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.

Purpose: The purpose of this article is to analyze the optimization process in depth, elaborating on the components of the entire process and the techniques used. Researchers have been drawn to the expanding trend of optimization since the turn of the century. The rate of research can be used to measure the progress and increase of this optimization procedure. This study is phenomenal to understand the optimization process and different algorithms in addition to their application by keeping in mind the current computational power that has increased the implementation for several engineering applications.Design/methodology/approach: Two-dimensional analysis has been carried out for the optimization process and its approaches to addressing optimization problems, i.e. computational power has increased the implementation. The first section focuses on a thorough examination of the optimization process, its objectives and the development of processes. Second, techniques of the optimization process have been evaluated, as well as some new ones that have emerged to overcome the above-mentioned problems.Findings: This paper provided detailed knowledge of optimization, several approaches and their applications in civil engineering, i.e. structural, geotechnical, hydraulic, transportation and many more. This research provided tremendous emerging techniques, where the lack of exploratory studies is to be approached soon.Originality/value: Optimization processes have been studied for a very long time, in engineering, but the current computational power has increased the implementation for several engineering applications. Besides that, different techniques and their prediction modes often require high computational strength, such parameters can be mitigated with the use of different techniques to reduce computational cost and increase accuracy.

This paper examines the role of Digital Twin Technology (DTT) in transforming infrastructure management, with a focus on sustainability. It highlights how advancements in Artificial Intelligence (AI), Building Information Modeling (BIM), and the Internet of Things (IoT) are driving the effectiveness of Digital Twins in real-world applications. Through detailed case studies, the paper showcases the practical benefits of DTT across various infrastructure sectors. It also evaluates current trends and strategies for enhancing DTT integration into infrastructure systems. The research reveals a striking 80% increase in DTT-related publications from 2019 to 2024, with Asia, particularly China, leading in contributions. The paper concludes by addressing the future potential, challenges, and risks of DTT, offering valuable insights for stakeholders aiming to optimize infrastructure management in the digital era.