Wiki Definition:

Generative Design

In the context of this site, Generative Design is taken to mean the manipulation of one or more aspects of a building's form or materiality in response to the results of a progressive series of performance calculations. In this way the geometry, material properties and/or operational characteristics of a building can be optimised to meet specific performance criteria.

The Generative Design Process

A generative design process requires three things:

  • A Performance Metric:
    One or more calculation results that can be derived directly from a computer model or calculation, providing a quantitative or qualitative indication of building performance. This can be as simple as a single number or as complex as an entire annual load profile. However, it must be possible to construct an unequivocal numerical test by which to determine an ordinal relationship between the results of multiple analysis. This usually means being able to judge the results of one calculation to be better/worse, desirable/undesirable, greater/lesser or above/below another.
  • A Configuration Variation:
    This is some aspect of the physical configuration of the building model that will be manipulated or changed before each consecutive calculation. This could be as simple as the width of a window or the thickness of insulation in a wall, or as complicated as the entire building form. This aspect is usually the real focus of the problem as the automated manipulation of complex building geometry is still a developing field. It is usually the main difficulty as well. The key is the ability to use script commands or a model generator to make changes that will affect the selected performance metric(s) and allow differences to be judged.
  • A Decision-Making Response:
    A means of determining which configuration parameter should be varied and by how much in response to each consecutive analysis result. Whilst ideally mathematical optimisation techniques would be used to 'home in' as quickly as possible on the most appropriate model configuration, it is often just as useful at early design stage to incrementally 'approach' a required target or randomly generate values and test for the best results. However, from the numerical test on each performance metric, it must be possible to either judge that the required target has been reached or to compute the magnitude and/or direction of subsequent variations such that results tend more towards desirable than undesirable values.

Generation and Optimisation

Resistance to generative systems has always been high within the building design industry - for good reason as the issues involved are very complex and there is often no obvious solution to any particular set of design problems. Also, every building is usually a compromise between a vast array of competing requirements. Rarely can a building element be truly optimised for any particular use or application, but instead must be flexible and adaptable to many different uses. Thus, when faced with many competing criteria, the best design solution is often the 'least worst' option.

However, this should not preclude the designer from at least knowing what the optimum for a particular application would be. In fact this is how most designers work - they know exactly what they would like to achieve, but then have to work within the constraints of budget, brief and regulations to achieve the best they can. This is the primary skill of a designer - assimilating a myriad of complex and competing requirements and then making the best set of compromises from a wide range of available options.

Of most significance here is that designers can work equally well with both objective (quantifiable) and subjective (unquantifiable) constraints. In fact, at the earliest stages of design it is only really possible to work with subjective issues as there is insufficient hard information about the building to calculate many of the objective criteria. Computer systems on the other hand tend to be of little use in tasks that involve subjective or unquantifiable parameters, but excel at objective tasks with clearly defined and quantifiable parameters, and with highly repetitive or iterative problems.

Thus the aim of generative design is to achieve the best compromise. Computational analysis and simulation can make a significant contribution at the very earliest stages of design by generating optimal solutions to very focused and tightly defined aspects of the design problem. The results may not be immediately and directly applicable, but provide useful information for the designer to assimilate within the broader design context.

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