The Application of Artificial Neural Networks to Facilitate the Architectural Design Process

  • Pao-Kuan WuEmail author
  • Shih-Yuan Liu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1069)


In this paper, the main purpose is how to apply Artificial Neural Networks (ANN) to facilitate the process of architectural site analysis which is one of the crucial steps in architectural design process. The experiment is based on a student design project which can demonstrate the way of AI application in this paper. The goal of this student design project is to arrange several studios including public studios and private studios on a particular parcel. In the experiment, the ANN models were trained by several environmental factors which can help students perceive better environmental features for the purposes of this design project; then the trained ANN models can be used to determine where the appropriate locations are for the arrangement of the public and private studios. Furthermore, by analyzing the ANN weight values can reveal more information about which environmental factors are more important than others.


Artificial Neural Networks Computer-aided design Architectural site analysis Geographic Information System 


  1. 1.
    Lawson, B.: How Designers Think: The Design Process Demystified, 4th edn. Architectural, London (2006)CrossRefGoogle Scholar
  2. 2.
    Salman, H.S., Laing, R., Conniff, A.: The impact of computer aided architectural design programs on conceptual design in an educational context. Des. Stud. 35, 412–439 (2014)CrossRefGoogle Scholar
  3. 3.
    Oxman, R.: Digital architecture as a challenge for design pedagogy: theory, knowledge, models and medium. Des. Stud. 29, 99–120 (2008)CrossRefGoogle Scholar
  4. 4.
    Bishop, C.M.: Neural Networks for Pattern Recognition, 1st edn. Oxford University, New York (1995)zbMATHGoogle Scholar
  5. 5.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)zbMATHGoogle Scholar
  6. 6.
    Pijanowski, B., Tayyebi, C.A., Doucette, J., Pekin, B.K., Braun, D., Plourde, J.: A big data urban growth simulation at a national scale: configuring the GIS and neural network based land transformation model to run in a high performance computing (HPC) environment. Environ. Model Softw. 51, 250–268 (2014)CrossRefGoogle Scholar
  7. 7.
    Basse, R.M., Omrani, H., Charif, O., Gerber, P., Bódis, K.: Land use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale. Appl. Geogr. 53, 160–171 (2014)CrossRefGoogle Scholar
  8. 8.
    Patuelli, R.S., Reggiani, L.A., Nijkamp, P.: Neural networks and genetic algorithms as forecasting tools: a case study on German regions. Environ. Plan. B Plan. Des. 35, 701–722 (2008)CrossRefGoogle Scholar
  9. 9.
    Heppenstall, A.J., Evans, A.J., Birkin, M.H.: Genetic algorithm optimisation of an agent-based model for simulating a retail market. Environ. Plan. B Plan. Des. 34, 1051–1071 (2007)CrossRefGoogle Scholar
  10. 10.
    Grekousis, G., Manetos, P., Photis, Y.N.: Modeling urban evolution using neural networks, fuzzy logic and GIS: the case of the Athens metropolitan area. Cities 30, 193–203 (2013)CrossRefGoogle Scholar
  11. 11.
    Alajmi, A., Wright, J.: Selecting the most efficient genetic algorithm sets in solving unconstrained building optimization problem. Int. J. Sustain. Built Environ. 3, 18–26 (2014)CrossRefGoogle Scholar
  12. 12.
    Alexander, C., Ishikawa, S., Silverstein, M.: A Pattern Language: Towns, Buildings, Construction. Oxford University Press, New York (1977)Google Scholar
  13. 13.
    Marzban, C.: Basic statistics and basic AI: neural networks. In: Haupt, S.E., Pasini, A., Marzban, C. (eds.) Artificial Intelligence Methods in the Environmental Sciences, 1st edn., pp. 15–47. Springer, Heidelberg (2009)Google Scholar
  14. 14.
    Haupt, S.E., Lakshmanan, V., Marzban, C., Pasini, A., and Williams, J.K.: Environmental science models and artificial intelligence. In: Haupt, S.E., Pasini, A., Marzban, C. (eds.) Artificial Intelligence Methods in the Environmental Sciences, 1st edn., pp. 3–13. Springer, Heidelberg (2009)Google Scholar
  15. 15.
    Fischer, M.M.: Computational neural networks: a new paradigm for spatial analysis. Environ. Plan. A 30, 1873–1891 (1997)CrossRefGoogle Scholar
  16. 16.
    Openshaw, S.: Neural network, genetic, and fuzzy logic models of spatial interaction. Environ. Plan. A 30, 1857–1872 (1998)CrossRefGoogle Scholar
  17. 17.
    Zhang, G.P.: An investigation of neural networks for linear time-series forecasting. Comput. Oper. Res. 28, 1183–1202 (2001)CrossRefGoogle Scholar
  18. 18.
    Beirão, J.N., Nourian, P., Mashhoodi, B.: Parametric urban design: an interactive sketching system for shaping neighborhoods. Presented at the the 29th International Conference on Education and research in Computer Aided Architectural Design in Europe, Ljubljana, Slovenia: University of Ljubljana (2011)Google Scholar
  19. 19.
    Beirão, J.N., Duarte, J.P., Stouffs, R.: Creating specific grammars with generic grammars: towards flexible urban design. Nexus Network J. 13, 73–111 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Asia UniversityTaichungTaiwan

Personalised recommendations