Downloads
DOI:
https://doi.org/10.47982/jfde.2024.299Keywords:
Generative Façade Design, 2D Power Spectrum Analysis, Computational Design Tools, Non-uniform Façades, Structural Optimisation, Natural Light Control, NaturalnessAbstract
As the demand for highly efficient yet aesthetically pleasing, complex building envelope structures is rising worldwide, computational analysis and generative design tools are becoming ever so relevant. Previous methods for achieving a natural distribution of structural or shading elements in non-uniform façades are mostly based either on computer-generated pseudo-randomness or a literal biomorphic approach where a naturally occurring pattern is directly projected on the façade surface. As an alternative, this research introduces a novel technique for optimisation that utilises a two-dimensional Power Spectrum Analysis, suitable for numerically assessing the alignment of designed geometry with natural patterns. By integrating this optimisation method into the design process, the façade pattern generation can be automated and optimal design can be selected by evaluating multiple design solutions. Instead of using repetitive geometrical patterns or generated pseudo-randomness, patterns objectively similar to those occurring in nature can be created without directly copying natural structures. The distribution of the structural and shading elements controls the way natural light permeates the building and, considering the data gathered from images of natural scenes, this method can be used to design structures not only with optimal structural and energy performance but also with visual and psychological occupant comfort in mind.
How to Cite
Published
Issue
Section
License
Copyright (c) 2024 Martin Ivanov, Jun Sato
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors or their institutions retain copyright to their publications without restrictions.
References
AGC. (2014). Technical Specifications – Leoflex(TM).
Bak, P., Tang, C., & Wiesenfeld, K. (1988). Self-organized criticality. Phys. Rev. A, 38(1), 364-374. doi:https://doi.org/10.1103/PhysRevA.38.364 DOI: https://doi.org/10.1103/PhysRevA.38.364
Bonham, C. R., & Parmee, I. C. (2004, 4 2). Developments of the cluster oriented genetic algorithm (COGA). Engineering Optimization, 36(2), 249-279. doi:10.1080/03052150410001650160 DOI: https://doi.org/10.1080/03052150410001650160
Brigham, E. O. (1988). The fast Fourier transform and its applications. New Jersey: Prentice-Hall Inc.
Choe, B., & Sato, J. (2016). Transparent Structures. 104th ACSA Annual Meeting Proceedings, Shaping New Knowledges. Association of Collegiate Schools of Architecture. Retrieved 5 7, 2023, from https://www.acsa-arch.org/chapter/transparent-structuresthis-methodology-encouraged-the-fluid-adaptive-growth-of-the-structures-from-cellularmodule-based-models-to-a-full-scale-installation-the-spirit-of-play-and-investigation-wa/
Cichocka, J. M., Browne, W. N., & Rodriguez, E. (2017). Optimization in the Architectural Practice - An International Survey. CAADRIA 2017: Protocols, Flows, and Glitches, (pp. 387-396). Suzhou, China. doi:10.52842/conf.caadria.2017.387 DOI: https://doi.org/10.52842/conf.caadria.2017.387
Cichocka, J. M., Migalska, A., Browne, W. N., & Rodriguez, E. (2017). SILVEREYE – The Implementation of Particle Swarm Optimization Algorithm in a Design Optimization Tool. In G. Çağdaş, M. Özkar, L. F. Gül, & E. Gürer (Eds.), Computer-Aided Architectural Design. Future Trajectories (Vol. 724, pp. 151-169). Singapore: Springer Singapore. doi:10.1007/978-981-10-5197-5_9 DOI: https://doi.org/10.1007/978-981-10-5197-5_9
