A digital twin-based decision analysis framework for operation and maintenance of tunnels

Digital twins are at the core of urban infrastructure maintenance, operation and evaluation. In modern cities, digital twins can be established to integrate the life cycle spatio-temporal data of tunnels, as well as analyze the potential causes and effects of abnormalities in civil structures or electromechanical equipment. This will provide reasonable and feasible countermeasures to guide and optimize the operation and maintenance (O&M) management. This paper proposes a digital twin-based decision analysis framework for the O&M of tunnels. The framework defines an extended COBie standard-based organization method for the tunnel twin data, and uses Semantic Web technologies to achieve fusion at the data, object and knowledge levels. In addition, a rule-based reasoning engine has been developed by establishing a large rule base. The framework has been utilized for the fault cause analysis of fans in Wenyi Road Tunnel in Hangzhou, China to demonstrate its decision analysis process and validity. The application results show that the framework can provide efficient and automatic decision analysis support for the O&M of tunnels.