Diseño biodigital e inteligencia artificial. Procesos y soluciones innovadoras en la arquitectura contemporánea

Abstract

Biodigital design and artificial intelligence are two disciplines that offer a promising approach to creating innovative solutions for building a sustainable future. The convergence of the two disciplines seeks to develop living architecture, which mimics biological thinking, with the aim of producing autonomous and sustainable systems. Buildings generated through this approach can obtain energy through photosynthesis, regulate their temperature autonomously, purify indoor air and optimise their performance, thus reducing their environmental impact. This work aims to explore processes that seek the integration of biodigital design and artificial intelligence to produce innovative and sustainable results in the field of design. Through the analysis of cases, it examines the advantages and disadvantages of the union of biodigital design and AI, considering both its environmental and social effect. This article offers a new perspective on the interaction between the two disciplines, highlighting their potential to transform today's architecture for a liveable and environmentally friendly future.

Keywords:

bioinspired design computational processes energy optimization evolutionary algorithms sustainable architecture

References

Alawadhi, M., & Yan, W. (2021). BIM hyperreality: Data synthesis using BIM and hyperrealistic rendering for deep learning. arXiv:2105.04103. https://doi.org/10.48550/arXiv.2105.04103

As, I., Pal, S., & Basu, P. (2018). Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing, 16(4), 306-327. https://doi.org/10.1177/1478077118800982

Bagley, J. (1967). The behavior of adaptive systems which employ genetic and correlation algorithms. The University of Michigan. https://deepblue.lib.umich.edu/handle/2027.42/3354

Benyus, J. M., & Leal, A. G. (2012). Biomímesis: Innovaciones inspiradas por la naturaleza. Tusquets Editores S. A.

Bremermann, H. J. (1962). Optimization through evolution and recombination. Self-organizing systems, 93, 106. https://holtz.org/Library/Natural%20Science/Physics/Optimization%20Through%20Evolution%20and%20Recombination%20-%20Bremermann%201962.htm

Caetano, I., Santos, L., & Leitão, A. (2020). Computational design in architecture: Defining parametric, generative, and algorithmic design. Frontiers of Architectural Research, 9, 287-300. https://doi.org/10.1016/j.foar.2019.12.008

Cerrolaza, M., & Annicchiarico, W. (1996). Algoritmos de optimización estructural basados en simulación genética. Universidad Central de Venezuela.

Dartnell, L. (2007). Matrix: Simulating the world Part I - Particle models. +Plus Magazine. https://plus.maths.org/content/matrix-simulating-world-part-i-particle-models

Dartnell, L. (2012). Matrix: Simulating the world Part II: Cellular automata. +Plus Magazine. http://plus.maths.org/content/matrix-simulating-world-part-ii-cellular-automata

Del Campo, M., & Leach, N. (2022). Can machines hallucinate architecture? AI as design method. Archit. Design, 92, 6-13. https://doi.org/10.1002/ad.2807

Díaz Moreno, C., & García Grinda, E. (2009). Atmósfera, material del jardinero digital. En I. Ábalos (Ed.), Naturaleza y artificio. El ideal pintoresco en la arquitectura y el paisaje contemporáneo (pp. 24-33). Gustavo Gili.

Estévez, A., & Abdallah, Y. (2022). AI to matter-reality. Art, architecture & design. iBAG. UIC Barcelona.

Fogel, L. J., Owens, A. J., & Walsh, M. J. (1966). Artificial intelligence through simulated evolution. Wiley.

Fraile Narváez, M. (2019). Arquitectura biodigital. Diseño Editorial.

Fraser, A. S. (1957). Simulation of genetic systems by automatic digital computers. Australian Journal of Biological Sciences, 10, 484-491.

http://dx.doi.org/10.1071/BI9570484

Holland, J. H. (Ed.). (1992). Index. En Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence (p. 0). The MIT Press. https://doi.org/10.7551/mitpress/1090.003.0016

Ito, T. (2009). Tarzanes en el bosque de los medios. En I. Ábalos (Ed.), Naturaleza y artificio. El ideal pintoresco en la arquitectura y el paisaje contemporáneo (pp. 121-123). Gustavo Gili.

Montaner, J. M. (2015). La condición contemporánea de la arquitectura. Gustavo Gili.

Rechenberg, I. (1973). Evolutionsstrategie. Optimierung technischer systeme nach prinzipien derbiologischen evolution. S. d.

Shen, S., Clerckx, B., & Murch, R. (2022). Modeling and architecture design of reconfigurable intelligent surfaces using scattering parameter network analysis. IEEE Transactions on Wireless Communications, 21(2), 1229-1240. https://doi.org/10.1109/TWC.2021.3103256

Sommese, F., Hosseini, S. M., Badarnah, L., Capozzi, F., Giordano, S., & Ambrogi, V. (2023). Light-responsive kinetic façade system inspired by the Gazania flower: A biomimetic approach in parametric design for daylighting. Building and Environment, 247, 111052. https://doi.org/10.1016/j.buildenv.2023.111052

Tolmos Rodríguez-Piñero, P. (2003). Introducción a los algoritmos genéticos y sus aplicaciones. Universidad Rey Juan Carlos. https://dialnet.unirioja.es/servlet/libro?codigo=185891

Wagensberg, J. (2013). La rebelión de las formas. Tusquets Editores S. A.

Zhang, R., Wang, L., Guo, Z., Wang, Y., Gao, P., Li, H., & Shi, J. (2023). Parameter is not all you need: starting from non-parametric networks for 3D point cloud analysis. arXiv:2303.08134. https://arxiv.org/abs/2303.08134

Zuo, W., Chen, M.-T., Chen, Y., Zhao, O., & Cheng, B. (2023). Additive manufacturing oriented parametric topology optimization design and numerical analysis of steel joints in gridshell structures. Thin-Walled Structures, 188, 110817. https://doi.org/10.1016/j.tws.2023.110817

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