Cooley, J. W., Lewis, P. A., & Welch, P. D. (1969). The Fast Fourier Transform and Its Applications. EEE Transactions on Education, 12(1), 27-34. doi:doi: 10.1109/TE.1969.4320436. DOI: https://doi.org/10.1109/TE.1969.4320436
Costa, A., & Nannicini, G. (2018, 12). RBFOpt: an open-source library for black-box optimization with costly function evaluations. Mathematical Programming Computation, 10(4), 597-629. doi:10.1007/s12532-018-0144-7 DOI: https://doi.org/10.1007/s12532-018-0144-7
David, S. V., Vinje, W. E., & Gallant, J. L. (2004). Natural Stimulus Statistics Alter the Receptive Field Structure of V1 Neurons. Journal of Neuroscience, 24(31), 6991-7006. doi:10.1523/JNEUROSCI.1422-04.2004 DOI: https://doi.org/10.1523/JNEUROSCI.1422-04.2004
Fernandez, D., & Wilkins, A. (2008). Uncomfortable Images in Art and Nature. Perception, 37, 1098 - 1113. doi:10.1068/p5814 DOI: https://doi.org/10.1068/p5814
Field, D. J. (1987). Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America. A, Optics and image science, 4(12), 2379–2394. doi:https://doi.org/10.1364/josaa.4.002379 DOI: https://doi.org/10.1364/JOSAA.4.002379
Gircys, M., & Ross, B. J. (2019). Image Evolution Using 2D Power Spectra. Complexity, 2019, 21. doi:https://doi.org/10.1155/2019/7293193 DOI: https://doi.org/10.1155/2019/7293193
Gisiger, T. (2001). Scale invariance in biology: coincidence or footprint of a universal mechanism? Biological Reviews, 161-209. doi:https://doi.org/10.1017/S1464793101005607 DOI: https://doi.org/10.1017/S1464793101005607
Graham, D. J., & Field, D. J. (2007). Statistical regularities of art images and natural scenes: spectra, sparseness and nonlinearities. Spatial vision, 21(1-2), 149–164. doi:10.1163/156856807782753877 DOI: https://doi.org/10.1163/156856807782753877
Hagerhall, C. M., Purcella, T., & Taylor, R. (2004). Fractal dimension of landscape silhouette outlines as a predictor of landscape preference. Journal of Environmental Psychology, 24(2), 247–255. doi:10.1016/j.jenvp.2003.12.004 DOI: https://doi.org/10.1016/j.jenvp.2003.12.004
Harding, J., & Brandt-Olsen, C. (2018, 6). Biomorpher: Interactive evolution for parametric design. International Journal of Architectural Computing, 16(2), 144-163. doi:10.1177/1478077118778579 DOI: https://doi.org/10.1177/1478077118778579
Heusler, W., & Kadija, K. (2018). Advanced design of complex façades. Intelligent Buildings International, 220-233. doi:10.1080/17508975.2018.1493979 DOI: https://doi.org/10.1080/17508975.2018.1493979
Huang, Y., & Niu, J.-l. (2016). Optimal building envelope design based on simulated performance: History, current status and new potentials. Energy and Buildings, 117, 387-398. doi:https://doi.org/10.1016/j.enbuild.2015.09.025. DOI: https://doi.org/10.1016/j.enbuild.2015.09.025
Juricevic, I., Land, L., Wilkins, A., & Webster, M. (2010). Visual discomfort and natural image statistics. Perception, 39, 884-99. doi:10.1068/p6656. DOI: https://doi.org/10.1068/p6656
Kaplan, S., Kaplan, R., & Wendt, J. (1972). Rated preference and complexity for natural and urban visual material. Perception & Psychophysics, 12, 354–356. doi:10.3758/BF03207221 DOI: https://doi.org/10.3758/BF03207221
Kengo Kuma and Associates. (2013). Sunny Hills Japan. Aoyama, Tokyo, Japan. Retrieved from Kengo Kuma and Associates.
Ko, W., Kent, M., Levitt, B., & Betti, G. (2021). A Window View Quality Assessment Framework. LEUKOS, 18, 1-26. doi:10.1080/15502724.2021.1965889. DOI: https://doi.org/10.1080/15502724.2021.1965889
Larson, G. W., & Shakespeare, R. (1998). Rendering with Radiance. Michigan: Morgan Kaufmann.
