# 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
The data storage layer contains two primary data types: static data representing land characteristics and dynamic data acquired from IoT devices. Geographic information system (GIS) data are used to model terrain features, while building information modeling (BIM) is used to represent buildings and infrastructure for improved environmental understanding. The integration layer processes and harmonizes data from diverse sources. It supports simulation, monitoring, and prediction tasks by utilizing models and algorithms. This includes road network analysis to evaluate the potential impact of hazards on road performance and resilience under various scenarios, such as disaster events or network recovery situations. The application layer offers several functionalities, including real-time monitoring, hazard analysis, risk and impact assessment, and simulation. Finally, the visualization layer employs GIS-based tools to present analytical results in an interactive and accessible format. This includes dynamic maps, charts, tables, and dashboards, which enhance user comprehension by clearly conveying system feedback. [PERSON] et al. [36] proposed a five-layer road DT planning framework, consisting of data acquisition, data processing, data modeling and algorithm, data analysis and control, and service layers. Unlike DT frameworks applied during the O&M phase, this model is designed for the pre-construction or planning stage, where the primary inputs are derived from yet-to-be-built infrastructure. The data acquisition layer focuses on collecting data relevant to the road planning process, particularly during the feasibility stage. The input data are classified as internal (e.g., user input and historical records) and external (e.g., web-based sources, transportation data, and demographic information). The data processing layer receives input data from the data acquisition layer, integrates these data obtained from multiple sources, and standardizes them into computable formats for further analysis. The data modeling and algorithm layer comprises various models essential to the road planning process. It also incorporates DT models, such as those for simulation and forecasting, along with embedded algorithms designed for multi-objective optimization. The analysis layer serves as the core of the DT framework, providing an analytical environment based on the input data and modeling components. It contains two primary modules: one for performing data analysis and another for generating viable or optimal alternatives to support decision-making. The service or application layer provides a UI through which stakeholders can interact with the system to address planning-related challenges. This layer enables users to manage and control the entire planning process. [PERSON] et al. [29] proposed a DT framework for transportation infrastructure, including bridges, tunnels, and roads. The framework comprises five layers: data acquisition, data transmission, digital modeling, data fusion, and a service layer that facilitates user interaction. The data acquisition layer collects real-world data from physical assets to ensure that the DT accurately replicates its physical counterpart. Data are transferred between the physical and digital layers via the data transmission layer, which enables secure and efficient transfer of information collected by sensors and IoT devices. Digital modeling involves creating detailed and dynamic models, such as BIM, GIS, or data-driven frameworks, that accurately mirror the physical asset, including its geometry, behavior, properties, and systemic interactions. The data fusion layer integrates information from the data acquisition layer with the digital models created in the digital modeling layer. This process typically involves data processing, data mapping, data synchronization, data fusion, simulation, expert knowledge, and AI-based functions to ensure that the DT accurately reflects the current state and behavior of its physical counterpart.