PARAMTR: Enhanced generative design tools for large-scale housing developments within a prefabrication context

  • YEAR
    Joe, Joshua
    Pelosi, Antony
    Welch, Christopher
    2020 Conference Papers
    Conference Papers
    Digital Architecture, BIM & City Information Modelling (CIM)


The research investigates how enhanced, generative design tools can improve building performance and effectiveness of prefabrication at scale, while also encouraging design variance. In this context, enhanced generative design tools refers to a partially algorithmic design tool that supports the designer in their decision-making whilst retaining designer authorship during the design phase.

Designers encounter 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. Generative design tools allow for more complex and contextual modelling. When considered in relation to large-scale low-density housing developments, both technologies have the potential to revolutionise and increase economic and environmental viability for future developments.

A new generative tool has been created (PARAMTR) to address the above issues using a designbased research methodology. After identifying critical priorities based on a literature review, the research goal is to produce four different residential designs generatively optimised for prefabrication at scale.

Results from initial research show potential for vastly improved design variance, qualitative and quantitative aspects, and increased time efficiency. The paper will discuss progress towards designing and building smarter homes at scale.

Keywords: Generative design; parametric; residential; prefabrication.


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