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Development of an immersive digital twin framework to support infrastructure management: a case study of bridge asset health monitoring
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Development of an immersive digital twin framework to support infrastructure management: a case study of bridge asset health monitoring
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Conference Proceeding
Development of an immersive digital twin framework to support infrastructure management: a case study of bridge asset health monitoring
2024
Overview
Digital Twin (DT) has been widely adopted in construction and infrastructure projects to focus on the management and control of physical systems and assets responsively to meet the users' requirements. When integrating immersion functions with the digital and wireless transformation models, the DT applications can further allow users to interact with and experience the model information in a more engaging and intuitive way. To add new perspectives to DT applications, this research gave reality functions to develop an immersive DT framework and presented a case study of VR- and AR-based bridge health monitoring and twining systems to demonstrate the functionalities. The case study was focused on the Structural Health Monitoring (SHM) processes for a newly constructed extradosed bridge, facilitated by the integration of Building Information Modeling (BIM), Virtual Reality (VR), Augmented Reality, 3D game engines, and Internet of Things (loT) technologies. The bridge's geometric and property information was modeled in BIM parametric design software as a basis for VR model development and was used to develop the BIM-based sensory model in the 3D game engine according to the on-site conditions of the physically installed system. The integrated virtual model was further deployed to the AR device via a VR-to-AR workflow and was presented through the immersive DT dashboard. Project stakeholders can perform bridge damage detection using the information available from the dashboard. In this dashboard the loT tools were used for the multi-source data integration process by storing, processing, and transforming the monitoring data, lending opportunities for predictive simulations of bridge condition states.
Publisher
IAARC Publications
Subject
