During the second year of my PhD study, I embraced the invitation to apply my previously generated knowledge about parametric modelling, mass-customisation and structural op- timisation in a creative project on a furniture scale, initiated by Associate Professor Hank Hauesler and his team at University of New South Wales [Haeusler et al. 2017]. The in- tention of the project was to generate a modular furniture piece based on location-specific structural nodes by using a responsive parametric setup to generate all fabrication data directly from the integrated model. This description sounds very similar to the Smart Nodes Project discussed previously, and the technology used also builds on the results of this project. In contrast to the previous case study, the Sydney Opera Bar reduced the complexity of the structural node design by a magnitude to focus the exploration on the parametric link between global and local structure. This reduction allowed for the implementation of a responsive approach where all parts of the geometry - up to the initial design surface - could be modified or replaced without major adjustments of the scripts. An additional benefit was the integration of node design in one coherent paramet- ric model with the overall structural model, so that all the geometry needed to fabricate the Sydney Opera Bar Centre Piece, as displayed in Figure 5.7, was regenerated in re- sponse to geometrical changes at any stage of the parametric model. The use of a single integrated model is an innovation compared to the sequential application of design scripts, which was common practice in computational design until recently. The responsive na- ture of the parametric design definition introduced other challenges in the communication between domain experts.
The Sydney Opera Bar Centre Piece geometry was again defined as a curve-based topology applied to a design surface. The first step after importing the geometry involved
Figure 5.7: Sydney Opera Bar as feature during the Opera the Eighth Wonder
splitting the lattice geometry for the positioning of the structural nodes with a sphere. Those spheres were chosen based on the fabrication constraints of the desktop 3D printers that were used for the fabrication of the structural nodes. In the next module, the struc- tural simulation was conducted using Karamba3D as a plug-in for the Grasshopper visual programming editor in Rhinoceros CAD. Multiple load cases for live and dead load were considered and the member-sizes for the wooden struts fixed in reference to the material constraints. In succession, the structural model was analysed for the member-sizes asso- ciated to the node topologies. Those weights were used as input for the GD of structural nodes using ‘Exoskeleton’ plug-in as a generative engine. The resulting geometries were tagged with a labelling system to identify the position of the structural nodes in the overall system.
As a result of the design process, the table in Figure 5.7 was exhibited and used in the VIP Area of the Opera ‘The Eighth Wonder’. The design added an original design artefact to the spatial experience of the space as part of the show. The organic shapes of the structural nodes are a unique feature that I designed by using a custom digital tool, extending a preliminary design together with Narissa Bungbrakearti, Emily Leung and M. Hank Haeusler from the University of New South Wales. My own contribution to the project was the mass-customisation of the structural nodes using structural optimisation. The engineering of the design solution was conducted by Sascha Bohnenberger of Bollinger + Grohmann engineers at the Melbourne office. Everything else was handled by M. Hank Haeusler and his team in Sydney.
Multi-Disciplinary Communication Both the architects and engineers worked on a shared model during the process, thereby allowing each other to diverge and converge their thinking during the design exploration in response to the needs of the design state and previous interactions. While the architect benefited a lot from the flexibility of the parametric model to modify the architectural geometry, the engineer exported geometry into the explicit modelling space of the CAD software to define certain design states. A
Figure 5.8: Two stages of the optimisation process
re-import as reference geometry was used as the basis for the structural calculations and to advance the design during the next design state. Design iterations occurred on different levels of the parametric setup, and while the parametric model was shared, it was only explicitly used for design changes by the engineer to change the macro structure of the structural design. Here, the introduction of a structural symmetry improved the structural behaviour, and was realised by changing the design surface. The import mechanism for the explicitly modelled design surface allowed the designer to integrate those changes without breaking the parametric logic of the model.
Multi-Scale Structural Optimisation Reflecting on the initial results of the optimi- sation process presented in Figure 5.8 on the left, a macro-level optimisation was intro- duced, establishing a structural symmetry of the two half-shells and relaxing the node topology on the design surface to create an even distribution of the structural nodes. This step impacted strongly on the meeting of fabrication constraints while the node design op- timisation was mainly responsible for the generation of the unique expression of the table and the local reduction of the material needed for the structural nodes. This major step in the optimisation of the overall structural system introduced by Sascha Bohnenberger led to a multi-staged optimisation approach that works on different scales. The resulting geometry is given in Figure5.8on the right.
Implementation of optimisation processes for architectural design in one coherent model allows for major improvements in design coordination, code reuse and streamlined design by improved communication processes. Even working across different scales and with multiple stages of the optimisation process is possible. Fixing design states in explicit model representations is necessary to review and discuss specific artefacts. Here, the convergence is quite abrupt in parametric design, as the transformation into an explicit model breaks the model association and the responsive capabilities. Therefore, the initial definition of the kind of geometry that should be modified needed to be specified to establish a consistent geometric pipeline to avoid breaking the parametric logic. In this process, the different modes of thinking in engineering and architecture are evident in the intention of the engineer to optimise a single solution and the desire of the architect to explore a variety of design expressions.
