Cuando una empresa con Programa IMMEX en la modalidad de servicios o una persona que cuente con autorización para destinar

The Smart Structures Project investigates multi-scale optimisation and additive manufac- turing of non-standard structures using a set of digital tools, comprising of Typogenetic Design and structural optimisation. A set of physical and virtual prototype was developed during the exploration of different node geometries, which contribute parts to the multi- staged design model. Structural optimisation was used to develop a steel truss model suit- able as design scenario for testing the interactive mass-customisation of structural nodes in facade application. Figure5.23shows the structural layout, parametric truss and loca- tion of the node designed using Typogenetic Design. The interactive design of structural

Figure 5.23: Facade structure as parametric truss as design scenario

nodes was used to evolve the shape of a structural node as proof-of-concept. In another step a fixed topology was introduced with angles of the interior structure limited to 45 degrees. This fixed topology resembles the shape of the node design, while reducing ma- terial deposition during 3D printing to limited areas. The interactive mass-customisation application could be further improved by integrating some of the node design options developed during the Smart Structures Project.

The efficient design-to-manufacturing process for structural components extended the potential of intuitive exploration for the designer, and led to increased design freedom while generating nodes of organic shapes. The refinement of the node design for 3D printing led to an understanding of the design model as multi-staged and multi-scale optimisation process that consists of three separate stages that were explored independently. The three stages defined for the Smart Structures Project are: (a) structural optimisation using member-sizing, (b) interactive node design using the prototype of the Typogenetic Design tool and (c) a micro structure for which two different approaches were developed. Dividing the decision making process for structural systems into segments reduced computational costs during the early stages of the design. Different levels of scale have specific com- putational needs, and computational costs increase toward higher resolution of geometry. During design space exploration, computational resources were focused on the generation of shape variations. Tectonic articulation of structural nodes using different structural optimisation techniques allowed me to successively differentiate the node geometry during different stages of node design. Continually increasing the use of computational resources after creative decision making about the shape of the structural nodes was finished allowed the designer to explore design solutions selectively at different levels of detail. A diagram- matic illustration of a multi-staged model for interactive structural design is shown in Figure5.24.

The use of intelligent systems allowed for semi-automated exploration of design spaces for structural nodes. The scenario map in Figure 5.25 shows the design process using Typogenetic Design. After choosing a reference image for the Shape Comparison, the

Figure 5.24: Multi-staged model for interactive node design

evolutionary search over 25 generations with an initial population of 20 different design due to hardware constraints. Different organic shapes suitable for the design scenario were explored. The screen captures of the different interfaces show the shape development, while the choices of the designer are highlighted with pink dashed boxes. After finishing the initial design spaces exploration, the geometry was refined for 3D printing.

During refinement of the node geometry, a fixed typology was introduced based on a set of points populating the initial geometry and an internal structure that was generated based on a point grid. The created line topology was translated into a mesh by using marching cube algorithm with a homogeneous weighting. Additional optimisation of the material used for particular members of the resulting node design could be facilitated by applying the BESO (bi-directional structural optimisation) and member-sizing of fixed topology approach discussed below. Finally, the designed node was merged with brackets for mounting on the steel truss. The different stages of the geometric refinement are illustrated in Figure5.26.

Figure 5.26: Refinement Smart Structures node after morphological search

During another stage of the Smart Structures Project, a BESO optimisation and member-sizing of fixed topology approach was investigated and digital tools developed to test the design process. An initial fixed topology was optimised by discrete BESO optimisation that removes the least stressed members of the node structure. After reducing the amount of struts in the node structure, a member-sizing algorithm was used to specify the thickness of each of the members in the node structure. In the next procedure, thin branches were pruned from the node design to avoid failure during 3D printing. A physical prototype was 3D printed to test the generated geometry. The successive steps of this process are shown in Figure5.27.

Figure 5.27: BESO and member-sizing of fixed topology

In the third stage of the Smart Structure Project, a node design using principal stress lines was produced and tested to explore the design potential for micro structures of structural nodes. A mesh geometry was designed and analysed using structural simulation in Karamba3D plug-in for Grasshopper in Rhinoceros CAD. This plug-in was used in all applications of structural simulation during the Smart Structures Project. The delicate appeal of the node design illustrates the potential of 3D printing to generate intricate aesthetic articulation. While the product shown as virtual and physical prototype in Figure 5.28 was not suited for structural application, a combination with the process for generating an internal structure described above could be used to strengthen the node design for use as bespoke structural component.

The next iteration of the investigation revealed the potential for elimination of a separate stage for the formation of a micro structure by refining the internal structure tested in the refinement of nodes generated by Typogenetic Design. A high resolution of the micro structure in combination with a population of the mesh geometry with points in a suitable density for 3D Delaunay triangulation also unveiled a highly detailed an complex geometrical outcome. The result of this exploration captured as overview and close-up is reported in Figure 5.29b. During the development of this node design, the BESO and member-sizing of fixed topology approach was applied to the emerging micro-structure to

Figure 5.28: Node design exploration using principal stress lines

position the material exactly where it was needed from a structural perspective.

