Fuzzy Logic-Based Model for Evaluating Architectural Space Quality
الموضوعات : Space Ontology International Journal
Mansoureh Sadrykia
1
,
Somayeh Sadrykia
2
1 - Deptartment of Geomatics Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran
2 - Department of Architecture, Urbanism and Art, Urmia University, Urmia, Iran
الکلمات المفتاحية: Fuzzy Sets, Architectural Quality, Space Evaluation, Qualitative Criteria,
ملخص المقالة :
Considering the significance of architectural space quality in human beings lives and the multi-faceted nature of its indicators, there is a need for scientific and reliable methods for modeling space quality that widely integrate and evaluate affecting indicators. Despite the effective role of space quality indicators’ importance in promoting architectural designs, researchers have not yet studied various criteria involved in evaluating architectural space comprehensively. Despite the necessity of taking into account all aspects of space quality for specifying a complete set of influencing criteria and accordingly determining the importance of the considered criteria for creating desirable space quality, this issue has not yet been considered well in previous studies. Currently, architects rely on professional expertise to enhance space quality. However, a mathematical model that systematically evaluates qualitative criteria can serve as a more objective tool for optimizing design outcomes. Fuzzy sets theory provides an efficient tool for considering vagueness uncertainties of the professionals' knowledge in a mathematical framework guaranteeing that outcomes of space evaluation will be concrete and reliable. The novelty of present study includes a) integrating different aspects of space quality in one space evaluation model, b) considering and modeling the fact that some criteria are related to more than one quality indicator and c) considering that expertise on influencing criteria weights of importance are qualitative judgments that include vagueness uncertainty, utilizing fuzzy sets theory to model the uncertainty quantitatively and mathematically. We first studied the most important quality criteria defined by scholars and discussed major relevant criteria. We showed that quality of architectural space have three main indicators, each with various criteria. The weights of the criteria in each indicator were categorized into low, medium and high level. Using fuzzy sets theory the most influential criteria relevant to each of main quality indicators were determined. Among the specified 30 crucial quality criteria, “legibility”, “visual clarity”, “hierarchy”, “objective transparency”, and “diversity” had the highest importance for more than one indicator. Therefore, it can be concluded that they play a more significant role in creating space quality. Finally, we presented a fuzzy space quality evaluation conceptual model. The presented model provides architects with a set of helpful design rules that would enable them to reliably consider the effects of each space quality criterion to design more desirable architectural spaces for users. This study can be a guide for planners, designers, managers and policy makers engaged in designing and assessing various spaces.
Anuar, A., Mekhribanu, G., & Abdullah, A. (2024). The interrelation between socio-cultural aspects and the quality of public open spaces in Almaty, Kazakhstan. Planning Malaysia, 22(33). https://doi.org/10.21837/pm.v22i33.1568
Appleyard, D., & Lintell, M. (1972). The environmental quality of city streets: the residents' viewpoint. Journal of the American institute of planners, 38(2), 84-101.
Arabacioglu, B. C. (2010). Using fuzzy inference system for architectural space analysis. Applied Soft Computing, 10(3), 926-937.
Aşilioğlu, F., & Çay, R. D. (2020). Determination of quality criteria of urban pedestrian spaces. ACE: architecture, city and environment, 15(44). https://raco.cat/index.php/ACE/article/view/385274
Atman, C. J., Cardella, M. E., Turns, J., & Adams, R. (2005). Comparing freshman and senior engineering design processes: an in-depth follow-up study. Design studies, 26(4), 325-357.
Bostancioglu, E. (2021). Double skin façade assessment by fuzzy AHP and comparison with AHP. Architectural Engineering and Design Management, 17(1-2), 110-130.
Canter, D. (1977). The Psychology of Place. In: Palgrave Macmillan.
Carmona, M. (2010). Contemporary public space: Critique and classification, part one: Critique. Journal of urban design, 15(1), 123-148.
Chapman, D. (2003). Creating neighbourhoods and places in the built environment. Taylor & Francis.
Chou, C.-C. (2003). The canonical representation of multiplication operation on triangular fuzzy numbers. Computers & Mathematics with Applications, 45(10-11), 1601-1610.
Diker, F., & Erkan, İ. (2022). Fuzzy logic method in the design of elementary school classrooms. Architectural Engineering and Design Management, 18(5), 739-758.
Eilouti, B. (2020). Reinventing the wheel: A tool for design quality evaluation in architecture. Frontiers of Architectural Research, 9(1), 148-168.
Ezzeldin, M., Assem, A., & Abdelmohsen, S. (2021). Automated assessment of architectural spatial layout configurations using fuzzy logic. Archnet-IJAR: International Journal of Architectural Research, 15(2), 328-343.
Gehl, J. (2011). Life between buildings.
Gibson, V., Rowley, A., & Ward, C. (1997). Does short termism affect the quality of urban design in the UK?
Golkar, K. (2001). Components of urban design quality.
