# Digital Twin Framework for Road Infrastructure Management
## 4 Digital Twin Framework for Road Infrastructure Management
### Analysis of Existing DT Frameworks for Road Infrastructure Management
#### 4.1.3 Road Tunnels  
Tunnels are an essential component of integrated transportation infrastructure [43]. A DT can be developed to integrate a tunnel life cycle spatiotemporal data model and to analyze the potential causes and effects of abnormal conditions in these civil structures [44]. [PERSON] et al. [45] presented a methodology for developing an underground utility tunnel based on a DT framework. The proposed tunnel DT comprises three main layers: data acquisition, modeling, and service. The data acquisition layer gathers information from multiple sources, including infrastructure (e.g., 2D and 3D documents, spatial data), sensors (e.g., sensor data, images), and external records such as climate datasets, demographic information, and maintenance logs. The modeling layer is responsible for infrastructure and sensor modeling, geospatial modeling, and data storage. Its primary function is to model underground tunnel structures using BIM and GIS technologies. The service layer provides functionalities for managing both unusual situations, such as fire detection, disasters (e.g., flooding, earthquakes), and spatial object displacement, and routine operations, including space utilization, asset management, and event forecasting. [PERSON] et al. [44] proposed a DT-based decision analysis framework to support the O&M of tunnels. This framework consists of four layers: data acquisition, data fusion, model building, and service. The data acquisition layer collects and stores data while establishing the database required for the tunnel DT's operation. The data fusion layer organizes and integrates DT data to enable spatiotemporal representation and semantic fusion, thereby incorporating more comprehensive and accurate information into the O&M decision-making process. The model construction layer develops virtual models of physical tunnels and simulates their properties, operational states, and evolution laws to reflect the tunnel's actual condition. Finally, the service layer provides tunnel O&M managers with essential services for decision-making, including fault diagnosis, cause traceability, condition assessment, trend prediction, and cost analysis, to support O&M planning. [PERSON] et al. [43] developed applications for tunnel DT construction and virtual-real integration. The tunnel DT reference model was designed to include a real tunnel scene, a virtual tunnel scene model, DT data, service systems, and interconnections between all layers. DT data served as the foundation for constructing the DT, while the virtual tunnel scene model formed the core of the DT framework. The service system acted as the platform through which the DT was implemented. Its primary functions included condition monitoring, digital interaction, decision support, and optimal control between the real and virtual models. The outcomes of this model contributed to smooth tunnel traffic flow, accident prevention, facility management, emergency response, and the efficient digital management of tunnels. [PERSON] et al. [46] presented a decision support system (DSS) for the pathology assessment and diagnosis of tunnels, aimed at supporting tunnel maintenance tasks that require complex decision-making. Notably, this system is designed to ensure safety while optimizing maintenance and repair costs. It is structured into three core layers: the data layer, the application layer, and the UI layer. The data layer houses inspection databases containing information related to tunnel inspections and repairs. The application layer is primarily used to query data from the semantic repository, where intelligent processing is performed to identify tunnel pathologies. The UI layer enables tunnel experts to interact with the DSS to obtain insights and information about tunnel conditions. [PERSON] et al. [47] developed a tunnel defect diagnosis system based on Industry Foundation Classes and Semantic Web technologies. The system comprises four layers: the data layer, the semantic web layer, the function layer, and the decision-making layer.