Matusiak, B., & Klöckner, C. (2015). How we evaluate the view out through the window. Architectural Science Review, 59, 1-9. doi:10.1080/00038628.2015.1032879 DOI: https://doi.org/10.1080/00038628.2015.1032879
Melmer, T., Amirshahi, S. A., Koch, M., Denzler, J., & Redies, C. (2013). From regular text to artistic writing and artworks: Fourier statistics of images with low and high aesthetic appeal. Frontiers in human neuroscience(7), 106. doi:10.3389/fnhum.2013.00106 DOI: https://doi.org/10.3389/fnhum.2013.00106
Nagy, G., & Osama, N. (2016). Biomimicry, an Approach, for Energy Effecient Building Skin Design. Procedia Environmental Sciences, 34, 178-189. doi:10.1016/j.proenv.2016.04.017 DOI: https://doi.org/10.1016/j.proenv.2016.04.017
Nannicini, G. (2021, 2 11). On the implementation of a global optimization method for mixed-variable problems. Open Journal of Mathematical Optimization, 1-25. doi:10.5802/ojmo.3 DOI: https://doi.org/10.5802/ojmo.3
O’Hare, L., & Hibbard, P. (2011). Spatial frequency and visual discomfort. Vision research, 51, 1767-77. doi:10.1016/j.visres.2011.06.002. DOI: https://doi.org/10.1016/j.visres.2011.06.002
Oliva, A., Torralba, A., Guerin-dugue, A., & Herault, J. (1999). Global Semantic Classification of Scenes using Power Spectrum Templates. Challange of Image Retrieval. New Castle. doi:10.14236/ewic/CIR1999.9 DOI: https://doi.org/10.14236/ewic/CIR1999.9
Oliveira Santos, F., Louter, C., & Correia, J. R. (2018). Exploring Thin Glass Strength Test Methodologies. Challenging Glass Conference Proceedings, (pp. 713-724). doi:https://doi.org/10.7480/CGC.6.2192
Olshausen, B., & Field, D. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381, 607-9. doi:10.1038/381607a0 DOI: https://doi.org/10.1038/381607a0
Párraga, C. A., Troscianko, T., & Tolhurst, D. (2000). The human visual system is optimised for processing the spatial information in natural visual images. Current biology, 35-8. doi:10.1016/S0960-9822(99)00262-6 DOI: https://doi.org/10.1016/S0960-9822(99)00262-6
Pastore, L., & Andersen, M. (2022). The influence of façade and space design on building occupants’ indoor experience. Journal of Building Engineering, 46. doi:10.1016/j.jobe.2021.103663 DOI: https://doi.org/10.1016/j.jobe.2021.103663
Redies, C., Hasenstein, J., & Denzler, J. (2007). Fractal-like image statistics in visual art: similarity to natural scenes. Spatial Vision, 21((1-2)), 137–148. doi:10.1163/156856807782753921 DOI: https://doi.org/10.1163/156856807782753921
Ruderman, D. L. (1994). The statistics of natural images. Network: Computation in Neural Systems, 5(4), 517-548. doi:doi: 10.1088/0954-898X_5_4_006 DOI: https://doi.org/10.1088/0954-898X/5/4/006
Ruderman, D. L., & Bialek, W. (1994, Aug). Statistics of natural images: Scaling in the woods. Phys. Rev. Lett., 73(6), 814--817. doi:https://link.aps.org/doi/10.1103/PhysRevLett.73.814 DOI: https://doi.org/10.1103/PhysRevLett.73.814
Rutten, D. (2013). Galapagos: On the Logic and Limitations of Generic Solvers. Architectural Design, 83(2), 132-135. doi:10.1002/ad.1568 DOI: https://doi.org/10.1002/ad.1568
Sato, J. (2010). Jun Sato: Items in Jun Sato Structural Engineers. Tokyo, Japan: INAX Publishing.
