Weld defects such as porosity, inclusion, burn-through, and lack of penetration are difficult to detect and control effectively in an orthotropic steel deck (OSD), which will be a fatigue crack initiation site and lead to several fatigue cracking. The crack growth behavior in defective welded joints is different from that of defect-free joints. This study investigates crack–inclusion interaction for rib-to-deck welded joints in OSDs based on numerical simulation and linear elastic fracture mechanics (LEFM). A refined finite element model of a half U-rib with cracks and inclusions was established by using the FRANC3D-ABAQUS interactive technology. The full processes of the crack–inclusion interaction from approaching and penetrating were accurately simulated. Critical parameters, including the stress intensity factor (SIF), the shape factor, the growth rate, and the growth direction were analyzed. The stiff and soft inclusions amplify and shield the SIF of cracks when the crack grows to the local area of inclusions. During the entire process of crack growth, the soft and stiff inclusion accelerate and inhibit the crack growth, respectively. The stiff inclusion will lead to asymmetric growth of the crack shape, where the portion of the crack away from the inclusions has a higher growth rate. The soft and stiff inclusions will attract and repel the direction of crack growth at the proximal point, respectively.

The deflection control of the main girder in suspension bridges, as flexible structures, is critically important during their operation. To predict the vertical deflection of existing suspension bridge girders under the combined effects of stochastic traffic loads and environmental temperature, this paper proposes an integrated deflection interval prediction method based on a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), a probability density estimation layer, and bridge monitoring data. A time-series training dataset consisting of environmental temperature, vehicle load, and deflection monitoring data was built based on bridge health monitoring data. The CNN-LSTM combined layer is used to capture both local features and long-term dependencies in the time series. A Gaussian distribution (GD) is adopted as the probability density function, and its parameters are estimated using the maximum likelihood method, which outputs the optimal deflection prediction and probability intervals. Furthermore, this paper proposes a method for identifying abnormal deflections of the main girder in existing suspension bridges and establishes warning thresholds. This study indicates that, for short time scales, the CNN-LSTM-GD model achieves a 47.22% improvement in Root Mean Squared Error (RMSE) and a 12.37% increase in the coefficient of determination (${\mathrm{R}}^2$) compared to the LSTM model. When compared to the CNN-LSTM model, it shows an improvement of 28.30% in RMSE and 6.55% in ${\mathrm{R}}^2$. For long time scales, the CNN-LSTM-GD model shows a 54.40% improvement in RMSE and a 10.22% increase in ${\mathrm{R}}^2$ compared to the LSTM model. Compared to the CNN-LSTM model, it improves RMSE by 38.43% and $R^2$ by 5.31%. This model is instrumental in more accurately identifying abnormal deflections and determining deflection thresholds, making it applicable to bridge deflection early-warning systems.

The fatigue cracking of orthotropic steel bridge decks (OSDs) is a difficult problem that hinders the development of steel structures. The most important reasons for the occurrence of fatigue cracking are steadily growing traffic loads and unavoidable truck overloading. Stochastic traffic loading leads to the random propagation behavior of fatigue cracks, which increases the difficulty of the fatigue life evaluations of OSDs. This study developed a computational framework for the fatigue crack propagation of OSDs under stochastic traffic loads based on traffic data and finite element methods. Stochastic traffic load models were established based on site-specific, weigh-in-motion measurements to simulate fatigue stress spectra of welded joints. The influence of the transverse loading positions of the wheel tracks on the stress intensity factor of the crack tip was investigated. The random propagation paths of the crack under stochastic traffic loads were evaluated. Both ascending and descending load spectra were considered in the traffic loading pattern. The numerical results indicated that the maximum value of K_I was 568.18 (MPa·mm^1/2) under the most critical transversal condition of the wheel load. However, the maximum value decreased by 66.4% under the condition of transversal moving by 450 mm. In addition, the propagation angle of the crack tip increased from 0.24° to 0.34°—an increase ratio of 42%. Under the three stochastic load spectra and the simulated wheel loading distributions, the crack propagation range was almost limited to within 10 mm. The migration effect was the most obvious under the descending load spectrum. The research results of this study can provide theoretical and technical support for the fatigue and fatigue reliability evaluation of existing steel bridge decks.

Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. Traditional machine learning techniques rely heavily on feature extraction, where weak feature extraction can lead to suboptimal features and poor classification performance. In contrast, ML-based methods, particularly deep learning approaches like convolutional neural networks (CNNs), automatically extract relevant features from raw data, improving the accuracy and adaptability of the damage identification process. This study developed a time–frequency-based data-driven approach aiming to improve the effectiveness of traditional data-driven structural damage identification approaches for large complex structures. Firstly, the structural acceleration signals in the time domain were converted into two-dimensional images via the Gram angle difference field (GADF). Subsequently, the characteristic feature in the image data was studied by convolutional neural networks (CNNs) to predict the structural damage conditions. An experimental study on a scale model of a cable-stayed bridge was conducted to identify the damage of stay cables under the moving vehicle load on the main girders. The CNN was employed to extract the characteristic features from the time-varying monitoring data of vehicle–bridge interactions. The CNN parameters were optimized to conduct the structural damage classification task. The performance of the proposed method was evaluated by comparing it with various traditional pre-trained networks. The effect of environmental noise on the prediction accuracy was also investigated. The numerical results show that the ResNet model has the best performance in terms of damage identification accuracy and convergence speed, achieving higher accuracy and faster convergence compared to the other four traditional networks. The method can accurately identify damage on bridges using insufficient sensors on the bridge deck, which has valuable potential for application to real-world bridges with monitoring data. As the Signal-to-Noise Ratio (SNR) decreases from 20 dB to 2.5 dB, the prediction accuracy of ResNet decreases from 86.63% to 62.5%, which demonstrates the robustness and reliability in identifying structural damage.

Precast concrete segmental bridges (PCSBs) with hybrid tendons may be the most competitive solution for achieving the advantages of rapid construction and favorable structural performance. Therefore, the flexural behavior of precast concrete segmental bridges (PCSBs) with unbonded tendons and epoxy joints was experimentally investigated in this study, and the effects of the joint types were recorded. Investigations were carried out on the ultimate loads, prestressed strand stresses, deflections, as well as failure modes, while an unbonded monolithic beam was tested for comparison. In addition, the strain measurement proved that the average strains agree with the assumption of plane section, regardless of whether the joints were set. The flexural strengths of prefabricated components were 9~15% lower than those of the monolithic beams with unbonded tendons. Meanwhile, the shape of the joints also influenced the flexural bearing capacity; the bearing capacity of the dual-tooth joint beam was 4.5% lower than that of the single-tooth one, and the bearing capacity of the flat butt joint member was 5.7% lower than that of the dual-tooth joint beam. Moreover, the experimental deflection curve and ultimate bearing capacity of the models with different shear keys showed a good correlation with the FE results. These research outcomes will aid in comprehending the roles of joints in the flexural behaviors of precast UHPC segmental bridges.