# Digital Twin Framework for Road Infrastructure Management
## 6 Implementation Scenario--Case Study
Additionally, bridge DT models--including 3D surface models, simulation-based FEM models, data-driven surrogate models, and information management models--are created and updated using the processed data from the previous layer. These models help determine the actual state of the bridge surface, assess the structural condition, the bridge's history, and make predictions about its future state [42]. Road asset data, asset management data, and other relevant information are stored within the asset and other models category. When the data are processed as semantic data, such as ontologies in the processing layer, they can be represented in semantic models. These include rule update models, semantic reasoning models, and knowledge retrieval models. (4) Data analytics/application layer  
In this layer, road asset management analytics provides valuable information regarding bridge conditions, maintenance planning and prioritization, and decision-making support. The maintenance strategy category offers functions such as predictive insights, enabling the forecasting of future asset conditions, as well as the prioritization of maintenance actions by comparing various maintenance options. Transportation management functionalities, including traffic management and planning, deliver traffic-related services such as traffic adjustments and other necessary interventions. Bridge degradation modeling can be facilitated through the infrastructure performance degradation function within the simulation category. Information stored in the asset information model, such as monitoring data on bridges (e.g., stress, strain, cracks, and corrosion), can be utilized. Additionally, sub-functions such as asset optimization, risk prognosis, and cost simulation can be performed. The evaluation category is the most critical functional aspect of this layer, focusing on assessing various aspects of bridge infrastructure. In our case, this includes evaluating features related to bridge conditions, such as strength, durability, and damage. Sub-functions within the evaluation category, like state assessment, condition assessment, and safety assessment, can be leveraged. Asset conditions can be visualized in both 2D and 3D formats, potentially within a GIS environment or through BIM representations. Functions in the monitoring category support the ongoing assessment of bridge conditions by gathering data on stress, strain, cracks, and corrosion. The control category functionalities allow for the operation and management of bridge assets through DTs, remote operation control, data collection, real time monitoring, and synchronization between DTs and the current condition of the assets. Lastly, the alert category functionalities assist in reporting analysis results and managing emergencies related to bridges, such as disaster warnings, extreme weather, asset condition alerts, and warnings for fire or flooding, all aimed at enhancing safety. Users/User interface layer  
The user interface layer allows users to access and interact with the DT to manage road assets and gain insights for informed decision-making. Users can access the DT platform and utilize various functionalities within the data analytics application layer. This includes data analytics functions such as road asset management, maintenance strategy, transportation management, and simulation. These tasks can be performed through web portals, desktop applications, mobile applications, or other platforms. Within this framework, bridge managers can input the necessary data or parameters required for operating or maintaining physical bridges via web (desktop) or mobile applications. As a result, users receive recommendations, actionable insights, or decisions regarding the parameters needed for optimal asset operation. At this stage, users can control (send parameters) and supervise bridge assets through data analytics and the application layer, or directly from the service layer.