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Art
Architecture

Data-driven architectural design to production and operation

Profile image of Sina MostafaviSina Mostafavi

2015

https://doi.org/10.17101/SYSTEMA.V3I2.310
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8 pages

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Abstract

Data-driven architectural production and operation explored within Hyperbody rely heavily on system thinking implying that all parts of a system are to be understood in relation to each other. These relations are established bi-directionally so that data-driven architecture is not only produced (designed and fabricated) by digital means but also incorporates digital, sensing-actuating mechanisms that ena-ble real-time interaction between (natural or artificial) environments and users. Data-driven architectural production and operation exploit, in this context, the generative potential of process-oriented approach-es wherein interactions between (human and non-human) agents and their (virtual and physical) envi-ronments have emergent properties that enable proliferation of hybrid architectural systems and ecolo-gies.

Figures (5)
Figure 1: Physical change and variation explored within Hyperbody (2010) In contrast to mechanical reproduction’ contemporary digital production implies inter al. that the work of art and architecture become digital addressing, therefore, principles* such as data-driven representation, generation, production, and operation. Thus, as more recently convincingly argued by Carpo®, the historical understanding of buildings as physically built identical replicas of architectural intent (formalized in design) which eventually in the 20th century became serially, mass produced identical copies, is challenged by the contemporary data-driven parametric multiplicity and variation. Such multiplicity and variation allowing versions of architectural intent to be virtually or physically implemented and experienced through inter al. spatial reconfiguration has been explored within Hyperbody (fig. 1) with the understanding that multiple versions of the built space may be achieved through kinetic transformation. understanding that multiple versions of the built space may be achieved through kinetic Furthermore, experimentation with parametric multiplicity and variation has been addressec with focus on generative design and data-driven production, which were explored to criticall reveal what these techniques may offer architectural production as well as outline wha challenges remain in their application. The relationship between generative processes anc data-driven production has been focus of current architectural research and practice largel\ due to the phenomenon of emergence explored inter al. within self-organization, which i defined as a process, in which the organization of a system emerges bottom-up’ from the interaction of its components. Self-organizing swarms? for instance, are employed in genera tive design processes, which deal with ample amounts of data featuring sometime conflict ing attributes and characteristics. Those attributes and characteristics are incorporated ir behaviors according to which design components such as programmatic units swarm to wards locally optimal spatial configurations®. In this context, architectural design become: procedural instead of object-oriented and architectural form emerges in a process in whicl the interaction between all parts of the system generate the result. Thus, the architect be comes the designer of the process and only indirectly of the result. Furthermore, experimentation with parametric multiplicity and variation has been addressed century became serially, mass produced identical copies, is challenged by the contemporary
Figure 1: Physical change and variation explored within Hyperbody (2010) In contrast to mechanical reproduction’ contemporary digital production implies inter al. that the work of art and architecture become digital addressing, therefore, principles* such as data-driven representation, generation, production, and operation. Thus, as more recently convincingly argued by Carpo®, the historical understanding of buildings as physically built identical replicas of architectural intent (formalized in design) which eventually in the 20th century became serially, mass produced identical copies, is challenged by the contemporary data-driven parametric multiplicity and variation. Such multiplicity and variation allowing versions of architectural intent to be virtually or physically implemented and experienced through inter al. spatial reconfiguration has been explored within Hyperbody (fig. 1) with the understanding that multiple versions of the built space may be achieved through kinetic transformation. understanding that multiple versions of the built space may be achieved through kinetic Furthermore, experimentation with parametric multiplicity and variation has been addressec with focus on generative design and data-driven production, which were explored to criticall reveal what these techniques may offer architectural production as well as outline wha challenges remain in their application. The relationship between generative processes anc data-driven production has been focus of current architectural research and practice largel\ due to the phenomenon of emergence explored inter al. within self-organization, which i defined as a process, in which the organization of a system emerges bottom-up’ from the interaction of its components. Self-organizing swarms? for instance, are employed in genera tive design processes, which deal with ample amounts of data featuring sometime conflict ing attributes and characteristics. Those attributes and characteristics are incorporated ir behaviors according to which design components such as programmatic units swarm to wards locally optimal spatial configurations®. In this context, architectural design become: procedural instead of object-oriented and architectural form emerges in a process in whicl the interaction between all parts of the system generate the result. Thus, the architect be comes the designer of the process and only indirectly of the result. Furthermore, experimentation with parametric multiplicity and variation has been addressed century became serially, mass produced identical copies, is challenged by the contemporary
ciations between them. Figure 2: Generative design explorations developed with MSc 3 students (2013) by informing the point cloud with multiple simulations (in this case solar radiation for different seasons) humans, whereas agency is not located in one or the other, but in the heterogeneous asso-
ciations between them. Figure 2: Generative design explorations developed with MSc 3 students (2013) by informing the point cloud with multiple simulations (in this case solar radiation for different seasons) humans, whereas agency is not located in one or the other, but in the heterogeneous asso-
Figure 3: Diagrammatic snapshots illustrating generative systems within the informed point cloud (2013) studies space and transformation. In this context, the architect employs artificial agents that produce populations of architec-
Figure 3: Diagrammatic snapshots illustrating generative systems within the informed point cloud (2013) studies space and transformation. In this context, the architect employs artificial agents that produce populations of architec-
holistic computational design system. Eco-Locator (fig. 2-5) has developed a twofold climatic strategy focused on Computational Fluid Dynamics (CFD) for airflow simulation and solar radiation including on site daylight access simulations. The goal was to provide data sets that would inform the generative system. In this case, the generative system implies a recursive search evaluating site condi- tion and generating rough configurations for the pavilion, its structure as well as its circula- tion system in relation to specific clusters of program. Eco-Locator (fig. 2-5) has developed a twofold climatic strategy focused on Computational
holistic computational design system. Eco-Locator (fig. 2-5) has developed a twofold climatic strategy focused on Computational Fluid Dynamics (CFD) for airflow simulation and solar radiation including on site daylight access simulations. The goal was to provide data sets that would inform the generative system. In this case, the generative system implies a recursive search evaluating site condi- tion and generating rough configurations for the pavilion, its structure as well as its circula- tion system in relation to specific clusters of program. Eco-Locator (fig. 2-5) has developed a twofold climatic strategy focused on Computational
Figure 5: Renders illustrating spatial and morphological quality of the eco-locator project (2013) design interventions into the optimization process.
Figure 5: Renders illustrating spatial and morphological quality of the eco-locator project (2013) design interventions into the optimization process.

