P.: Generative design methods and the exploration of worlds of formal possibility. Architect. Des. 84(5), 24–31 (2014)Google Scholar
  3. 3.
    Gero, J.S., Kazakov, V.A.: Evolving design genes in space layout planning problems. Artif. Intell. Eng. 12(3), 163–176 (1998)CrossRefGoogle Scholar
  4. 4.
    Mitchell, W.J., Steadman, J.P., Liggett, R.S.: Synthesis and optimization of small rectangular floor plans. Environ. Plan. B 3(1), 37–70 (1976)CrossRefGoogle Scholar
  5. 5.
    Quiroz, J.C., Louis, S.J., Banerjee, A., Dascalu, S.M.: Towards creative design using collaborative interactive genetic algorithms. In: Evolutionary Computation 2009, pp. 1849–1856. IEEE, May 2009Google Scholar
  6. 6.
    Koenig, R., Knecht, K.: Comparing two evolutionary algorithm based methods for layout generation: dense packing versus subdivision. Artif. Intell. Eng. Des. Anal. Manuf. 28(03), 285–299 (2014)CrossRefGoogle Scholar
  7. 7.
    Rodrigues, E., Gaspar, A., Gomes, Á.: An evolutionary strategy enhanced with a local search technique for the space allocation problem in architecture, part 1: methodology. Comput. Aided-Des. 45(5), 887–897 (2013)CrossRefGoogle Scholar
  8. 8.
    Rodrigues, E., Gaspar, A., Gomes, Á.: An evolutionary strategy enhanced with a local search technique for the space allocation problem in architecture, part 2: validation and performance tests. Comput. Aided-Des. 45(5), 898–910 (2013)CrossRefGoogle Scholar
  9. 9.
    Rodrigues, E., Gaspar, A., Gomes, Á.: An approach to the multi-level space allocation problem in architecture using a hybrid evolutionary technique. Autom. Constr. 35, 482–498 (2013)CrossRefGoogle Scholar
  10. 10.
    Michalek, J.J., Choudhary, R., Papalambros, P.Y.: Architectural layout design optimization. Eng. Optim. 34, 461–484 (2002)CrossRefGoogle Scholar
  11. 11.
    Garza, A.G.D.S., Maher, M.L.: GENCAD: a hybrid analogical/evolutionary model of creative design. In: Gero, J.S., Maher, M.L. (eds.) Computational And Cognitive Models Of Creative Design V, pp. 141–171. Key Center of Design Computing and Cognition, University of Sidney, Sydney (2001)Google Scholar
  12. 12.
    Virirakis, L.: GENETICA: a computer language that supports general formal expression with evolving data structures. IEEE Trans. Evol. Comput. 7(5), 456–481 (2003)CrossRefGoogle Scholar
  13. 13.
    Kalay, Y.E.: Architecture’s New Media: Principles, Theories and Methods of Computer-Aided Design. The MIT Press, Cambridge (2004)Google Scholar
  14. 14.
    Wright, K.B.: Researching internet-based populations: advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. J. Comput.-Mediated Commun. 10(3), 00–00 (2005)CrossRefGoogle Scholar
  15. 15.
    Evans, J.R., Mathur, A.: The value of online surveys. Internet Res. 15(2), 195–219 (2005)CrossRefGoogle Scholar
  16. 16.
    Urbano, P., Sousa-Rodrigues, D.: The advantage of using rules in online surveys. Revista de Ciências da Computação III(3), 38–49 (2008)Google Scholar
  17. 17.
    Urbano, P., Sousa-Rodrigues, D.: Rule based systems applied to online surveys. In: IADIS WWW/Internet Conference. Freiburg, Oct 2008Google Scholar
  18. 18.
    Sousa-Rodrigues, D., de Sampayo, M.T., Rodrigues, E., Gaspar, A.R., Gomes, Á., Antunes, C.H.: Online survey for collective clustering of computer generated architectural floor plans. arXiv preprint arXiv:1504.08145 (2015)
  19. 19.
    de las Heras, L.P., Fernandez, D., Fornes, A., Valveny, E., Sanchez, G., Llados, J.: Perceptual retrieval of architectural floor plans. In: 10th IAPR International Workshop on Graphics Recognition (2013)Google Scholar
  20. 20.
    Dias, M.S., Eloy, S., Carreiro, M., Proênça, P., Moural, A., Pedro, T., Freitas, J., Vilar, E., d’Alpuim, J., Azevedo, S.: Designing better spaces for people: virtual reality and biometric sensing as tools to evaluate space use. In: Gu, N., Watanabe, S., Erhan, H., Haeusler, M.H., Huang, W., Sosa, R. (eds.) Rethinking Comprehensive Design: Speculative Counterculture, Proceedings of the 19th International Conference of the Association of Computer-Aided Architectural Design Research in Asia, Hong Kong (2014)Google Scholar
  21. 21.
    Dias, M.S., Eloy, S., Carreiro, M., Vilar, E., Marques, S., Moural, A., Proênça, P., Cruz, J., d’Alpuim, J., Carvalho, N., Azevedo, A.S., Pedro, T.: Space perception in virtual environments - on how biometric sensing in virtual environments may give architects users’s feedback. In: Thompson, E.M. (ed.) Fusion - Proceedings of the 32nd eCAADe Conference, Newcastle upon Tyne, England, UK, vol. 2, pp. 271–280, September 2014Google Scholar
  22. 22.
    de Sampayo, M.T., Sousa-Rodrigues, D., Jimenez-Romero, C., Johnson, J.H.: Peer assessment in architecture education. In: International Conference on Technology and Innovation, Brno, Czech Republic, September 2014Google Scholar
  23. 23.
    Girvan, M., Newman, M.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99, 7821–7826 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Anthonisse, J.M.: The rush in a directed graph. Technical report, Stichting Mathematicsh Centrum, Amsterdam (1971)Google Scholar
  25. 25.
    Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40(1), 35–41 (1977)CrossRefGoogle Scholar
  26. 26.
    Brandes, U.: A faster algorithm for betweenness centrality*. J. Math. Sociol. 25(2), 163–177 (2001)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • David Sousa-Rodrigues
    • 1
  • Mafalda Teixeira de Sampayo
    • 2
  • Eugénio Rodrigues
    • 3
  • Adélio Rodrigues Gaspar
    • 3
  • Álvaro Gomes
    • 4
  1. 1.Faculty of Maths, Computing and Technology, Centre of Complexity and DesignThe Open UniversityMilton KeynesUK
  2. 2.Department of Architecture, CIESLisbon University InstituteLisbonPortugal
  3. 3.Department of Mechanical Engineering, ADAI, LAETAUniversity of CoimbraCoimbraPortugal
  4. 4.Department of Electrical and Computer Engineering, INESC CoimbraUniversity of CoimbraCoimbraPortugal

Personalised recommendations