Golkar, K. (2007). The concept of quality of life in urban design. Safeh Quarterly, 16(44), 75-66.
Harputlugil, T., Gültekin, A. T., Prins, M., & Topçu, Y. İ. (2016). Architectural design quality assessment based on analytic hierarchy process: A case study. METU Journal of the Faculty of Architecture, 31(2).
Jacob, J. (1961). The death and life of great American. Cities. New York: Vintage Books.
Lang, J. (1987). Creating Architectural Theory. In: Van Nostrand.
Lynch, K. (1984). Good city form. MIT press.
Mazur, Ł., Bać, A., Vaverková, M. D., Winkler, J., Nowysz, A., & Koda, E. (2022). Evaluation of the quality of the housing environment using multi-criteria analysis that includes energy efficiency: a review. Energies, 15(20), 7750.
Montgomery, D. C. (2019). Introduction to statistical quality control. John wiley & sons.
Montgomery, J. (1998). Making a city: Urbanity, vitality and urban design. Journal of urban design, 3(1), 93-116.
Moulaert, F., Schreurs, J., & Van Dyck, B. (2011). Reading space to ʻaddressʼ spatial quality. Spindus: Spatial Innovation Planning, Design and User Involvement.
Moulaert, F., Van Dyck, B., Khan, A. Z., & Schreurs, J. (2013). Building a meta-framework to ‘address’ spatial quality. International planning studies, 18(3-4), 389-409.
Nadin, M. (2018). Application of fuzzy logic in design of an aesthetics-based interactive architectural space.
Nady, A., Hassan, D. K., & Assem, A. (2023). A Proposed Framework for Automated Evaluation of Architectural Spatial Configurations Using Fuzzy Logic Approach. JES. Journal of Engineering Sciences, 51(1), 19-48.
Naghibi Iravani, S., Karimimansoob, V., Arash Sohrabi, S., Gheitarani, N., & Dehghan, S. (2024). Applying Fuzzy Logic and Analysis Hierarchy Process (AHP) in the Design of Residential Spaces; Case of Study: Arak City, 13(2), 144-160.
Nazari, A., & Torabi, M. R. (2022). Identification and Prioritization of Quality Indicators for Building Facade Design; Using a Fuzzy Approach. Journal of Iranian Architecture & Urbanism (JIAU), 13(2), 281-295.
Nordwall, U., & Olofsson, T. (2013). Architectural caring. Architectural qualities from a residential property perspective. Architectural Engineering and Design Management, 9(1), 1-20.
Pakzad, J. (2002). Quality of space. Abadi, 2 (2), 100-110. [in Persian]
Punter, J., & Carmona, M. (1997). The design dimension of planning: theory, content, and best practice for design policies. Taylor & Francis.
Rapoport, A. (1970). The study of spatial quality. Journal of Aesthetic Education, 4(4), 81-95.
Relph, E. (2016). The paradox of place and the evolution of placelessness. In Place and placelessness revisited (pp. 20-34). Routledge.
Rossi, P. H., Lipsey, M. W., & Freeman, H. E. (2003). Evaluation: A systematic approach. Sage publications.
Sanni-Anibire, M. O., & Hassanain, M. A. (2016). Quality assessment of student housing facilities through post-occupancy evaluation. Architectural Engineering and Design Management, 12(5), 367-380.
Shao, Q.-G., Jiang, C.-C., Lo, H.-W., & Liou, J. J. H. (2023). Establishing a sustainable development assessment framework for a smart city using a hybrid Z-fuzzy-based decision-making approach. Clean Technologies and Environmental Policy, 25(9), 3027-3044. https://doi.org/10.1007/s10098-023-02547-7
Simpson, J. A., Weiner, E. S., & Proffitt, M. (1993). Oxford English dictionary: additions series (Vol. 3). Oxford [England]: Clarendon Press; New York: Oxford University Press.
Sozen, M., & Tanyeli, U. (1994). Dictionary of Art Terms and Concepts. In: Remzi Publications, Istanbul.
Stringer, A., Dunne, J., & Boussabaine, H. (2012). Schools design quality: A user perspective. Architectural Engineering and Design Management, 8(4), 257-272.
Thomson, D. S., Austin, S. A., Devine-Wright, H., & Mills, G. R. (2003). Managing value and quality in design. Building Research & Information, 31(5), 334-345.
Tibbalds, F. (2012). Making people-friendly towns: Improving the public environment in towns and cities. Taylor & Francis.
Voordt, D. (2005). van der,(2005), Architecture in use: an introduction to the programming, design and evaluation of buildings. In: Amsterdam: Architectural Press.
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
Zemmouri, N., & Schiler, M. E. (2005). Application of fuzzy logic in interior daylight estimation. Journal of Renewable Energies, 8(1), 55-62.
Zimmermann, H. J. (1996). Fuzzy Set Theory - and Its Applications. Springer Netherlands. https://books.google.com/books?id=vd9QAAAAMAAJ