(a) Virtual prototype of final node design (b) Micro structure of virtual prototype

Figure 5.29: Smart Structures node design

Custom-Optimisation of Node Design The final node design was based on a long trajectory of investigations in custom-optimisation of structural nodes at RMIT Univer- sity. The strategy for generation of the structural node shown in Figure 5.29a combines BESO optimisation of structural nodes [Crolla and Williams 2014], [Prohasky et al. 2015], [Seifi et al. 2016], [Seifi et al. 2018], [Williams et al. 2015] with an extension of the fixed

topology approach discovered during the last iteration of the Smart Nodes Project in the UABB Pavilion [Crolla et al. 2017]. The fixed-topology is again optimised by discrete BESO optimisation and member-sizing on the micro structure. The generation of the micro structure based on a rectangular 3D-grid, as illustrated in Figure 5.29b, addressed fabrication constraints for 3D printing by fixing the internal angle at 45 degrees.

Multi-Staged and Multi-Scale Optimisation Smart Structures used a hybrid opti- misation employing parametric truss optimisation for the adaptation of the global struc- tural configuration and Typogenetic Design for the shape design of structural nodes. On the micro scale of node design, a fixed topology is introduced to address fabrication con- straints. Again, BESO and member-sizing of fixed topology approach was used to locally differentiate the micro structure. The operation of Smart Structures on different scales ex- tended the multi-scale optimisation approach developed during the Smart Nodes Project by integrating a micro structure into the node design process. Segmentation of the design process into distinct stages allowed the designer to focus computational capabilities on one process at a time. Therefore, a higher resolution of the node geometry was achieved. Bringing together the aspects mentioned before, the process envisions integrating aes- thetic evaluation aimed to incorporate material properties and fabrication considerations into the design process in early stages of architectural design. Then, the computational constraints for the generation of a 3D printable geometry of the final node hindered the fabrication of a physical prototype. Also a use of Typogenetic Design to generate dif- ferent geometries with incorporated interior structure was not possible due to hardware restrictions. The integration of structural simulation in Typogenetic Design at that time could not be facilitated, because a comprehensive structural simulation for RhinoPython is not available. Attempts to script Karamba plug-in for structural simulation using an external reference to Grasshopper did not lead to the expected assembly of a structural model. The software implementation of Karamba3D plug-in did not facilitate an inter- face to run structural simulation remotely from RhinoPython, because the Goo-objects 3

generated by other Grasshopper components were not accepted as valid input. The step towards integrating structural simulation in Typogenetic Design could be facilitated using a parametric Typogenetic Design tool as specified in Appendix B.

Preliminary Conclusions Combination of complex structural systems using truss op- timisation with intricate node detailing in a staged procedure allowed designers to in- teractively mass-customise structural nodes with interesting aesthetic attributes. Often, structural performance is the single decision variable in architectural optimisation, despite the existence of a variety of other factors which influence the design of building structures. Integration of aesthetic criteria allowed the designer to explore novel structural solutions

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Goo-objects are native data types used by Grasshopper. Karamba3D was implemented using custom data types that are not compatible with goo-objects outside of the resilient Grasshopper interface.

with a reference to the design input provided. Complexity in design has been changing as result of the development of new design strategies, inspired by other fields of creative de- sign production, as well as the introduction of novel technologies into the building process [Achten 2003]. Smart Structures was a step on the path to control increasing complexity enabled by current technologies of additive manufacturing, creative decision support and machine learning.

The chosen design strategy incorporated multi-staged and multi-scale modeling in hybrid systems for structural optimisation. This strategy focused on continuous differen- tiation of the structural system to minimise material use and increase the delicacy of the resulting custom-optimised nodes. Automation of fabrication of structural components to establish a complete digital process chain using multi-scale structural optimisation could increase efficiency by reduction of raw material use, as well as the level of skilled manual labour needed for the additive manufacturing of structural steel components and their assembly on site. The generation of 3D printable node geometries by using the set of tools compiled during Smart Structures Project could also reduce the amount of shop drawing necessary to fabricate structural nodes.

Integrating multiple stages of material reduction by structural optimisation led to intricate results that integrated continuously changing thicknesses of materials into smooth shapes with an organic character. Edmund Burke argues in his book ‘A Philosophical Inquiry Into The Origin Of Our Ideas Of The Sublime And Beautiful’ that delicacy is a contributing factor to the aesthetics of the human body Burke [1914, p. 102, 104, 146]. Burke’s enquiry was aimed at achieving a more general understanding of beauty, and involved reflecting on terms like “sublime”, “smoothness”, “gradual variation” and “delicacy of form”DeZurko [1952, p. 107]4.

