## 6 Current Applications of DTs in Transportation Infrastructure
### Infrastructure Maintenance  
Apart from planning and traffic management, the use of DTs is also finding increasing application in infrastructure maintenance. Traditional maintenance--usually based on periodic inspection with reactive repairs--causes sudden failures and expensive downtime. DTs, in this view, will enable a shift toward predictive maintenance since the continuous monitoring of the health of infrastructure assets allows for the early detection of potential issues that could become critical. DTs are based on real-time sensor data, providing very high accuracy in simulations and data analytics, so as to allow improvements to be made in the facility's interaction with both the environment and users [109]. In this respect, a highly relevant case study is the application of DTs in the maintenance of bridges in China. A host of sensors integrated into the bridge structure monitor factors such as stress, vibration, and temperature. These data feed into the DT, which processes the data using machine learning algorithms to identify anomalies and predict potential structural failures [110]. By examining these trends, proactive scheduling of repairs and rehabilitation by maintenance teams limits the possibility of surprise closures of these bridges and helps provide a longer life to the asset. This chapter describes the application of DTs in intelligent construction and their relative advantages and disadvantages. It then analyzes the research theme through a case study approach to explain the importance of DTs in smart construction. Some researchers have designed a methodology for constructing full lifecycle energy consumption detection based on DT technology to try to reduce errors caused by traditional full lifecycle building energy consumption detection methods [51]. In this case, the whole-lifecycle building energy consumption detection data fit well with the holographic mirroring capability of DT technology. This study presents two cases involving the use of UAVs with visible cameras in creating DTs for better monitoring and management of assets, which allows for possible preventive maintenance and cost savings for transportation agencies [111]. Tunnels and roadways have also found uses in this technology. For example, a DT of a tunnel can integrate data on humidity, air quality, and structural stress for real-time condition monitoring. An alert is sent to maintenance teams when the DT detects conditions that could bring about structural damage so that interventions can be carried out much earlier. This approach will not only improve safety but also help maintain the road at a lower cost through reductions in emergency repairs accompanied by unplanned disruptions. Even with several advantages of using DTs in infrastructure maintenance, several barriers still remain. Of these, the foremost is highly accurate and reliable sensor data; faulty or incomplete data leads to erroneous predictions and thus wrong maintenance decisions. A full-scale predictive maintenance system requires a significant investment in sensors, processing infrastructure, and human resources that can understand outputs from the DT and take necessary actions [112]. In light of the aforementioned factors, the solution to these challenges will require the further development of sensor technology,different methods of data processing, and elaboration on standard protocols for predictive maintenance in transport systems. In recent years, rapidly evolving non-destructive testing (NDT) techniques--such as UAV-based inspections, robotic crawlers, and advanced sensing--have significantly enhanced Structural Health Monitoring (SHM) practices for bridges and other critical infrastructure [39; 113]. Unlike traditional SHM, which relies primarily on fixed sensors and periodic manual inspections, these methods facilitate targeted, high-resolution data collection to detect surface cracks, corrosion, spalling, and other localized defects in real time.