Digital Twins for Managing Railway Bridge Maintenance, Resilience, and Climate Change Adaptation

Abstract

Innovative digital twins (DTs) that allow engineers to visualise, share information, and monitor the condition during operation is necessary to optimise railway construction and maintenance. Building Information Modelling (BIM) is an approach for creating and managing an inventive 3D model simulating digital information that is useful to project management, monitoring and operation of a specific asset during the whole life cycle assessment (LCA). BIM application can help to provide an efficient cost management and time schedule and reduce the project delivery time throughout the whole life cycle of the project. In this study, an innovative DT has been developed using BIM integration through a life cycle analysis. Minnamurra Railway Bridge (MRB), Australia, has been chosen as a real-world use case to demonstrate the extended application of BIM (i.e., the DT) to enhance the operation, maintenance and asset management to improve the sustainability and resilience of the railway bridge. Moreover, the DT has been exploited to determine GHG emissions and cost consumption through the integration of BIM. This study demonstrates the feasibility of DT technology for railway maintenance and resilience optimisation. It also generates a virtual collaboration for co-simulations and co-creation of values across stakeholders participating in construction, operation and maintenance, and enhancing a reduction in costs and GHG emission.

Track Geometry Prediction Using Three-Dimensional Recurrent Neural Network-Based Models Cross-Functionally Co-Simulated with BIM

“…It is predicted that results from the study will present the potential of BIM and machine learning integration in railway maintenance. The novelties of the study are this study purposes a workflow to cross-functionally co-simulate BIM with machine learning, which is the highest BIM maturity (BIM Level 5) [ 4 ], in order to predict track geometry parameters and there has never been a study focusing on this topic. This can develop automated track maintenance in the railway industry.…”

Multi-Hazard Effects of Crosswinds on Cascading Failures of Conventional and Interspersed Railway Tracks Exposed to Ballast Washaway and Moving Train Loads

“…For example, the sleeper support conditions can be assessed and predicted using machine learning technologies based on the acceleration data of track components [ 57 ] and the rail displacement from digital video records [ 36 ]. Ground penetrating radar (GPR) can also reflect the ballast layer condition and the void zone [ 58 ], as well as on the bridge ends for digital twin based monitoring [ 59 , 60 ]. However, monitoring the sleeper support conditions on the ballasted railway line is challenging.…”

An Integration of Business Processes and Information Management for Improving the Efficiency and Reliability of Infrastructure

“…The platform displays adaptability in its modular architecture, which ensures that additional features and updates can be integrated with minimal disruption (Figure 17). The proposed prototype system performed the tasks efficiently (20 s compared to 3 min) and enabled direct input within models, which is useful when it involves enriching the digital twin of a large project or a network of assets like highways or bridges [49][50][51][52]. The process management system also keeps a log of transactions between models and can display forms and tasks based on which user logs into the system.…”