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Monitoring and predicting the dynamic responses of railway bridges under moving trains, including displacement and acceleration, are vital for evaluating the safety and serviceability of the train–bridge system. Traditionally, finite element analysis methods with high computational burden are used to predict the train-induced responses according to the given train loads and, hence, cannot easily be integrated as an available structural-health-monitoring strategy. Therefore, this study develops a novel framework, combining the train–bridge coupling mechanism and deep learning algorithms to efficiently predict the train-induced bridge responses while considering train load duration. Initially, the feasibility of using neural networks to calculate the train–bridge coupling vibration is demonstrated by leveraging the nonlinear relationship between train load and bridge responses. Subsequently, the instantaneous multiple moving axial loads of the moving train are regarded as the equivalent node loads that excite adjacent predefined nodes on the bridge. Afterwards, a deep long short-term memory (LSTM) network is established as a surrogate model to predict the train-induced bridge responses. Finally, the prediction accuracy is validated using a numerical case study of a simply supported railway bridge. The factors that may affect the prediction accuracy, such as network structure, training samples, the number of structural units, and noise level, are discussed. Results show that the developed framework can efficiently predict the train-induced bridge responses. The prediction accuracy of the bridge displacement is higher than that of the acceleration. In addition, the robustness of the displacement prediction is proven to be better than that of the acceleration with the variation of carriage number, riding speed, and measurement noise.

Precise finite element modeling is critically important for the construction and maintenance of long-span suspension bridges. During the process of modeling, shape-finding and model calibration directly impact the accuracy and reliability. Scholars have provided numerous alternative proposals for the shape-finding of main cables in suspension bridges from both theoretical and finite element analysis perspectives. However, it is difficult to apply these solutions to suspension bridges with special components. Seeking a viable solution for such suspension bridges holds practical significance. The Nanjing Qixiashan Yangtze River Bridge is the first three-span suspension bridge in China. To maintain the configuration of the main cable, the suspension bridge is equipped with specialized suspenders near the anchors, referred to as displacement-limiting suspenders. It is the first suspension bridge in China to use displacement-limiting suspenders and their anchorage system. Taking the suspension bridge as a research background, this paper introduces a refined finite element modeling approach considering the effect of geometric nonlinearity. Firstly, based on the loop adjustment and temperature correction, the shape-finding and force assessment of the main cables are carried out. On this basis, a nonlinear finite element model of the bridge was established and calibrated, taking into account factors such as pylon settlement and cable saddle precession. Finally, the static and dynamic characteristics of the suspension bridge were thoroughly investigated. This study aims to provide a reference for the design, construction and operation of the three-span continuous suspension bridge.

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.