Distributed optical fiber sensing (DOFS) is an advanced tool for structural health monitoring (SHM), offering high precision, wide measurement range, and real-time as well as long-term monitoring capabilities. It enables real-time monitoring of both temperature and strain information along the entire optical fiber line, providing a novel approach for safety monitoring and structural health assessment in transportation engineering. This paper first introduces the fundamental principles and classifications of DOFS technology and then systematically reviews the current research progress on Brillouin scattering-based DOFS. By analyzing the monitoring requirements of various types of transportation infrastructure, this paper discusses the applications and challenges of this technology in SHM and damage detection for roads, bridges, tunnels, and other infrastructure, particularly in identifying and tracking cracks, deformations, and localized damage. This review highlights the significant potential and promising prospects of Brillouin scattering technology in transportation engineering. Nevertheless, further research is needed to optimize sensing system performance and promote its widespread application in this field. These findings provide valuable references for future research and technological development.

The attributes of diversity and concealment pose formidable challenges in the accurate detection and efficacious management of distresses within subgrade structures. The onset of subgrade distresses may precipitate structural degradation, thereby amplifying the frequency of traffic incidents and instigating economic ramifications. Accurate and timely detection of subgrade distresses is essential for maintaining and repairing road sections with existing distresses. This helps to prolong the service life of road infrastructure and reduce financial burden. In recent years, the advent of numerous novel technologies and methodologies has propelled significant advancements in subgrade distress detection. Therefore, this review delineates a concentrated examination of subgrade distress detection, methodically consolidating and presenting various techniques while dissecting their respective merits and constraints. By furnishing comprehensive guidance on subgrade distress detection, this review facilitates the expedient identification and targeted treatment of subgrade distresses, thereby fortifying safety and enhancing durability. The pivotal role of this review in bolstering the construction and operational facets of transportation infrastructure is underscored.

Balancing detection performance and computational efficiency is critical for sustainable pavement disease detection in energy-constrained scenarios. However, existing visual methods often struggle to adapt to structural transformations and capture critical features of pavement diseases in complex environments, while their computational demands can be resource-intensive. To address these challenges, this paper proposes a structure perception and feature attention network (SPFAN). The network includes a structure perception module that employs the updated deformable convolution technique. This technique enables the model to dynamically adjust and focus on the actual pavement disease regions, improving the accuracy of feature extraction, especially for diseases with irregular shapes and sizes. Additionally, the convolutional block attention module (CBAM) is integrated to optimize feature map attention across channel and spatial dimensions, enhancing the model focus on critical disease features without significantly increasing complexity. To further improve robustness, the generalized intersection over union (GIoU) loss function is adopted, ensuring better stability across targets of varying shapes and sizes. Experimental results on real-world pavement disease images show that the mAP@0.5 of the proposed SPFAN increases from 66.2% to 71.2%, an improvement of 7.55%, while the F1-score also increases by 9.03%, compared to the baseline YOLOv8n model. Furthermore, while achieving significant accuracy improvements, the proposed method maintains a similar parameter count as the baseline, preserving its low computational demands and high efficiency, making it suitable for real-time pavement damage detection in energy-constrained environments.

Asphalt mixtures exhibit complex mechanical behaviors due to their multiphase internal structures. To provide better characterizations of asphalt pavements under various forms of potential distress, a two-dimensional (2D) finite element simulation based on images of asphalt mixtures can be used to increase computational efficiency and reduce labor consumption. Nonetheless, using a representative image to eliminate the influence of dimension reduction from three dimensions to two dimensions is of great significance for attaining a reliable simulation result. Therefore, in this study, we investigated the consequence of dimension reduction for open-graded asphalt mixtures (denoted as OGFC-16), including a comprehensive characterization of these 2D models in terms of their morphologies and the similarities between them. This study aimed to reveal the variation in a 2D finite element simulation when applied to open-graded asphalt mixtures. Structural compositions, gradations, the aspect ratios of aggregates, and aggregate orientations were counted and calculated. In addition, the cosine similarity and structural similarity index measure (SSIM) were also calculated. Consequently, we performed a statistical analysis on the aforementioned indicators to quantitatively identify the discrepancy in the 2D images caused by dimension reduction. The results demonstrate that this 2D simulation might not be sufficient for representing the realistic mechanical performance of asphalt mixtures due to the remarkable variations in the image morphologies in different 2D images. However, the basic rules of stress behavior within structures can be accurately simulated. A compensative methodology for conducting a 2D simulation of open-graded asphalt mixtures should be based on a morphological characterization, considering structural compositions and the structural similarity index measure.

Patching is a common technology used in repairing asphalt-pavement potholes. Due to the differences in material properties between patched- and unpatched-asphalt mixtures, significant strain and stress concentrations could be induced; thus, further cracks and interfacial debonding distress could be caused. As a remedy, the strain and stress concentrations can be alleviated by utilizing optimum patching shapes. Therefore, this paper employed finite element methods (FEM) to deeply analyze the mechanical performance of patched-asphalt pavements embedded with different patching shapes. Three patching shapes, these being rectangular, stair, and trapezoid, were considered for use in pavement pothole repairs based on two- and three-dimensional finite element models. In the two-dimensional models, Top-Down and Bottom-Up crack propagations were simulated to assess the anti-damage performance of the patched pavements with different patching shapes. In addition, the thermal stress behaviors within patched-asphalt pavements were simulated using the two-dimensional model to analyze the performance of the patched pavements during the cooling process in construction. In addition, interface-debonding performance was simulated for the patched-asphalt pavements using three-dimensional models. In light of the simulation results, engineers are expected to better understand the mechanism within patched pavements and to improve the quality of the pavement patching.