Elsevier

Design Studies

Volume 52, September 2017, Pages 73-95
Design Studies

Meta-Parametric Design

https://doi.org/10.1016/j.destud.2016.09.005Get rights and content

Highlights

  • Inflexibility of parametric models and implications for design are discussed.

  • Explicit and implicit approaches compared in the context of artificial embryogeny.

  • Parametric models generated automatically that are cognisable by human designers.

  • Parametric models evolved in combination with metaheuristic algorithms.

  • Meta-Parametric Design software successfully tested on two case-study projects.

Parametric modelling software often maintains an explicit history of design development in the form of a graph. However, as the graph increases in complexity it quickly becomes inflexible and unsuitable for exploring a wide design space. By contrast, implicit low-level rule systems can offer wide design exploration due to their lack of structure, but often act as black boxes to human observers with only initial conditions and final designs cognisable. In response to these two extremes, the authors propose a new approach called Meta-Parametric Design, combining graph-based parametric modelling with genetic programming. The advantages of this approach are demonstrated using two real case-study projects that widen design exploration whilst maintaining the benefits of a graph representation.

Section snippets

Parametric modelling

Parametric modelling is now a well-established tool in the computational design community. Software applications such as Generative Components (Bentley Systems), Dynamo (Autodesk) and Grasshopper (McNeel and Associates) allow complex ideas to be quickly explored, often beyond the reach of traditional techniques such as hand sketching, physical model making and CAD.

A subset of parametric modelling based on dataflow programming associates parameters and functions to form a Directed Acyclic Graph

An implicit approach

Unlike parametric models, natural systems do not keep an explicit record of how to build an organism laid out in their genes. An organism's DNA contains rule based information that encodes an emergent process of development from a single embryo, i.e. morphogenesis. The complexity of an emergent process means that small alterations to the genotype can often lead to large changes to the phenotype, for example its size, shape and number of repeating modules in the living organism. A small change

Meta-Parametric Design

In response to both the inflexibility of explicit modelling tools and the irreducibility of an implicit approach, the possibility of automatically generating parametric models is an interesting alternative at the early design stage (Harding, Joyce, Shepherd, & Williams, 2013). Although Gero and Kumar (1993) have previously shown methods to widen the design space using re-parameterisation, this approach goes one step further in order to think topologically (DeLanda, 2002) and consider the whole

Implementation

Two real projects are shown here as examples of Embryo being used in different design contexts in practice. Firstly, to generate massing concepts for a residential development. Secondly, for a mixed-use tower project whereby parametric models are evolved to match an existing geometric concept design using shape analysis.

Conclusion

This paper began by highlighting two main approaches to form generation in computational design, explicit and implicit. The authors argued that an implicit approach, though giving the wide design exploration suitable for the concept stage, has a lack of structure that has often proven to be useful in parametric design. Such methods are therefore difficult to use in a collaborative design environment, where a common language is required for communication between humans and machines.

DAG-based

Acknowledgements

This work has been supported in part by Ramboll UK and the EPSRC funded Industrial Doctorate Centre in Systems at The University of Bath (Grant EP/G037353/1). The authors would like to thank both 3DReid and AG5 Architects, with particular mention to Charlie Whitaker & Daniel Nielsen.

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