Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-25T19:17:45.022Z Has data issue: false hasContentIssue false
  • English
  • Français

Design characteristics and aesthetics in evolutionary design of architectural forms directed by fuzzy evaluation

Published online by Cambridge University Press:  26 May 2020

Agnieszka Mars Open the ORCID record for Agnieszka Mars [Opens in a new window] ,
Ewa Grabska ,
Grażyna Ślusarczyk  and
Barbara Strug
Agnieszka Mars*
Affiliation:
Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Łojasiewicza 11, Kraków 30-348, Poland
Ewa Grabska
Affiliation:
Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Łojasiewicza 11, Kraków 30-348, Poland
Grażyna Ślusarczyk
Affiliation:
Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Łojasiewicza 11, Kraków 30-348, Poland
Barbara Strug
Affiliation:
Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Łojasiewicza 11, Kraków 30-348, Poland
*
Author for correspondence: Agnieszka Mars, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper deals with design characteristics-oriented approach to architectural design based on the combination of three methods – recognition, generation, and evaluation. Design characteristics are understood as a set of specific features which constitute a discriminant of a class of architectural forms. The Biederman recognition-by-components theory is used to recognize the design structure. An evolutionary algorithm, which serves as a generative tool, is driven by the fuzzy evaluation based on Birkhoff's aesthetic measure. Phenotypes of architectural objects are seen as configurations of Biederman's basic components essential for visual perception. Genotypes of these objects are represented by graphs with bonds, where nodes represent object components, node bonds represent component surfaces, while graph edges represent relations between surfaces. Graph evolutionary operators, that is, crossover and mutation, are defined in such a way that they preserve characteristic features seen as design requirements specified for designed objects. The fitness function is determined by the fuzzy evaluation of designs based on Birkhoff's aesthetic measure for polygons adapted for three-dimensional solids. The approach is illustrated by examples of designing objects with the use of a fuzzy evaluation mechanism, which takes into account both aesthetic criteria and the degree to which design requirements corresponding to object characteristic features are satisfied.

Keywords

Aesthetic measurearchitectural formdesign characteristicsevolutionary designgraph representation
Type
Research Article
Information
AI EDAM , Volume 34 , Special Issue 2: Thematic Collection: Design Cognition and Computing , May 2020 , pp. 147 - 159
Copyright
Copyright © Cambridge University Press 2020

