Fuzzy architectural spatial analysis (FASA) (alsofuzzy inference system (FIS) based architectural space analysis orfuzzy spatial analysis) is aspatial analysis method of analysing the spatial formation and architectural space intensity within any architectural organization.[1]
Fuzzy architectural spatial analysis was developed byBurcin Cem Arabacioglu (2010) from the architectural theories ofspace syntax[2][3] andvisibility graph analysis,[4] and is applied with the help of a fuzzy system with a Mamdami inference system based onfuzzy logic within any architectural space. Fuzzy architectural spatial analysis model analyses the space by considering the perceivable architectural element by their boundary and stress characteristics and intensity properties. The method is capable of taking allsensorial factors into account during analyses in conformably with theperception process of architectural space which is a multi-sensorial act.
^Arabacioglu, Burcin Cem (2010). "Using fuzzy inference system for architectural space analysis". Applied Soft Computing 10 (3): 926–937.doi:10.1016/j.asoc.2009.10.011
^Hillier, Bill and Hanson, Julienne (1984), "The Social Logic of Space", Cambridge University Press: Cambridge.
^Hillier, Bill (1999), "Space is the Machine: A Configurational Theory of Architecture", Cambridge University Press: Cambridge.
^Turner, Alasdair; Doxa, Maria; O'Sullivan, David and Penn, Alan (2001). "From isovists to visibility graphs: a methodology for the analysis of architectural space". Environment and Planning B 28 (1): 103–121.doi:10.1068/b2684
Arabacioglu, Burcin Cem (2010). "Using fuzzy inference system for architectural space analysis".Applied Soft Computing.10 (3):926–937.doi:10.1016/j.asoc.2009.10.011.
Cekmis, Asli; Hacihasanoglu, Isis; Ostwald, Michael J (2013). "A computational model for accommodating spatial uncertainty: Predicting inhabitation patterns in open-planned spaces".Building and Environment.73:115–126.doi:10.1016/j.buildenv.2013.11.023..
Dutta, Kamlesh; Sarthak, Siddhant (2011). "Architectural space planning using evolutionary computing approaches: a review".Artificial Intelligence Review.36 (4):311–321.doi:10.1007/s10462-011-9217-y.S2CID24000906..
Wurzer, Gabriel (2013). "In-process agent simulation for early stages of hospital planning".Mathematical and Computer Modelling of Dynamical Systems: Methods, Tools and Applications in Engineering and Related Sciences.19 (4):331–343.doi:10.1080/13873954.2012.761638.S2CID40775203..
Yang, Xin; Xu, Duan-qing; Zhao, Lei (2013). "Efficient data management for incoherent ray tracing".Applied Soft Computing.13 (1):1–8.doi:10.1016/j.asoc.2012.07.002..
