Copia del contrato celebrado por un mínimo de 3 años con la empresa que cuenta con el Programa IMMEX bajo la modalidad de albergue.

Inspired by the experience of the two case studies above, which I conducted to refine my research focus, I started working on a shape generation approach to build a generative engine as a basis for introducing adaptivity of design tools into interactive user input. After gaining an initial overview of interactive optimisation, I started exploring different representations for shape generation and tested one of them - an evolutionary search implementation using shape schema grammar. This grammar evolution approach revealed the potential for use as a generative engine. The phenomena and concepts encountered during my exploration of using grammar evolution are elaborated in this case study, which was dedicated to automated shape design, and also shared at [Muehlbauer et al. 2017a].

Shooting Star was a case study that reflects ideas of organic growth in a controlled GD environment. It used a genetic programming system that I designed using the PYGP library as a starting point for the implementation with Python as a software prototype in Rhinoceros CAD. The combination of grammar translation with a tree-based representa- tion was implemented using strongly-typed genetic programming. Grammar evolution for shape generation was used to develop a geometric vocabulary by means of shape schema grammar.

Early experiments revealed the complexity of computational interferences that ap- pear when directly using the Python script in the Grasshopper environment to generate shapes. The experiments conducted in this context showed the same generative potential for shapes generated using a convex hull algorithm (Figure5.11a) and complex shape con- figurations (Figure5.11b) of the Python implementation, while adding a variety of options for the post-processing of the shapes (Figure 5.11c). As the whole set of activities for a Grasshopper plug-in development on top of the conceptual exploration was beyond the scope of this study, I decided to build my software prototype using RhinoPython script. As a result, I was not able to use the whole scope of simulation and design translation capabilities of visual programming at that time.

Figure 5.10: Software pattern for Shape Generation

(a) Shapes generated by Con- vex Hull algorithm

(b) Complex shapes by com- bining meshes

(c) Post-processed array of shapes

Figure 5.11: Shape explorations in Grasshopper

bility of the generated geometries for the intended morphological explorative search pro- cedure. In these tests, initial mesh geometries were generated and combined into complex shapes by using the simple shape schema grammar representation presented in the BNF representation5.12. The generated variety satisfied the expectations for a vocabulary of shapes that could be used for the initialisation of a shape schema grammar to explore more complex design geometries. For the quick development of an evolutionary system, the chosen representation was extended to incorporate a fitness evaluation. At this stage of the design tool development, the complexity of fitness criteria was comparably low be- cause only two simple criteria were used as fitness functions (Equation5.1): (a) minimise surface area (reduce facade area and costs) and (b) maximise shape volume (increase built volume and revenue). This fitness measure also was used as an initial heuristic for the

hShapei ::= hPoint i, hPoint i, hPoint i, hPoint i, hPoint i, hPoint i, hPoint i, hPoint i, hPoint i, hPoint i hPoint i ::= X, Y, Z

Figure 5.12: Shape schema grammar representation

sustainability performance of complex building shapes, because the combination of the two criteria led to preferred selection of compact shapes. The results of these explorations were reported at [Muehlbauer et al. 2017a].

F itness = Surf aceArea/V olume (5.1) After my first studies of shape generation, a quick exploration of the application of the developed method for the generation of facade designs as a means of mass-customisation was undertaken. Generative facade designs like the ones showcased in Figure5.13provided initial insights on the design potential of the design tool used in this part of the case study on automated shape design. A design study of a hovering terminal for servicing private jets at airports used a custom design tool based on the developed generative approach. An illustration from the competition entry called Hoverport is displayed in Figure 5.14.

Figure 5.14: Application in Hoverport competition entry

Shooting Star (Figure5.15) extends the unique character of the iconic site by sculp- tural activating the space using a futuristic residential concept that addresses sustainabil- ity not only from an environmental perspective, but includes considerations about social and cultural sustainability. During the design process an occupant profile was used to guide the exploration: a modern creative millennial in a bachelor situation is inhabiting this exciting artefact, which incorporates smart technology to the extent that the build- ing exhibits awareness of the users in their current situation and reacts to their needs. The concept of sharing space with others at the centre of a community is reflected in the flexibility of spaces and integration of extendable technology.

Preliminary Conclusions At this stage of the software tool under development, an experimental prototype was used to explore different representations during the auto- mated generation of shapes with simple constraints and fitness criteria. Feature-based (reported in Appendix E Evo Type/Reverse Workshop) 2 and particle-based modes of representations were tested and investigated for their potential to integrate control struc- tures. The explorations performed during this case study revealed some characteristics necessary to use a case-based and site-based approach frequently found in architectural

2

The shapes generated using the feature-based representation extracted from the typological analysis turned out not to be intuitive enough for designers to engage with.

Figure 5.15: Shooting Star competition entry

design. The chosen mesh representation allowed me to generate compact building shapes of any dimension and proportion, but without their differentiation into sub-shapes.

A comprehensive evolutionary search was established using shape schema grammar and initial tests of command-line based designer interaction were undertaken. Those test showed that the narrow interactive approach was exhausting, and therefore not feasible for the exploration of large solution spaces. At the same time, the ergonomics of using the command line to evaluate design solutions was uncomfortable and, as a result, needed consideration.

The emergent potential of the shape schema grammar was narrow and led to the use of shape grammar with emergent properties during further investigations. Limitations to the shape variety generated by the experimental prototype revealed the need for more complex representations to facilitate creative design using design space exploration. The design

potential of combining mesh shapes using shape grammar was investigated as potential solution to provide the necessary resolution for shape generation.

Evolutionary shape generation provided a suitable framework for the development of a tool enabling design space exploration. Actual design activity with all its intentions was not able be integrated in shape generation, but some framing was provided by the constraints and criteria during evolutionary search. The articulation of constraints implicit in the shape representation needed to be complemented by explicit constraints during multi-criteria optimisation. In addition, qualitative criteria could not be integrated into the shape generation approach explored in Shooting Star.