Data-driven digital twin-based smart tunnel maintenance system

This is the authors' abstract. We don't add key points for this paper.

Tunnel facility management (FM) is crucial for ensuring safety, efficiency, and resilience of tunnel infrastructure. Current FM practices, such as reactive and preventive maintenance, have limitations - reactive maintenance can't prevent failures, and preventive maintenance can't predict asset maintenance needs, leading to costly and inefficient processes. In addition, existing computerized tunnel FM systems face various challenges, including the lack of integrated real-time monitoring information, visualization of assets in a three-dimensional (3D) environment, supporting predictive maintenance, and scaling into long infrastructure with complicated spatiotemporal relationships. This study addresses these limitations by proposing a data-driven digital twin (DT)-based framework that supports predictive maintenance to improve tunnel FM processes and enhance resilience. The proposed framework consists of six layers, allowing the integration of data from monitoring system, FM system, and building information modeling (BIM) models. The framework proposes a flexible tunnel data model and classification system that hierarchically divides the tunnel models, ensuring an efficient data connection from the physical twin to the DT. The system was implemented in a tunnel case study that generates maintenance plans and work orders using historical and current condition monitoring data, and the 3D visualization technology suggests maintenance and repair processes, making the FM decision process more effective. The proposed system detected and predicted the twin state based on a data-driven analysis, and the prediction accuracy of the machine learning models was sufficiently high for use in real scenarios to make FM plans in advance and prevent asset failures. The proposed framework is contributing to the infrastructure resilience by enhancing the tunnel system ability to predict the maintenance tasks and prevent failures using data-driven DT technology.