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Bayes Theory as a Methodological Approach to Assess the Impact of Location Variables of Hyperscale Data Centres: Testing a Concept

  • David King
  • Nadeeshani Wanigarathna
  • Keith Jones
  • Joseph Ofori-Kuragu

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

  • Keywords:
  • Bayes Theorem,
  • Delphi,
  • Data Centre,
  • Location Variables,
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David King

Anglia Ruskin University, United Kingdom - ORCID: 0000-0002-8026-3796

Nadeeshani Wanigarathna

Anglia Ruskin University, United Kingdom - ORCID: 0000-0001-8889-8019

Keith Jones

Anglia Ruskin University, United Kingdom - ORCID: 0000-0002-8883-9673

Joseph Ofori-Kuragu

Anglia Ruskin University, United Kingdom - ORCID: 0000-0003-2872-9437

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  • Publication Year: 2023
  • Pages: 398-406

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  • Publication Year: 2023

Chapter Information

Chapter Title

Bayes Theory as a Methodological Approach to Assess the Impact of Location Variables of Hyperscale Data Centres: Testing a Concept

Authors

David King, Nadeeshani Wanigarathna, Keith Jones, Joseph Ofori-Kuragu

DOI

10.36253/979-12-215-0289-3.39

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

43

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