The concept of digital twin (DT) has emerged as a trend in various industries development, enabling the creation of virtual models of physical objects. We conduct a systematic review of the DT technology in the field of transportation infrastructure management from the aspects of concept definition, whole life cycle application, advanced technology, and equipment utilization, as well as the challenges. We begin with an introduction that defines DT and its components, while also distinguishing it from building information modeling and cyber-physical systems. We explore the diverse applications of DT throughout its lifecycle and highlight the significance of DT in structural monitoring, infrastructure operation and maintenance, and dataset expansion. We further investigate the advanced techniques and equipment associated with DT components, focusing on the importance of virtual parts, data acquisition, transmission, multi-source data fusion processing, and data security as well as dynamic updating of models for effective integration and utilization of DT in transportation infrastructure management. We identify key challenges faced by DT in transportation infrastructure management and propose future trends in the study. This comprehensive review serves as a valuable resource for researchers, practitioners, and decision-makers in understanding the potential of DT technology in transportation infrastructure management.

Crack detection at the pixel level across complex scenarios (structural interference and adverse working conditions) is a critical consideration in the maintenance of ballastless track slab (BTS). Although existing deep learning models achieve acceptable accuracy on cracks with a monotonous background, the ground truth with high labor cost is inevitable and their performance in complex scenarios may fall far below their theoretical bounds or even cause “all‐black images.” A hybrid algorithm based on synthetic data from digital twin model and weakly supervised style transfer is proposed in this paper for addressing the above challenges. The algorithm uses a region‐attention strategy to enable the uncontrolled generative adversarial network (GAN) focusing its attention on weak labels containing crack regions, directly obtaining segmentation results with the same style as the ground truth of the crack forest dataset. In addition, a digital twin model that can simulate the real inspection working conditions is established to generate a synthetic crack dataset, enabling the hybrid algorithm to extract the most discriminative features. The results show that the performance of the hybrid algorithm on inspection images across complex scenarios is nearly 25% higher than that of the DeepLabv3+ network, while the time cost consumed is only 0.5% of the latter. The deployment of the region attention strategy also enables the hybrid algorithm to achieve a mean intersection of union (MIoU) of 79.38%, which is nearly twice as much as that of GAN. It not only eliminates the oversegmentation caused by structures such as rails and fastener systems but also overcomes “all‐black images.” In addition, synthetic data can greatly enlarge the range, type, and number of discriminative crack features compared with data augmentation based on limited real data, thus enhancing the performance of the hybrid algorithm for uncertain inspection data. Particularly, the fully trained hybrid algorithm based on the synthetic dataset shows good adaptability and generalization to adverse working conditions such as uneven lighting, noise, and blur.

Terrestrial laser scanning (TLS) technology has the advantages of wide range, high efficiency, and low cost in spatial information collection, so it is widely used in infrastructure monitoring and measurement. During TLS application, the registration and positioning of the point cloud have a direct impact on the quality of the data and the validity of the results. The linear distribution of the tunnel structure and the lack of significant features present challenges in the registration and positioning of 3D point clouds in railway tunnels. The commonly used registration methods are difficult to achieve high registration accuracy and are prone to propagation errors, which reduce the accuracy and effectiveness of results. To achieve accurate registration and positioning of multistation clouds in railway tunnels, we propose a coordinate-based global registration method. To determine the coordinates of scan points in the reference coordinate system and the direction of the reference coordinate system, a few fixed control points are used during the data collection stage. Consequently, each station cloud can be precisely positioned and automatically registered in the reference coordinate system without accumulating or propagating errors. In addition, the coordinate-based registration method eliminates the introduction of errors due to artificial target setting and feature point extraction, as well as the problem of accurately positioning the entire point cloud in the reference coordinate system, thereby enhancing the accuracy, efficiency, and automation levels of cloud registration. The experiment demonstrates that the coordinate-based global registration method is robust and applicable in complex scenes, and it is suitable for the accurate positioning and registration of multistation clouds in linear and curved railway tunnels. The coordinate-based registration method reduces the amount of error in the global registration link by 65% when compared to the point-based registration method, and the point cloud accuracy has reached fine registration, ensuring that fine-grained inverse modeling of the tunnel structure can be performed.

The timely identification of rail internal defects and the application of corresponding preventive measures would greatly reduce catastrophic failures, such as rail breakage. Ultrasonic rail defect detection is the current mainstream rail defect detection method thanks to its advantages of strong penetration, high accuracy, and ease to deploy. The 2D B-scan image output by ultrasonic detectors contains rich features of defects; however, rail engineers manually identify and localize the defect image, which can be time-consuming, and the image may be subject to missing identification or mistakes. This paper adopted state-of-the-art deep learning algorithms for novel B-scan images for the automatic identification and localization of rail internal tracks. First, through image pre-processing of classification, denoising, and augmentation, four categories of defect image datasets were established, namely crescent-shaped fatigue cracks, fishbolt hole cracks, rail web cracks, and rail base transverse cracks; then, four representatives of deep learning object detection networks, YOLOv8, YOLOv5, DETR, and Faster R-CNN, were trained with the defects dataset and further applied to the testing dataset for defect identification; finally, the performances of the three detection networks were compared and evaluated at the data level, the network structure level, and the interference adaptability level, respectively. The results show that the YOLOv8 network can effectively classify and localize four categories of internal rail defects in B-scan images with a 93.3% mean average precision at three images per second, and the detection time is 58.9%, 376.8%, and 123.2% faster than YOLO v5, DETR, and Faster R-CNN, respectively. The proposed approach could ensure the real-time, accurate, and efficient detection and analysis of internal defects to a rail.

Automatic semantic segmentation of point clouds in railway bridge scenes is a crucial step in the digitization process and is required for a variety of subapplications including digital twin reconstruction and component geometric quality verification. This paper details a method for reliably and effectively segmenting point clouds acquired from complex railway bridge scenes by unmanned aerial vehicles (UAVs). The method involves segmenting seven common infrastructure elements in railway bridge point clouds using an improved DGCNN after processing low-quality point clouds from UAVs with a score-based denoising algorithm. The segmentation performance of the network is measured by averaging the intersection to union ratio between the segmentation results and the true labels of different elements, i.e., the mean intersection over union (mIoU). The proposed method is evaluated on three different scenes of railway bridges and achieved mIoU values of 99.18%, 90.76%, and 85.84%, respectively, at three levels of complexity ranging from easy to difficult. The results demonstrate that the proposed method captures the most discriminative features from low-quality point clouds, allowing for the accurate and efficient digital representation of railway bridge scenes.