The Smart Structures Project closes the circle of my creative work during this PhD study by applying Typogenetic Design to a case study comparable with those that mo- tivated me to experiment with constructing a generative interactive system in the first place. Next, I want to direct the attention of the reader to the reflective processes that took place iteratively during the research process.

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Horatio Greenough rejected those terms as relative observations in his own theoretical work. DeZurko [1952, p. 107] Nevertheless, I argue for delicacy as a sign of beauty along the lines of Burke, who considers it a trait of elegance and fragility, and refers to natural metaphors like the human body and treesBurke [1914, p. 102], which resonates well with the understanding of proportions and beauty in architectural theory.

CHAPTER

6

Reflections

“Thinking begins only when we have come to know that reason, glorified for centuries, is the stiff-necked adversary of thought.”

–Martin Heidegger

6.1

Self-Organisation and Autopoiesis

In interactive evolutionary search, the designer actively selects solutions based on her or his design preferences. The evolutionary search needs to adapt to the external selection pressure of human interaction, augmented by the machine learning system, for which it constantly builds knowledge about the shape progression. No heuristics or a priori knowledge were incorporated in Typogenetic Design. While heuristics for the aesthetic considerations in design may be integral to an evolutionary system [Fernando et al. 2010], the integration of learning algorithms facilitated the adaptation during the iterative search and generated a genetic drift that reflected the designer’s subjective choices. An example of the shape progression during application of Typogenetic Design to Anguinum Project is displayed in6.1.

The simultaneous evaluation by the designer and Artificial Intelligence (AI) creates a variety of dynamics and drifts inside the autopoietic system1. Manuel DeLanda suggests

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The autopoietic unity formed by the phenomena specified and explored during the design and testing of Typogenetic Design define a specific phenomenology, as described by Humberto Maturana and Fran- cisco Varela inMaturana and Varela[1987, p. 22]. As an autopoietic system, Typogenetic Design exhibits emergent properties based on the relationships between evolutionary search, architectural optimisation and Human-Computer-Interaction (HCI). The emergent properties of the evolutionary process and the emergent representations used in generative design are taken for granted in studying the symbiotic re- lationship between designer and AI. The formation changes in emergent solutions therefore extend the notion of “changes caused by the cell’s own structure as a unity”Maturana and Varela[1987, p. 22]. This metaphor though can be used to talk about the generative synthesis of design solutions using emergent representations.

Figure 6.1: Shape progression during Typogenetic Design application for Anguinum Project

that “there is more to the emergent properties of a whole than the interactions of its parts”DeLanda [2011, p. 12]. The main features of Typogenetic Design, however, draw their knowledge from different sources, namely knowledge preserving classifiers. On the one side, the image input used as an external reference provides a focus point during the current run of the system, while the Shape Comparison, as part of the aesthetic guidance, introduces an own dynamic. On the other hand, the Online Classification as an interactive approach allows the design system to adapt to designer choices, which changes continually during the real-time adaptation to the designer selection of architectural shapes.

The abstract representation of architectural geometry using schema, rules and con- straints defines a conceptual space in which design solutions can evolve as autonomous elements. More features could be integrated to introduce references to typology. An approach that reflects those thoughts was explored during the deconstruction and recon- struction of architectural typology during Evotype/Reverse Workshop (AppendixE) and Evolutionary House Design (AppendixF). The reproductive generation of novel solutions based on evolutionary search displays drifting behaviour similar to the ontogeny’s develop- ment of species in evolution. Genetic information is translated into architectural shapes based on the genotype/phenotype relationship of grammar translation. The ecological

niche those architectural ‘creatures’ adapt to is defined by design-based metrics (criteria) such as area, volume and mass or structural simulation. These mechanisms define the structure of a solution space during shape generation. The conceptual deconstruction and reconstruction of typology in Evotype/Reverse Workshop (AppendixE) and Evolutionary House Design (Appendix F) revealed the potential for exploration of typological spaces by Typogenetic Design.

In addition, an environment for the development of design solutions needed to be defined. Restrictions of design space and non-design space - for instance constraints on dimensions and spaces that can be occupied by potential design solutions - define the digital environment for design solutions. Thus, I specified the environment to foster dis- tinctness of design solutions by providing a niche for the development of the architectural shapes inside the design system. The drift caused by adaptation of the evolutionary search to an environmental niche constitutes a selection mechanism performed by the environ- ment Maturana and Varela[1987, p. 103].

The development of architectural features and their preservation by conservation needs to be conceptually traded-off against the agility of the evolutionary system to adapt to the designer’s changes of mind. The genetic representation used in Typogenetic Design is a sufficient description of architectural shape to allow selection of specific features to emerge from the evolutionary search. Additional mechanisms need to be developed to preserve specific features during evolutionary search for architectural shape generation.