Most of the COVID-19 cases in Singapore have primarily come from foreign worker dormitories. This people group is especially vulnerable partly because of behavioural habits, but the built environment they live in also plays a significant role. These dormitories are typically densely populated, so the living conditions are cramped. The short lease given to most dormitories also means the design does not typically focus on environmental performance, like good natural ventilation. This paper seeks to understand how these dormitories' design affects natural ventilation and, subsequently, the spread of the COVID-19 particles by looking at two existing worker dorms in Singapore. Findings show that some rooms are poorly orientated against the prevailing wind directions, so there is dominant stagnant air in these rooms, leading to respiratory droplets' long residence times. These particles can hover in the air for 10 min and more. Interventions like increased bed distance and removing upper deck beds only showed limited ventilation improvements in some rooms. Comparatively, internal wind scoops' strategic placement was more effective at directing wind towards more stagnant zones. Large canyon aspect ratios were also effective at removing particles from higher elevations.
This paper advances the application of computational optimization to design for circular economy (CE) by comparing results of scalarized single-objective optimization (SOO) and multi-objective optimization (MOO) to a furniture design case study. A framework integrating both methods is put forward based on results of the case study. Existing design frameworks for CE emphasize optimization through an iterative process of manual assessment and redesign (Ellen MacArthur Foundation, 2015). Identifying good design solutions for CE, however, is a complex and time-consuming process. Most prominent CE design frameworks list at least nine objectives, several of which may conflict (Reike et al., 2018). Computational optimization responds to these challenges by automating search for best solutions and assisting the designer to identify and manage conflicting objectives. Given the many objectives outlined in circular design frameworks, computational optimisation would appear a priori to be an appropriate method. While results presented in this paper show that scalarized SOO is ultimately more time-efficient for evaluating CE design problems, we suggest that given the presence of conflicting circular design objectives, pareto-set visualization via MOO can initially better support designers to identify preferences.
This paper describes a computational design-support tool created in response to safe-distancing measures enforced during the COVID-19 pandemic. The tool was developed for a specific use case: understanding congestion in crowded migrant worker dormitories that experienced high rates of COVID-19 transmission in 2020. Building from agent-based and network-based computational simulations, the tool presents a hybrid method for simulating building resident movements based on known or pre-determined schedules and likely itineraries. This hybrid method affords the design tool a novel approach to simultaneous exploration of spatial and temporal design scenarios. The paper demonstrates the use of the tool on an anonymised case study of a high-density migrant worker dormitory, comparing results from a baseline configuration against design variations that modify dormitory physical configuration and schedule. Comparisons between the design scenarios provide evidence for reflections on pandemic-resilient design and operation strategies for dormitories. A conclusions section considers the extent to which the model and case study results are applicable to other dense institutional buildings and describes the paper’s contributions to general understanding of configurational and operational aspects of resilience in the built environment.
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