Bridge deck pavement damage has a significant effect on the driving safety and long-term durability of bridges. To achieve the damage detection and localization of bridge deck pavement, a three-stage detection method based on the you-only-look-once version 7 (YOLOv7) network and the revised LaneNet was proposed in this study. In stage 1, the Road Damage Dataset 202 (RDD2022) is preprocessed and adopted to train the YOLOv7 model, and five classes of damage were obtained. In stage 2, the LaneNet network was pruned to retain the semantic segmentation part, with the VGG16 network as an encoder to generate lane line binary images. In stage 3, the lane line binary images were post-processed by a proposed image processing algorithm to obtain the lane area. Based on the damage coordinates from stage 1, the final pavement damage classes and lane localization were obtained. The proposed method was compared and analyzed in the RDD2022 dataset, and was applied on the Fourth Nanjing Yangtze River Bridge in China. The results shows that the mean average precision (mAP) of YOLOv7 on the preprocessed RDD2022 dataset reaches 0.663, higher than that of other models in the YOLO series. The accuracy of the lane localization of the revised LaneNet is 0.933, higher than that of instance segmentation, 0.856. Meanwhile, the inference speed of the revised LaneNet is 12.3 frames per second (FPS) on NVIDIA GeForce RTX 3090, higher than that of instance segmentation 6.53 FPS. The proposed method can provide a reference for the maintenance of bridge deck pavement.

Measuring the impact of extreme winds is important in high-speed railway bridge construction to avoid the risk of engineering accidents. This study presents an early-warning framework for high-speed railway (HSR) bridge construction under extreme wind conditions, using a long-span continuous beam bridge constructed in a typhoon-prone area as a case study. Specifically, on-site wind environment measurements during a typhoon are utilized, and parameters such as fluctuating wind turbulence intensity, gust factor, turbulence integral scale, and power spectral density are employed to characterize the wind environment. Multi-step prediction of gust wind speed during the bridge construction is performed using the hybrid Back Propagation-Genetic Algorithm (BP-GA). Hierarchical warnings and control actions are proposed and implemented based on the prediction results. The analysis results of wind parameters revealed a discrepancy between the measured and specified typhoon turbulence intensities, with the windward turbulence intensity being lower than specified. The longitudinal average gust factor exceeded the transverse value. The prediction curve based on the BP-GA algorithm closely resembles the actual curve, meeting accuracy requirements. Notably, the one-step prediction provided the most accurate results. Based on the predicted wind speed trend for the upcoming half-hour, appropriate hierarchical warnings and control measures were conducted.