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oapen-20.500.12657-890932024-04-03T02:23:26Z Chapter Bayes Theory as a Methodological Approach to Assess the Impact of Location Variables of Hyperscale Data Centres: Testing a Concept King, David Wanigarathna, Nadeeshani Jones, Keith Ofori-Kuragu, Joseph Bayes Theorem Delphi Data Centre Location Variables thema EDItEUR::N History and Archaeology The theme of ’The Impact of Engineering Practices on a Sustainable Built Environment’ emphasises the importance of considering various dimensions of resilient infrastructure. Selecting the location for a Hyperscale Data Centre is a crucial process that involves assessing the impact of various location variables. To determine the viability of a location, it is essential to identify the potential risks associated with each variable. This paper presents a proprietary methodological approach that includes a Delphi study to identify risks, a Likert scoring system to assess prior probabilities, and a Bayesian theory-based decision tree to assess the impact through risk prediction. The paper's contributions are significant, and the proposed methodology makes it possible to predict the risk level of each location variable by identifying the appropriate contingency percentage. The study's findings indicate that the paper's proposed approach is an effective way to mitigate the risks associated with selecting a location for a Hyperscale Data Centre. Embracing this knowledge allows us to align research and practise with the conference’s call to studying the resilience of buildings and infrastructure to natural disasters and climate change, and developing strategies for adaptation and mitigation, ensuring that these practises become integral to shaping the future of Data Centres 2024-04-02T15:46:19Z 2024-04-02T15:46:19Z 2023 chapter ONIX_20240402_9791221502893_62 2704-5846 9791221502893 https://library.oapen.org/handle/20.500.12657/89093 eng Proceedings e report application/pdf n/a 9791221502893_39.pdf https://books.fupress.com/doi/capitoli/979-12-215-0289-3_39 Firenze University Press 10.36253/979-12-215-0289-3.39 10.36253/979-12-215-0289-3.39 bf65d21a-78e5-4ba2-983a-dbfa90962870 9791221502893 137 9 Florence open access
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The theme of ’The Impact of Engineering Practices on a Sustainable Built Environment’ emphasises the importance of considering various dimensions of resilient infrastructure. Selecting the location for a Hyperscale Data Centre is a crucial process that involves assessing the impact of various location variables. To determine the viability of a location, it is essential to identify the potential risks associated with each variable. This paper presents a proprietary methodological approach that includes a Delphi study to identify risks, a Likert scoring system to assess prior probabilities, and a Bayesian theory-based decision tree to assess the impact through risk prediction. The paper's contributions are significant, and the proposed methodology makes it possible to predict the risk level of each location variable by identifying the appropriate contingency percentage. The study's findings indicate that the paper's proposed approach is an effective way to mitigate the risks associated with selecting a location for a Hyperscale Data Centre. Embracing this knowledge allows us to align research and practise with the conference’s call to studying the resilience of buildings and infrastructure to natural disasters and climate change, and developing strategies for adaptation and mitigation, ensuring that these practises become integral to shaping the future of Data Centres
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