Rib-to-deck (RTD) innovative double-sided welded joints have the potential to enhance the fatigue resistance of orthotropic steel decks (OSDs). However, welding residual stress (WRS) has been reported to be a contributing factor to fatigue cracking of steel bridges. This paper performed fatigue crack growth of the RTD innovative double-sided welded joints of OSD considering WRS. Firstly, a thermal-mechanical sequential coupling finite element model (FEM) of the RTD double-sided welded joints was established. The WRS distribution along the direction of deck thickness was analyzed. Secondly, the FEM of the OSD segment with crack was established. The fatigue crack behavior of RTD double-sided welded joints under the combined action of vehicle load and WRS was studied. Finally, the influences of initial crack on fatigue life was analyzed. Results indicate the distribution of the transverse WRS along the direction of deck thickness is tension-compression-tension stress, which has the characteristics of the sinusoidal function. The ∆ K _eff value increases significantly considering WRS than without considering WRS during crack growth. WRS has a significant effect on fatigue life, and the fatigue life of the weld toe without considering WRS is about twice that of considering WRS. The crack depth and aspect ratio ( a ₀ / c ₀ ) of the initial crack affect the fatigue life of RTD double-sided welded joints.

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.

Residual stresses are an inevitable result of the welded fabrication process of corrugated steel webs (CSWs), resulting in structures with high and unpredictable stress fields, causing unexpected failures. The residual stress field is affected by structural parameters and the welding path of CSWs. This study proposes the welding process simulation method for CSWs with element birth and death technology. The optimization design method of heat source parameters is proposed. The feasibility of the simulation method is verified by comparing the numerical results with the experimental results of relevant literature. As a part of the study, a comparison of residual stress fields upon cooling welded CSWs with bending angles of 30, 45, and 60 degrees is presented. Thereafter, the effect of two types of single-sided welding paths and double-sided welding construction processes on residual stress distribution is discussed. Generally, the study results have shown that the equivalent residual stress near the weld reaches the maximum 344 MPa, which is very close to the Q345 steel yield strength. The size of the bending angle has no major effect on the residual stress distribution pattern, but it influences the residual stress value at the bending position. The residual stress at the bending position increases with the bending angle of CSWs. Different welding paths significantly impact the residual stress of the weld toe, and selecting a reasonable welding path can effectively reduce the residual stress value by 20 to 40 MPa.

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.

A cable-supported bridge is usually a key junction of a highway or a railway that demands a higher safety margin, especially when it is subjected to harsh environmental and complex loading conditions. In comparison to short-span girder bridges, long-span flexible structures have unique characteristics that increase the complexity of the structural mechanical behavior. Therefore, the system safety of cable-supported bridges is critical but difficult to evaluate. This study proposes a novel and intelligent approach for system reliability evaluation of cable-supported bridges under stochastic traffic load by utilizing deep belief networks (DBNs). The related mathematical models were derived taking into consideration the structural nonlinearities and high-order statically indeterminate characteristics. A computational framework is presented to illustrate the steps followed for system reliability evaluation using DBNs. In a case study, a prototype suspension bridge is selected to investigate the system reliability under stochastic traffic loading based on site-specific traffic monitoring data. The numerical results indicated that DBNs provide an accurate approximation for the mechanical behavior accounting for structural nonlinearities and different system behaviors, which can be treated as a meta-model to estimate the structural failure probability. The dominant failure modes of the suspension bridge are the fracture of suspenders followed by the bending failure of girders. The degradation of suspenders due to fatigue-corrosion damage has a significant effect on the system reliability of a suspension bridge. The numerical results provide a theoretical basis for the design on cable replacement strategies.

The mechanical behavior of carbonaceous mudstone deteriorates greatly when exposed to wet environments, and the precise evaluation of its slope stability has been a difficulty. This study aims to establish a numerical analysis method for the instability problems of its slopes; this method considers the effects of weathering and water-softening by establishing their mathematical expressions. The weathering and water-softening effects are reflected by variations in the mechanical properties (e.g., elastic modulus, angle of internal friction, and cohesion) of carbonaceous mudstone, with the depth following a logistic function and the shear strength parameters varying with wetting duration and degree of saturation. Their weathering and water-softening effects are reproduced with the use of the ABAQUS finite-element software and MATLAB programming. The proposed numerical method is applied to analyze the seepage field and stability of a highway cut slope with and without protection structures; the application results show that the proposed numerical method is reliable in analyzing the slope’s instability problem. The use of the herringbone skeleton structures can reduce the water-softening effects and thus increase the safety factor of the slope. The findings of this study could provide guidance to the design and construction of highway cut slopes in mountain areas that are rich in carbonaceous mudstone.