A computational methodology for generating modular design options for building extensions

Adaptation of existing building stock is an urgent issue due to aging infrastructure, growth in urban areas and the importance of demolition mitigation for cost and carbon savings. To accommodate the scale of implementation required, there is a need to increase the efficiency of current design and production processes. Computational methodologies have proven to increase design efficiency by generating and parsing through myriad design options based on multivariate (e.g., spatial, environmental, and economic) factors. Modular Construction (MC) is another approach used to increase efficiency of both design and production. This paper combines these approaches in a novel methodology for generating modular design options for extensions of existing buildings (an efficacious form of building adaptation). The methodology focuses on key architectural design metrics such as energy use, daylighting, life cycle impact, life cycle costing and structural complexity, whereby a set of Paretooptimal exploratory design options are generated for evaluation and further design development. A functional demonstration is then carried out for the extension of Ken Soble Tower in Hamilton, Ontario. The contribution of this research is the efficient development and evaluation of design options for improving existing residential infrastructure in order to meet required energy improvements using modular extensions.