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Beyond digital shadows: Digital Twin used for monitoring earthwork operation in large infrastructure projects.
Rogage, Kay; Mahamedi, Elham; Brilakis, Ioannis; Kassem, Mohamad.
Afiliação
  • Rogage K; Northumbria University, Newcastle Upon Tyne, UK.
  • Mahamedi E; Northumbria University, Newcastle Upon Tyne, UK.
  • Brilakis I; Cambridge University, Cambridge, UK.
  • Kassem M; Newcastle University, Newcastle Upon Tyne, UK.
AI Civil Eng ; 1(1): 7, 2022.
Article em En | MEDLINE | ID: mdl-38013884
Current research on Digital Twin (DT) is largely focused on the performance of built assets in their operational phases as well as on urban environment. However, Digital Twin has not been given enough attention to construction phases, for which this paper proposes a Digital Twin framework for the construction phase, develops a DT prototype and tests it for the use case of measuring the productivity and monitoring of earthwork operation. The DT framework and its prototype are underpinned by the principles of versatility, scalability, usability and automation to enable the DT to fulfil the requirements of large-sized earthwork projects and the dynamic nature of their operation. Cloud computing and dashboard visualisation were deployed to enable automated and repeatable data pipelines and data analytics at scale and to provide insights in near-real time. The testing of the DT prototype in a motorway project in the Northeast of England successfully demonstrated its ability to produce key insights by using the following approaches: (i) To predict equipment utilisation ratios and productivities; (ii) To detect the percentage of time spent on different tasks (i.e., loading, hauling, dumping, returning or idling), the distance travelled by equipment over time and the speed distribution; and (iii) To visualise certain earthwork operations.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: AI Civil Eng Ano de publicação: 2022 Tipo de documento: Article País de publicação: Singapura

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: AI Civil Eng Ano de publicação: 2022 Tipo de documento: Article País de publicação: Singapura