# 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
This integration enhances the DT's effectiveness in supporting data analysis and informed decision-making. The service layer provides the required interface and functionalities through the DT platform, supporting transportation infrastructure applications such as bridge health monitoring, defect detection, lifecycle management, safety evaluation, and proactive maintenance. [PERSON] et al. [37] proposed a DT-based conceptual framework for the lifecycle management of smart civil infrastructure. This framework is structured into several layers: monitoring and reality capture, data management, data engineering, visualization, open-BIM, and service. The monitoring and reality capture layer is responsible for real-time data acquisition using IoT technologies, such as sensors, video cameras, radio-frequency identification (RFID) systems, and quick response (QR) codes. This ensures seamless collection and interpretation of real-time information. The data management layer receives real-time data from the acquisition layer, which includes cloud-based BIM, IoT data, and asset management databases. ML techniques are applied to the IoT database to assess data quality and accuracy, verify incoming data, and detect anomalies. The data engineering layer performs analysis and decision-level data fusion using AI tools, including ML and deep learning models, within cloud and edge computing environments. In the context of road infrastructure asset management, edge computing facilitates real-time data analysis at the point of data generation, supporting immediate decision-making for predictive maintenance and system optimization. The results of these analyses are presented through user-friendly interfaces, which feature an automated control dashboard integrated with a GIS environment in the visualization layer. The insights provided by the DT are reviewed by users to enable more informed and effective decision-making. [PERSON] et al. [38] proposed a unified framework for the design, prototyping, and testing of DTs for bridges and road infrastructure, aimed at enhancing their operational efficiency, management, and development. The framework was designed for implementation using the Robot Operating System and relevant simulation tools. It consists of multiple interconnected layers: a physical/virtual layer (representing interconnected entities), a sensor layer (including both fixed and mobile sensors), a carrier layer, an add-on layer, a data acquisition layer, an annotation layer (for manually added metadata), and an application layer (dedicated to data analysis and decision support). The framework is intended to support physical road infrastructure management by enabling interaction with real-world conditions. In the physical/virtual layer, the physical and digital representations of road infrastructure are interconnected. To facilitate the transmission of real-world data, sensors are embedded within the road infrastructure network either before or during the construction phase. Furthermore, mobile sensors collect essential data through various carriers and equipment, such as wheeled, legged, and limbless robots, as well as unmanned aerial vehicles (UAVs). Additional sensors can be mounted on selected carriers as needed. The data acquisition layer consolidates raw data collected from both embedded sensors in the infrastructure and mobile sensors deployed on carriers. The annotation layer enables the manual input of supplementary information, such as user reports and observational data, which may include road conditions, structural damage, and traffic scenarios. Data interpretation occurs in the application layer, where collected data are analyzed, synthesized, and presented to users. This layer supports decision-making by delivering actionable insights related to road infrastructure conditions and performance. Table 1 summarizes the findings obtained from DT applications in road transportation infrastructure.