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

Alexander, C, Ishikawa, S, Silverstein, M, Jacobson, M, Fiksdahl-King, I and Angel, S (1977). A Pattern Language: Towns, Buildings, Construction. New York: Oxford University Press.Google Scholar
Biederman, I (1987) Recognition-by-components: a theory of human image understanding. Psychological Review 94, 115–147.CrossRefGoogle ScholarPubMed
Birkhoff, GD (1933) Aesthetic Measure. Cambridge, MA:Harvard University Press.CrossRefGoogle Scholar
De Jong, T and Van der Voordt, TJM (2002) Ways to study - criteria for scientific study and design. In De Jong, T and Van der Voordt, TJM (eds), Ways to Study and Research Architectural, Urban and Technical Design. Delft: Delft University Press, pp. 19–32.Google Scholar
De Silva Garza, G and Maher, ML (1999) Evolving design layout cases to satisfy feng shui constraints. Proceedings of the Fourth Conference on Computer-Aided Architectural Design Research in Asia, CAADRIA99, Shanghai, pp. 115-124.Google Scholar
Gardner, B and Krishnamurti, R (2008) Ordering the Aesthetic (A+) in Architecture: Advancing a Theory of Modular Computation, Nexus.Google Scholar
Garip, E and Garip, B (2012) Aesthetic evaluation differences between two interrelated disciplines: a comparative study on architecture and civil engineering students. Procedia – Social and Behavioral Sciences 51, 533–540.CrossRefGoogle Scholar
Goldberg, DE (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley.Google Scholar
Grabska, E and Borkowski, A (1996) Assisting creativity by composite representation. In Gero J.S. and Sudweeks F. (eds), Artificial Intelligence in Design ‘96. Dordrecht, Netherlands: Kluwer Academic Publishers, pp. 743–759.Google Scholar
Jupp, J and Gero, JS (2010) Let's look at style: visual and spatial representation and reasoning in design. In Argamon, S, Burns, K and Dubnov, S (eds),The Structure of Style. Springer, pp. 159–195.CrossRefGoogle Scholar
Kane, C and Schoenauer, M (1996) Topological optimum design using genetic algorithms. Control and Cybernetics 25, 1059–1088.Google Scholar
Kaplan, S (1987) Aesthetics, affect, and cognition: environmental preference from an evolutionary perspective. Environment and Behavior 19, 3–32.CrossRefGoogle Scholar
Kilman, C (2016) Small house, big impact: the effect of tiny houses on community and environment (PDF). Undergraduate Journal of Humanistic Studies (Carleton College),vol.2, Winter 2016.Google Scholar
Mars, A and Grabska, E (2015) Towards an implementable aesthetic measure for collaborative architecture design. In Luo Y (eds), Proceedings of the 12th International Conference on Cooperative Design, Visualization, and Engineering. CDVE 2015. Lecture Notes in Computer Science, Vol. 9320. Cham: Springer, pp. 72–75.CrossRefGoogle Scholar
Mitchell, M (1996) An Introduction to Genetic Algorithms. Cambridge, MA, USA: MIT Press Cambridge.Google Scholar
Rozenberg, G (1997). Handbook of Graph Grammars and Computing by Graph Transformations: Volume 1. Foundations. London: World Scientific.CrossRefGoogle Scholar
Santosa, H and Fauziah, N (2016) Aesthetic Evaluation of Restaurants Facade Through Public Preferences and Computational Aesthetic Approach. Proceedings of the 8th International Conference on Architecture Research and Design (AR+DC), November 1–2, 2016. Indonesia: Institut Teknologi Sepuluh (ITS), pp. 31–40.Google Scholar
Schnier, T and Gero, JS (1996) Learning representations for creative design using evolution. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10, 175–177.CrossRefGoogle Scholar
Scruton, R (1979) The Aesthetics of Architecture. Princeton, NJ: Princeton University Press.Google Scholar
Simon, H (1975) Style in design. In Eastman, C (ed), Spatial Synthesis in Computer-Aided Building Design. London: Applied Science, pp. 287–309.Google Scholar
Ślusarczyk, G, Strug, B and Stasiak, K (2016) An ontology-based graph approach to support buildings design conformity with a given style. Applied Ontology 11, 279–300.CrossRefGoogle Scholar
Strug, B, Grabska, E and Ślusarczyk, G (2014). Supporting the design process with hypergraph genetic operators. Advanced Engineering Informatics 28, 11–27.CrossRefGoogle Scholar
Strug, B, Ślusarczyk, G and Grabska, E (2017) Design patterns in generation of artefacts in required styles. Proc. Int. Conf. Generative Art 2016, GA'16, Domus Argenia Publisher, Milan, pp. 71–78.Google Scholar
Tarko, J and Grabska, E (2011) Aesthetic measure for three-dimensional objects. Machine Graphics and Vision 20, 439–454.Google Scholar
Tjalve, E (1979) A Short Course in Industrial Design. London: Newnes-Butterworths.Google Scholar
Wallendorf, M, Zinkhan, G and Zinkhan, LS (1981) Cognitive complexity and aesthetic preference. In Hirschman, EC and Holbrook, MB (eds), SV – Symbolic Consumer Behaviour. New York: Association for Consumer Research, pp. 52–59.Google Scholar
Whitfield, TWA and Slatter, PE (1979) The effects of categorisation and prototypicality on aesthetic choice in a furniture selection task. British Journal of Psychology 70, 65–75.CrossRefGoogle Scholar
Wong, SSY and Chan, KCC (2009) EvoArch: an evolutionary algorithm for architectural layout design. Computer-Aided Design 41, 649–667.CrossRefGoogle Scholar