TY - JOUR
T1 - Beyond digital shadows
T2 - A Digital Twin for monitoring earthwork operation in large infrastructure projects
AU - Rogage, Kay
AU - Mahamedi, Elham
AU - Brilakis, Ioannis
AU - Kassem, Mohamad
N1 - Funding information: This research is supported by UKRI Innovate UK (Project number: 44584, Project Title AI-driven and real-time command and control centre for site equipment in infrastructure projects; and Project number, Funding body: UKRI Innovate UK, United Kingdom) under the ‘Increase productivity, performance and quality in UK construction’ competition.
PY - 2022/12/28
Y1 - 2022/12/28
N2 - 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.
AB - 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.
KW - Data pipeline
KW - Digital Twin
KW - Machine learning
KW - Data analytics
KW - Earthwork
U2 - 10.1007/s43503-022-00009-5
DO - 10.1007/s43503-022-00009-5
M3 - Article
SN - 2730-5392
VL - 1
JO - AI in Civil Engineering
JF - AI in Civil Engineering
IS - 1
M1 - 7
ER -