# Digital Twin Framework for Road Infrastructure Management
## 4 Digital Twin Framework for Road Infrastructure Management
### Analysis of Existing DT Frameworks for Road Infrastructure Management
#### 4.1.1 Road Infrastructure Network  
In general, the concept of road infrastructure comprises all types of its physical components, such as road and roadside facilities, geotechnical facilities, drainage and culverts, and road structures, i.e., bridges and tunnels. Road infrastructure asset classification and its components can be found in [30; 31], specifically regarding Korean national highways, which may serve as a representative example. Road infrastructure necessitates proper and timely maintenance and management to prolong its possible service life. Also, maintaining and managing the road infrastructure network is essential to keep the good condition of roads and smooth traffic, and enable economic operation of roads without damage [30]. [PERSON] et al. [32] proposed a DT-based decision support tool for road inspection and pavement maintenance. This tool consists of four fundamental DT layers: the physical layer, database layer, server layer, and application layer. The physical layer performs data collection throughout the lifecycle of the road using various tools and sensors. The databaselayer manages the asset management database, road DT data, and sensor information. The DT, which forms the core of the architecture, resides within this layer, enabling data visualization and model updates. The server layer comprises the asset management system, web server, and management decision support tools, all of which facilitate infrastructure condition analysis and optimize asset management. Finally, the application layer provides a UI through a web platform and mobile applications. [PERSON] et al. [33] developed a DT-based application for road vegetation control management. Their DT architecture comprised four layers: data acquisition and preprocessing, data structuring and integration, data analytics, and service. The data acquisition and preprocessing layer forms the foundation of the road DT, linking physical assets to their virtual counterparts. Road data can be categorized into three main groups: asset data (e.g., physical properties, location), asset management data, and knowledge data (e.g., policies, standards). The data structuring and integration layer ensures the accurate representation of data and semantic information while integrating various data sources to support effective data searches. The data analytics layer employs analytical models for data fusion, modeling, and simulation to support asset condition monitoring, evaluation, and the prediction of real-world events. Finally, the service layer facilitates interaction between DTs and stakeholders, enabling improved decision-making and efficient O&M. [PERSON] et al. [34] implemented a DT-based layered framework to develop asphalt pavement modeling software for analyzing vehicle-tire-pavement interactions. The proposed road infrastructure DT framework comprised five layers: structural monitoring, data acquisition and storage, analytical modeling, visualization, and decision-making. Structural monitoring involves collecting data from real-world assets using sensors and monitoring technologies. These data are stored and used for analysis, which forms the core component of the system and enables the simulation of asset conditions in real-world environments. The results are visualized within the DT system, generating insights that inform asset maintenance and management decisions. This process is continuous and bidirectional, evolving with the ongoing updates received from structural monitoring systems and associated models. [PERSON] et al. [35] proposed a landscape DT architecture for multi-hazard risk analysis, including applications related to road networks. The architecture comprised five main layers: physical, data storage, integration, application, and visualization. The physical layer represents the real-world environment and incorporates data collected from sensors such as rain gauges and change detection systems.