## 5 DT Technology in the Lifecycle of Transportation Infrastructure
### Maintenance and Upgrade Phase  
As transportation infrastructure ages, its structural components become increasingly susceptible to degradation caused by environmental exposure and operational loads. Traditional maintenance methods, which rely on scheduled inspections and repairs, often fail to account for the actual conditions of the infrastructure, leading to inefficiencies, excessive costs, and unforeseen breakdowns. Digital twins (DTs) transform this paradigm by continuously collecting real-time sensor data from transportation facilities and integrating it into virtual models. This enables facility managers to monitor real-time operational status, assess degradation patterns, and develop responsive maintenance plans. For example, in bridge health monitoring, the integration of real-time sensor data with predictive models supports condition-based maintenance strategies that enhance efficiency and mitigate the risks of abrupt failures [89]. However, the effectiveness of these strategies depends on the quality and quantity of sensor data, as well as the capacity to process and analyze large datasets in real time. Addressing these challenges is critical to unlocking the full potential of DTs in modernizing infrastructure maintenance and alleviating financial burdens associated with unnecessary repairs. DT technology during the maintenance and upgrade phases offers predictive maintenance capabilities and the assessment of the condition of transportation infrastructure, ensuring more accurate and timely interventions. Predictive maintenance is an advanced concept in maintenance methodology. It provides dynamic analysis and forecasts of condition information for equipment and infrastructure, which enables the identification of problems and the execution of preventive measures before the occurrence of failures. In this way, it prevents the aggravation of problems. DT technology makes use of real-time integration of multi-type sensor data, such as structural stress, vibration, and temperature, to establish a virtual entity model of the infrastructure, simulate its operating conditions, and evaluate its health status [42]. Assisted by machine learning and data mining algorithms, DTs are capable of analyzing this data [90] for trends that could predict potential failure risks. A necessary contribution to bridge and tunnel maintenance is performed by DTs, which provide data analysis retrieved from devices like vibration sensors and strain gauges to detect minor changes in the structure and offer a prognosis on the timing of the appearance of future damage. With such an analysis, maintenance teams can prepare for certain maintenance plans, such as strengthening inspections in some areas or replacing parts in advance to avoid structural failure. This predictive maintenance approach significantly reduces the incidence of emergency repairs and service interruptions from sudden failures, thereby reducing costs not only in repair expenses but also by enhancing safety and stability in the transportation system. During the maintenance and upgrade phase, advanced Structural Health Monitoring (SHM) and non-destructive testing (NDT) techniques are critical for accurately assess ing infrastructure conditions and predicting potential deterioration [91; 92]. For instance, UAV-based crack detection, thermographic imaging, and ultrasonic scanning enable high-resolution data collection from bridge and tunnel structures, ensuring timely interventions and reducing the risk of catastrophic failure. By integrating SHM and NDT datasets into digital twin (DT) platforms, engineers can validate simulation results against real-time measurements, refine predictive maintenance schedules, and optimize resource allocation for refurbishment [93]. Incorporating NDT data--particularly from emerging robotic or drone-based inspection methods--into building information modeling (BIM) and geographic information system (GIS) models significantly enhances condition-based maintenance strategies [94].