Preliminary Conclusions Responsibilities and tasks were clearly defined early in the design process. As a result, the design team had a clear functional division. The design team leader was responsible for the allocation of workload to team members and for the establishment of clear communication protocols. In particular, the feedback cycles for design iterations have to be considered as interfaces between the different stages of the computational design process to allow team members to work efficiently and effectively. Managing those interfaces requires careful consideration of the aspects of the design that are kept in fluent motion and those aspects that can be crystallised as a basis for the design iterations during the next design state. In parametric collaborative design processes, the use of online tools like Flux or Modelo can provide enough information and geometry for the other designers to progress in the design process. Despite this, the collaborative work during optimisation-based architectural projects required a structured schema of the algorithmic steps to communicate the design process and a shared parametric model as collaborative design spaces. Dividing the design process into different stages with respective computational modules to separate the individual parts of the design introduces the potential to focus on different aspects of design in a constant frame of reference. Adding information to the coherent model in a modular fashion as in programming practice had to be managed through the design team leader or model manager throughout the process to identify and mediate conflicts emerging during code development.
In architectural optimisation currently criteria are generally limited to quantitative evaluation. Another characteristic of recent architectural optimisation is the focus on convergence rather than variation. This is problematic in the context of creative design in architecture, because architects and designers usually explore a variety of design solutions. As a result, architects and designers learn about the design case by evaluating the design solutions, which were explored. During this process knowledge about the design case is accumulated for the next design iterations. Critical reflection of my work in architectural optimisation also revealed that the conditions determine the outcome (e.g. solution or pareto front). Correspondingly, architectural design needs to be extended by interactive mechanisms that not only allow design space exploration, but also creative interaction with the generative process.
Toward integrated design space exploration The Sydney Opera Bar was the last case study that I used to explore architectural optimisation to propose software features, characteristics and mechanisms for extending the functionality. In the next step, I para- metrically tested the geometric representation to generate a larger set of variants using the same structural simulation and node customisation approach. I achieved larger freedom for geometric expression of the shape representation by omitting the host surface. This experiment and the generated shapes in Figure 5.9 revealed that if I want to use design tools for shape generation, I need to establish a set of rules and checking mechanisms to
constrain the design space to feasible solutions.
The exploration of design spaces as one of the core activities architects use to refine the initial project brief stands in contrast to the engineering approach that refines one particular solution to gain the best outcome in a goal-oriented design process. Constant negotiation between the two approaches leads to a design process that accommodates the needs of both professions. The chosen level of flexibility allows the designer to integrate both continuous improvement of the design and disruptive changes, as shown in the itera- tions of the design surface. These preliminary conclusions led to the urge for development of a hybrid approach that incorporates shape optimisation and GD methodologies and exhibits novel characteristics as a design system. GD can provide divergent properties to the design process in contrast to the converging nature of optimisation.
(a) A variety of generated furniture shapes (b) An array of furniture shapes
Figure 5.9: Parametric explorations of design space
It was here exactly that my fascination for generative processes was spawned out of my experience of a responsive parametric model that provided the flexibility to change its shape in response to changing boundary conditions and design decisions. The gen- tle reaction of the sensitive geometry on the smooth organic surface showed me that the computational process provides answers to yet unasked questions, if I am open-minded enough to listen to its gentle whisper. The work on structural optimisation revealed the importance of shape optimisation for performance of structural components and showed the potential application of GD for performance-based design computation of architectural solutions. In addition, I experienced the limitations of design space explorations by gen- erating large catalogues of solutions. Browsing those catalogues to find the solutions that were suitable and desirable for a specific design case seemed cumbersome and difficult.
The shape optimisation problem for structural nodes using additive manufacturing is a multi-criteria optimisation problem that leads to a variety of solutions based on the pareto front. Because the Smart Structures Project and Sydney Opera Bar did not ad- dress the fabrication constraints of additive manufacturing as part of the fitness function, the results were based on single criteria optimisation leading to one specific result for every configuration explored. Reflection on the shape topological optimisation problem led to my interest in the problem of pareto-front exploration by designers. The tools
for interactive design space exploration in architectural optimisation using multi-criteria optimisation were limited. As a result, I started working on Typogenetic Design as a design tool for interactive design space exploration. I identified interactive evaluation of design solutions as part of the fitness evaluation as a feasible solution for the problem of integrating quantitative criteria from performance evaluation and qualitative criteria from designer review. This position for a useful intervention in the evolutionary search needed a different software architecture compared to other tools used in architectural optimisation. I started building Typogenetic Design from an automated shape generation tool that I used for the Shooting Star case study described in the next section.