This study addresses the severe corrosion issues in the coastal regions of southern China by proposing an improved YOLO v5-GOLD-NWD model. Utilizing corrosion data from the National Center for Materials Corrosion and Protection Science of China, a dataset was constructed for metal-surface corrosion under different protective coatings. This dataset was used for model training, testing, and comparison. Model accuracy was validated using precision, recall, F1 score, and prediction probability. The results demonstrate that the proposed improved model exhibits better identification precision in metal corrosion detection, achieving 78%, a 4% improvement compared to traditional YOLO v5 models. Additionally, through identification and statistical analysis of corrosion image datasets from five types of coated metal specimens, it was found that powder epoxy coating, fluorocarbon coating, epoxy coating, and chlorinated rubber coating showed good corrosion resistance after 24 months of exposure. Conversely, Wuxi anti-fouling coating exhibited poor corrosion resistance. After 60 months of natural exposure, the powder epoxy coating specimens had the highest corrosion occurrence probability, followed by chlorinated rubber coating and epoxy coating, with fluorocarbon coating showing relatively lower probability. The fluorocarbon coating demonstrated relatively good corrosion resistance at both 24 and 60 months of exposure. The findings of this study provide a theoretical basis for enhancing the corrosion protection effectiveness of steel structures in coastal areas.

Coastal areas face severe corrosion issues, posing significant risks and economic losses to equipment, personnel, and the environment. YOLO v5, known for its speed, accuracy, and ease of deployment, has been employed for the rapid detection and identification of marine corrosion. However, corrosion images often feature complex characteristics and high variability in detection targets, presenting significant challenges for YOLO v5 in recognizing and extracting corrosion features. To improve the detection performance of YOLO v5 for corrosion image features, this study investigates two enhanced models: EfficientViT-NWD-YOLO v5 and Gold-NWD-YOLO v5. These models specifically target improvements to the backbone and neck structures of YOLO v5, respectively. The performance of these models for corrosion detection is analyzed in comparison with both YOLO v5 and NWD-YOLO v5. The evaluation metrics including precision, recall, F1-score, Frames Per Second (FPS), pre-processing time, inference time, non-maximum suppression time (NMS), and confusion matrix were used to evaluate the detection performance. The results indicate that the Gold-NWD-YOLO v5 model shows significant improvements in precision, recall, F1-score, and accurate prediction probability. However, it also increases inference time and NMS time, and decreases FPS. This suggests that while the modified neck structure significantly enhances detection performance in corrosion images, it also increases computational overhead. On the other hand, the EfficientViT-NWD-YOLO v5 model shows slight improvements in precision, recall, F1-score, and accurate prediction probability. Notably, it significantly reduces inference and NMS time, and greatly improves FPS. This indicates that modifications to the backbone structure do not notably enhance corrosion detection performance but significantly improve detection speed. From the application perspective, YOLO v5 and NWD-YOLO v5 are suitable for routine corrosion detection applications. Gold-NWD-YOLO v5 is better suited for scenarios requiring high precision in corrosion detection, while EfficientViT-NWD-YOLO v5 is ideal for applications needing a balance between speed and accuracy. The findings can guide decision making for corrosion health monitoring for critical infrastructure in coastal areas.

In concrete block pavement, individual blocks are laid in a predetermined pattern to create a surface interlocked with joint filler that has some discontinuities rather than continuous paving. The overall structural stability is guaranteed by minimizing the rotation and displacement of the blocks. In this paper, dynamic dropped loading tests were conducted to evaluate the load dispersion capacity of concrete block pavement with various block shapes and different laying patterns. The results showed that the vertical deflection increased logarithmically with increasing load pressure. No significant shape-dependent differences in load dispersion were observed, except for the stack pattern. The herringbone pattern showed the best performance of the three different laying patterns that were studied. In all laying patterns, the ratio of stress transferred from the applied pressure on the surface to the base tended to decrease exponentially as the applied pressure increased, consisting of three phases. Whereas the herringbone pattern had the highest load dispersion capacity in Phase 1 (${\sigma }_0<0.2\mathrm{MPa}$ ), the difference between the patterns decreased as the applied pressure increased in Phase 2 ($0.2\mathrm{MPa}\le {\sigma }_0\le 0.6\mathrm{MPa}$ ), and in Phase 3 (${\sigma }_0>0.6\mathrm{MPa}$ ) the ratio became constant even though the applied pressure increased. The block shape did not exert a significant influence on the load distribution capacity.

The successive growth of the aviation industry has progressively heightened the importance of airport pavement management systems. Existing research has primarily focused on the technological advancements of optimization models, with limited applicability in practice. In this study, we introduce condition cases optimization (CCO) to address these limitations while incorporating multi-facility and multi-year network optimization models. We developed condition index, serviceability level, integrated assessment indices and performance models for decision-making criteria. As a result, a practical decision-making strategy was proposed which can flexibly reflect budget constraints. Sensitivity analysis highlighted the impact of initial budget, maintenance methods, costs, and thresholds on decision outcomes. Using a case study, we validated the effectiveness and practicality of the CCO method as an efficient decision-making tool.

Concrete block pavement is used as a modern pavement type owing to its economic benefits. The physical properties, mechanical properties, and functional characteristics of bedding sand and joint sand, the main components of concrete block pavements, should be accurately understood. Owing to the discontinuous structure of these pavements, load transfer efficiency (LTE) is the most important index from the perspective of performance. The factors that affect LTE are bedding sand (type, grading, and thickness), joint sand (type and grading), and concrete block (shape and pattern). Therefore, the optimal design for concrete block pavements can be achieved by analyzing each factor. In this study, three types of sand (river, quartz, and manufactured) were selected to examine the mechanical properties via direct transfer experiments. It was observed that the shear strength of river sand was the highest. It was also found that the difference between the shear strength and the internal friction angle (according to the content of 0.6 mm in the grading of bedding sand) was not large, and the shear strength and the friction angle increased as the content increased to 0.075 mm. In addition, the load transfer characteristics of the joint and rotational interlocking of the block were evaluated through a non-destructive test using the degree of joint filling, block shape, and construction pattern as variables. As a result, the degree of joint filling had the greatest effect on the load transfer characteristics of the joint and rotational interlocking of the block. The effect of the block shape was larger than that of the construction pattern. When a heavy load is applied, the LTE between blocks must be maintained at 50% or more, and the rotation of blocks at 0.6° or less, if 75% or more of the joint must be filled for preventing excessive vertical deformation.