PARAMTR

josh joe

How can an enhanced generative design tool improve optimisation of building performance and feasibility of prefabricated housing at scale, whilst encouraging design variance in the conceptual and developed design phases?

The research aims to investigate how enhanced, generative design tools can improve building performance and effectiveness of prefabrication at scale, whilst also encouraging design variance. In this context, “enhanced generative design tools” refers to a partially algorithmic design tool that enhances the designer in their decision-making whilst retaining designer authorship during the design phase. Designers are encountering more issues with residential projects as complexity, scale and performance requirements increase. Prefabrication has the potential to significantly reduce construction time, cost, and material waste at scale, whilst parametric design tools allow for more complex and contextual modelling. When considered in relation to mass housing developments, both technologies have the potential to revolutionise and increase economic and environmental viability for future developments. The paper utilises a design-based research methodology to iterate and refine the scope of PARAMTR. A generative tool (PARAMTR) was created based upon identified priorities, with the research goal being to produce at least 4 vastly different residential designs generatively optimised for prefabrication at scale. Results from initial research show potential for vastly improved design variance and efficiency of time spent on projects improved qualitative and quantitative aspects of the individual designs. The paper will discuss progress towards the research goal.

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