Journal
SUSTAINABLE CITIES AND SOCIETY
Volume 68, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.scs.2021.102791
Keywords
Automatic optimal design; Social distancing measures; Computer aided design of physical spaces; COVID-19 pandemic
Categories
Funding
- University of Bradford's COVID-19 Response Fund
- Spanish Ministry of Science, Innovation, and Universities (Computer Science National Program) of the Agencia Estatal de Investigacion and European Funds (AEI/FEDER, UE) [TIN2017-89275-R]
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As the COVID-19 pandemic evolves, there is a need to develop appropriate technological solutions to reconfigure physical environments to accommodate physical distancing measures. A design optimization methodology utilizing the solution to the mathematical circle packing problem is proposed to provide optimal redesign solutions for a given physical space.
As the COVID-19 pandemic unfolds, manually enhanced ad-hoc solutions have helped the physical space designers and decision makers to cope with the dynamic nature of space planning. Due to the unpredictable nature by which the pandemic is unfolding, the standard operating procedures also change, and the protocols for physical interaction require continuous reconsideration. Consequently, the development of an appropriate technological solution to address the current challenge of reconfiguring common physical environments with prescribed physical distancing measures is much needed. To do this, we propose a design optimization methodology which takes the dimensions, as well as the constraints and other necessary requirements of a given physical space to yield optimal redesign solutions on the go. The methodology we propose here utilizes the solution to the well-known mathematical circle packing problem, which we define as a constrained mathematical optimization problem. The resulting optimization problem is solved subject to a given set of parameters and constraints ? corresponding to the requirements on the social distancing criteria between people and the imposed constraints on the physical spaces such as the position of doors, windows, walkways and the variables related to the indoor airflow pattern. Thus, given the dimensions of a physical space and other essential requirements, the solution resulting from the automated optimization algorithm can suggest an optimal set of redesign solutions from which a user can pick the most feasible option. We demonstrate our automated optimal design methodology by way of a number of practical examples, and we discuss how this framework can be further taken forward as a design platform that can be implemented practically.
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