Automation in Construction
Volume 127, July 2021
Sheida Shahi, Patryk Wozniczka, Chris Rausch, Ian Trudeau, Carl Haas
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 Pareto-optimal 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.
Adaptation of dated residential towers is an urgent issue due to aging housing infrastructure and growing demand for affordable housing. Computational design methodologies have the potential for facilitating optimized design strategies driven by improved energy performance and reduced life-cycle carbon emissions. Modular Construction (MC) can also increase efficiencies in the design and implementation of building adaptation projects and minimize construction waste. The application of MC in the adaptation of existing buildings is gaining interest with improvements to MC technologies and processes, as well as large-scale adoption. There are currently no frameworks for the integration of MC in the adaptation of complex buildings driven by energy performance and Life Cycle Analysis (LCA). To address this gap, a framework is developed for integrating computational design methodologies and design optimization using energy use and LCA for improving overall building adaptation processes. The building adaptation of Ken Soble Tower in Hamilton, Ontario, is used for the functional demonstration. A set of extension modules are considered, and various adaptation scenarios that conform to set design constraints are evaluated for energy use and LCA. The results of this study prove the practicality of using computational design methodologies for the integration of MC in the adaptation of concrete residential towers and can promote the efficiency of improving existing residential infrastructure.