# Digital Twin Framework for Road Infrastructure Management
## 6 Implementation Scenario--Case Study
This entails gathering relevant inventory data (sub-units) and condition data, particularly bridge monitoring data, which includes stress and strain measurements, cracks, and corrosion assessments. Additionally, traffic information and other relevant data, such as temperature, wind speed, direction, and extreme weather conditions, can be collected. Traditionally, inspections heavily rely on the experience of inspectors, with condition evaluations performed using various tools. Most inspections involve site visits and visual checks. The inspection and data collection processes may still utilize a paper-based approach, which hampers efficient maintenance activities. However, employing DTs within this framework allows for a more efficient data acquisition and transmission process. This layer gathers real-time data from bridge infrastructures through IoT devices, embedded  
Figure 11: General workflow of BMS based on [56]. sensors, and mobile sensors, including versatile robots and UAVs, alongside necessary manual surveying and inspection efforts. All collected data can be categorized into five classifications: road asset data (main facilities, such as bridges and related objects); asset management data, including bridge monitoring data like stress, strain, cracks, and corrosion; supplementary data, such as periodic survey records, maintenance records, and the geographic location of defects along with their relevant surrounding assets; knowledge data, including maintenance requirements and standards, bridge operational needs, budgets, and constraints; and general data like bridge traffic loads, road network sections, relevant statistics, and weather data. These data are collected in a spatial context, linked with their geographic locations and semantic relationships. (2) Data processing, integration, and storage layer  
The data processing layer handles the information collected through the data acquisition layer from various sources. These collected data can exist in different formats, including databases, documents, files, spreadsheets, and images, depending on their characteristics. For instance, data from real-time sensors may be in image or video formats, while periodic survey records, maintenance records, and operational records are often stored in document files. Location information for defects or related assets may be stored in geospatial data formats. Knowledge data, which include maintenance requirements, relevant standards, operational needs, budgets, and constraints, can typically be found in spreadsheets or document files. Bridge traffic loads might be recorded in log files, while weather-related data, such as temperature, wind speed, wind direction, and extreme weather conditions, will be in their respective formats. Figure 12: The implementation of the road management DT framework for BMS. The acquired raw data undergo processing using relevant techniques like machine learning for noise removal, anomaly detection, and data cleaning. This processing ensures data quality and accuracy, allowing the collected data to be further analyzed while remaining free from various defects. Additionally, data from multiple sources can be integrated into the DT data structure, such as ontologies, to enhance modeling and analytics capabilities. After these procedures, the collected data are converted into a standardized format to facilitate further analysis. (3) Data/digital modeling layer  
This layer as the core layer of the DT model receives processed data from the data processing, integration, and storage layer. It organizes these data into the appropriate data models, such as representation models, bridge models, semantic models, and asset data models. For example, a BIM can be automatically generated from geometric data, incorporating the original geometry as well as current condition updates based on inspection data. Since the BMS does not currently include the geometric representation of bridges, integrating BIM models into this system will provide a comprehensive, accurate, and advanced virtual representation [56].