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  26. Mayr, E. (1999). Darwin's Influence on Modern Thought. Retrieved February 10, 2013, from http://www.biologie.uni-hamburg.de/b-online/e36_2/darwin_influence.htm About the Authors Henriette Bier After graduating in architecture (1998) from the University of Karlsruhe in Germany, Henriette Bier has worked with Morphosis (1999-2001) and ONL (2004) on internationally relevant projects in the US and EU. She has taught data-driven design (2002-2003) at universities in Austria, Germany, Belgium and the Netherlands and since 2004 she teaches and researches at Technical University Delft (TUD) with focus on data-driven architecture. She initiated and coordinated (2005-06) the workshop and lecture series on Digital Design and Fabrication with invited guests from MIT and ETHZ and finalized (2008) her PhD on System-embedded Intelligence in Architecture. She coordinated EU projects (2008-10) and since 2003 she regularly lectured and led workshops in EU and US. Results of her research have been internationally published in books, journals, and conference proceedings. Sina Mostafavi Sina Mostafavi is a practicing architect, PhD-candidate, and expert in computational design. He re- searches and teaches at the Delft University of Technology with focus on computational design meth- odologies and technologies that facilitate integrated, performance driven design processes from con- ception to production. He practiced architecture since 2007 and is founding partner, co-owner, and lead designer of TAO (Transformative Architecture Office), a firm that offers consultancy services for innovative design solutions and state-of-the-art construction processes. He holds a master degree in architecture from University of Tehran and a bachelor degree from Tehran University of Art. He is cur- rently project researcher in Robotic Building project at Hyperbody group at TU Delft.

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