# Digital Twin Framework for Road Infrastructure Management
## 4 Digital Twin Framework for Road Infrastructure Management
### Proposed Digital Twin Framework for Road Infrastructure Management
#### 4.3.3 Data/Digital Modeling Layer  
The data/digital modeling layer encompasses digital or virtual representations of corresponding physical assets or entities. As the core component of the DT virtual model, this layer receives processed data from the data processing, integration, and storage layer and organizes it into its respective data models. These models form the basis for executing various functionalities in the subsequent layer. Representation models include the fundamental frameworks such as GIS, BIM, and CIM. Bridge DT models, reviewed in this context, consist of 3D surface models, simulation-based FEM models, data-driven surrogate models, and information management models. Additionally, semantic information models can be classified into rule update models, semantic reasoning models, knowledge retrieval models, and agent-based models. Finally, asset and other models encompass transportation models, mathematical models, asset information models, and agent-based models. In practice, digital models are primarily based on BIM, GIS, or an integrated platform that combines both. In most cases, BIM represents the digital model of built assets, including information about their functional characteristics [37]. It serves as a core component of the DT, supporting a wide range of capabilities and functions. Semantic and comprehensive BIM models can be generated automatically from geometric data and may include the original geometry, current condition updates based on inspection data, and visualization of the monitored areas [9]. Additionally, BIM facilitates connections between construction processes, operations, and infrastructure management by providing predictability and simulation capabilities. Currently, DTs have surpassed BIM, which served as an essential tool for infrastructure information before DTs, by emphasizing the real-time nature of modeling to a greater extent by taking advantage of effectively integrating multi-source and multimodal observational data [54]. On the other hand, BIM supports the development of city-level information models, such as CIM, which functions as a decision support tool by integrating information models and smart technologies (e.g., GIS) at the urban scale [48; 51]. All these models represent objects in multidimensional digital formats, emphasizing the built environment. However, their working mechanisms and functionalities differ significantly from one another [55]. Figure 8 illustrates the distinct differences between BIM, CIM, and DT, as well as their interrelationships for clarity. Figure 8: Comparison of BIM, CIM, and DT [55]. The CIM model shares similar features with BIM but operates on a city-wide scale. Both BIM and CIM require manual data integration methods and are not designed to describe, analyze live events, or develop responses to specific situations. While the construction of DTs can utilize similar reality capture techniques as BIM and CIM, their mechanisms and functionalities are distinct. DTs aim to facilitate innovative, data-driven approaches for planning, management, design, and operational practices within the built environment [55]. Meanwhile, for both planned and existing assets, particularly in scenarios involving construction planning, such as road widening or network expansion, transportation models and other relevant AI-based mathematical models can be applied. These models may include the trip end model, travel demand model, highway and public transport assignment models, variable demand model, and land use-transport interaction models [36]. The asset information model manages data related to equipment, personnel, and production processes associated with physical assets [51]. In the context of road infrastructure, data acquired through various methods--including road asset data, asset management data, and other relevant information--can be incorporated and stored within the asset information model. These models and datasets are utilized in the data analytics/application layer to support insight generation and facilitate optimal decision-making.