Schaaf, A. v., & Hateren, J. v. (1996). Modelling the Power Spectra of Natural Images: Statistics and Information. Vision Research, 36(17), 2759-2770. doi:https://doi.org/10.1016/0042-6989(96)00002-8. DOI: https://doi.org/10.1016/0042-6989(96)00002-8
Simoncelli, E. P., & Olshausen, B. A. (2001). Natural image statistics and neural representation. Annu. Rev. Neurosci., 24, 1193-1216. doi:10.1146/annurev.neuro.24.1.1193. PMID: 11520932 DOI: https://doi.org/10.1146/annurev.neuro.24.1.1193
Spehar, B., & Taylor, R. (2013). Fractals in Art and Nature: Why do we like them? Proceedings of SPIE - The International Society for Optical Engineering. doi:10.1117/12.2012076 DOI: https://doi.org/10.1117/12.2012076
Spehar, B., Clifford, C. W., Newell, B. R., & Taylor, R. P. (2003). Universal aesthetic of fractals. Computers & Graphics, 27(5), 813–820. doi:https://doi.org/10.1016/S0097-8493(03)00154-7. DOI: https://doi.org/10.1016/S0097-8493(03)00154-7
Stals, A., Jancart, S., & Elsen, C. (2016). How Do Small and Medium Architectural Firms Deal with Architectural Complexity? A Look Into Digital Practices. eCAADe 2016: Complexity & Simplicity, (pp. 159-168). Oulu, Finland. doi:10.52842/conf.ecaade.2016.2.159 DOI: https://doi.org/10.52842/conf.ecaade.2016.2.159
Szendrő, P., Vincze, G., & Szasz, A. (2001). Pink-noise behaviour of biosystems. European Biophysics Journal, 30, 227-231. doi:https://doi.org/10.1007/s002490100143 DOI: https://doi.org/10.1007/s002490100143
Tabadkani, A., Roetzel, A., Li, H. X., & Tsangrassoulis, A. (2021). Daylight in Buildings and Visual Comfort Evaluation: the Advantages and Limitations. Journal of Daylighting, 8, 181-203. doi:10.15627/jd.2021.16 DOI: https://doi.org/10.15627/jd.2021.16
Tolhurst, D., Tadmor, Y., & Chao, T. (1992). Amplitude spectra of natural images. Ophthalmic and Physiological Optics, 12, 229-232. doi:10.1111/j.1475-1313.1992.tb00296.x DOI: https://doi.org/10.1111/j.1475-1313.1992.tb00296.x
Torralba, A., & Oliva, A. (2003). Statistics of natural image categories. Network: Computation in Neural Systems, 391-412. doi:10.1088/0954-898X_14_3_302 DOI: https://doi.org/10.1088/0954-898X_14_3_302
Ulrich, R. S. (1983). Aesthetic and affective response to natural environment. In Behavior and the natural environment (pp. 85–125). New York: Plenum Press. doi:10.1007/978-1-4613-3539-9_4 DOI: https://doi.org/10.1007/978-1-4613-3539-9_4
Verbeeck, K. (2006). Randomness as a Generative Principle in Art and Architecture. Massachusetts Institute of Technology. Retrieved 5 20, 2023, from https://dspace.mit.edu/handle/1721.1/35124
Vierlinger, R., & Hofmann, A. (2013). A Framework for Flexible Search and Optimization in Parametric Design. Design Modelling Symposium. Berlin. doi:10.13140/RG.2.1.1516.8727
Vincent, J. (2009). Biomimetic patterns in architectural design. Architectural Design, 74-81. doi:10.1002/ad.982 DOI: https://doi.org/10.1002/ad.982
Wienold, J., & Christoffersen, J. (2006). Evaluation methods and development of a new glare prediction model for daylight environments with the use of CCD cameras. Energy and Buildings, 38, 743-757. doi:10.1016/j.enbuild.2006.03.017. DOI: https://doi.org/10.1016/j.enbuild.2006.03.017
Wortmann, T., & Nannicini, G. (2016). Black-Box Optimisation Methods for Architectural Design. CAADRIA 2016: Living Systems and Micro-Utopias - Towards Continuous Designing, (pp. 177-186). Melbourne, Australia. doi:10.52842/conf.caadria.2016.177 DOI: https://doi.org/10.52842/conf.caadria.2016.177
Zitzler, E. (1999). Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. Zurich: Swiss Federal Institute of Technology.
Zitzler, E., & Thiele, L. (1998). An Evolutionary Algorithm for Multiobjective Optimization: The Strength Pareto Approach. Zurich: Swiss Federal Institute of Technology.