Interscalable material microstructure organization in performance-based computational design

Abstract

Various parameters can be integrated in material-based computational design in architecture. Materials are the main driver of these processes and evaluated with the constraints related to the form, performance, and fabrication techniques. However, current methodologies mostly involve investigating already existing materials. Studies on computational material design, in which new materials are developed by designing their microstructures in response to the performative issues, are generally undertaken at the material scale, and not adopted to the architectural design process yet. To resolve this issue, the methodology titled Interscalable Material Microstructure Organization in Performance-based Computational Design (I2MO_PCD) is developed and presented in three stages, including (1) identification of different types of material microstructures, (2) computational material design, and (3) prototyping. Data-based material modelling and visualization, and algorithmic modelling techniques are utilized, followed by various performance simulations as a part of an iterative process. New microstructure organizations are designed computationally, organized under three main groups as linear-curvilinear, crystal and metaball-voronoi. The outcomes of different performance analyses, including structure, radiation, direct sun hours, acoustics and thermal bridge were compared. Thus, the role of geometrical organization of microstructures, scales and material types in different performance computations were identified, by designing and fabricating synthetic materials.

Keywords

Material microstructure / Computational design / Performance computation / Architectural design

References

[1] Ashby, M.,F., 1999. Material Selection in Mechanical Design. Butterworth-Heinemann, Oxford, UK.

[2] Bechthold, M., Weaver, J.C., 2017. Materials science and architecture. Nat. Rev. Mater. 2, 17082.

[3] Boddeti, N., Rosen, D.,W., Maute, K., Dunn, M.,L., 2019. Multiscale optimal design and fabrication of laminated composites. Compos. Struct. 228, 111366.

[4] Bostanabad, R., Zhang, Y., Li, X., Kearney, T., Brinson, L.,C., Apley, D.,W., Liu, W.,K., Chen, W., 2018. Computational microstructure characterization and reconstruction: review of the state-of-the-art techniques. Prog. Mater. Sci. 95, 1- 41.

[5] Brown, N.C., Mueller, C.T., 2019. Design variable analysis and generation for performance based parametric modeling in architecture. Int. J. Architect. Comput. 17 (1), 36- 52.

[6] Cang, R., Xu, Y., Chen, S., Liu, Y., Jiao, Y., Ren, M.Y., 2017. Microstructure representation and reconstruction of heterogeneous materials via deep belief network for computational material design. J. Mech. Des. 139, 071404.

[7] Castelo-Branco, R., Caetano, I., Leitao, A., 2022. Digital representation methods: the case of algorithmic design. Frontiers of Architectural Research 11, 527- 541.

[8] Chatzikonstantinou, I., Sariyildiz, S., 2016. Approximation of simulation-derived visual comfort indicators in office spaces: a comparative study in machine learning. Architect. Sci. Rev. 59 (4), 307- 322.

[9] Chen, Z., Wang, K., Giuliani, F., Atkinson, A., 2015. Microstructural characteristics and elastic modulus of porous solids. Acta Mater. 89, 268- 277.

[10] Datta, S., Roy, B., Davim, J.P., 2016. Computational materials design: different concepts and aspects. In: Datta, S., Davim, J. (Eds.), Computational Approaches to Materials Design: Theoretical and Practical Aspects, vol. 1, pp. 1-12. P., chapter.

[11] Doskar, M., Zeman, J., Rypl, D., Novak, J., 2020. Level-set based design of Wang tiles for modelling complex microstructures. Comput. Aided Des. 123, 102827.

[12] Gramazio, F., Kohler, M., 2008. Digital Materiality in Architecture. Lars Müller Publishers, Baden, Switzerland.

[13] Groeber, M.A., Jackson, M.A., 2014. DREAM. 3D: a digital representation environment for the analysis of microstructure in 3D. IntegratingMaterials and Manufacturing Innovation 3, 5.

[14] Hensen, J.L.M. ve Lamberts, R., 2011. Building Performance Simulation for Design and Operation. Spon Press, Abingdon-UK.

[15] Kolarevich, B., ve Klinger, K., 2008. Manufacturing Material Effects Rethinking Design and Making Architecture. Routledge, New York, pp. 196-198.

[16] Ma, C., Zhang, Z., Luce, B., Pusateri, S., Xie, B., Rafiei, M.,H., Hu, N., 2020. Accelerated design and characterization of nonuniform cellular materials via a machine-learning based framework. npj Comput. Mater. 6, 40.

[17] Mahdavi, A., 1999. A comprehensive computational environment for performance based reasoning in building design and evaluation. Autom. ConStruct. 8, 427- 435.

[18] McDowell, D.L., Pancal, J.H., Choi, H., Seepersad, C.C., Allen, J.K., ve Mistree, F., 2010. Integrated Design of Multiscale Multifunctional Materials and Products. Elsevier, Burlington-MA.

[19] Menges, A., 2016. Computational Material Culture, Architectural Design, pp. 76-83.

[20] Oxman, N., 2010. Material-based Design Computation (PhD) Thesis. Massachusetts Institute of Technology.

[21] Stieler, D., Schwinn, T., Leder, S., Maierhofer, M., Kannenberg, F., Menges, A., 2022. Agent-based modeling and simulation in architecture. Autom. ConStruct. 141, 104426.

[22] Takano, N., Zako, M., 2000. Integrated design of graded microstructures of heterogeneous materials. Archieve of Applied Mechanics 70, 585- 596.

[23] Tseranidis, S., Brown, N.C., Mueller, C.T., 2016. Data driven approximation algorithms for rapid performance evaluation and optimization of civil structures. Autom. ConStruct. 72, 279- 293.

[24] Yazici, S., Tanacan, L., 2018. A study towards interdisciplinary research: a material-based integrated computational design model (MICD-m) in architecture. Architect. Sci. Rev. 61 (Nos. 1-2), 68- 82.

[25] Yazici, S., Tanacan, L., 2020. Material-based computational design (MCD) in sustainable architecture. J. Build. Eng. 32 (November 2020) 101543.

[26] Zhang, S., Vijayavenkataraman, S., Lu, W.F., Fuh, J.Y.H., 2019. A review on the use of computational methods to characterize, design, and optimize tissue engineering scaffolds, with a potential in 3D printing fabrication. J. Biomed. Mater. Res. B Appl. Biomater. 107 b (issue 5), 1329- 1351.

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