Numerical modeling approaches are favored for performing long-term analyses of continuous prestressed concrete rigid-frame (CPCR) bridges due to the complexity and high cost of experimental testing on such structures. In this study, a refined numerical modeling approach is first presented and validated by comparing the field monitor data of an existing long-span CPCR bridge in China. Then, long-term deflection analysis—considering box girder cracks, concrete creep, joint damage behavior and prestress—is conducted based on the proposed refined modeling approach. It is found that the time-dependent loss of longitudinal prestress has the most significant influence on the long-term structural stiffness, while joint damage between different segments has limited impact on overall structural performance, especially for large-span bridge cases. The local stress distribution is significantly influenced by typical damage, albeit with a different scope of impact. Therefore, targeted reinforcement has to be performed to achieve satisfactory repair results under different damage conditions.

In cold regions, the power generation efficiency of wind turbines is affected by blade icing. Heavy icing on blades will change the aerodynamic configuration of the blades and can even cause blades to crack or break. Therefore, monitoring and deicing technologies are important for the safe operation of wind turbines. This study proposes a novel strain ratio index based on mechanical analysis of icing, which causes the neutral axis shift and different strain ratio change between waving and shimmy directions. Data from the 5 kW wind turbine blade model in a low-temperature laboratory and the 1.5 MW full-scale field wind turbine monitoring over 1 year are used to validate the effectiveness of the proposed method. The proposed strain ratio index and icing detection criteria are derived from mechanical analysis with clear interpretability while reducing ambiguity from structural damage. The relationship between the strain ratio index and ice thickness is quantified through laboratory tests and validated by field applications, demonstrating the effectiveness and robustness under complex real-world service scenarios.

With the widespread implementation of bridge structural health monitoring (SHM) systems, monitored bridge networks have gradually formed. Understanding vehicle loads and considering spatio-temporal correlations within bridge networks is critical for structural condition assessment and maintenance decision making. This study aims to predict traffic flows by investigating traffic flow correlations within a bridge network using multi-bridge data, thereby supporting bridge network-level SHM. A transformer-based traffic flow prediction model considering spatio-temporal correlations of bridge networks (ST-TransNet) is proposed. It integrates external factors (processed via fully connected networks) and multi-period traffic flows of input bridges (captured by self-attention encoders) to generate traffic flow predictions through a self-attention decoder. Validated using weigh-in-motion data from an 8-bridge network, the proposed ST-TransNet reduces prediction root mean square error (RMSE) to 12.76 vehicles/10 min, outperforming a series of baselines—SVR, CNN, BiLSTM, CNN&BiLSTM, ST-ResNet, transformer, and STGCN—with significant relative reductions of 40.5%, 36.9%, 36.6%, 37.3%, 35.6%, 31.1%, and 22.8%, respectively. Ablation studies confirm the contribution of each component of the external factors and multi-period traffic flows, particularly the recent traffic flow data. The proposed ST-TransNet effectively captures underlying the spatio-temporal correlations of traffic flow within bridge networks, offering valuable insights for enhancing bridge assessment and maintenance.

Bridges are critical components of transport networks, yet they inevitably deteriorate over time. Damage in a bridge reduces its stiffness, altering both its static and dynamic responses to loading. This change also affects the dynamic excitation of the loading source, such as a passing vehicle, meaning that vehicle vibrations can be used to infer bridge damage. Deflections under a moving instrumented axle are influenced by both bridge and vehicle properties, making them unique to each vehicle. To extract meaningful information about bridge conditions, these data must therefore be transformed into a form independent of vehicle characteristics. The bridge influence line, defined as the response to a unit moving load, provides a reliable descriptor of structural behaviour that is unaffected by vehicle properties. In this study, a fleet of vehicles is simulated to generate acceleration signals, from which bridge influence lines are derived using the inverse Newmark–beta method. Two damage indicators are proposed: D1, derived from the mid-span point of the mean Moving Reference Influence Line (MRIL), and D2, defined as the area under the mean MRIL curve. Results show that both indicators can effectively detect global bridge damage. D2 exhibits a clear linear relationship with stiffness loss and remains stable under varying speeds and noise levels, whereas D1 is more sensitive to factors such as measurement noise, vehicle speed, and variations in vehicle properties, but remains reliable at lower speeds and moderate noise levels. Measurement errors are simulated by adding random noise to the acceleration inputs, and the fleet monitoring approach is shown to mitigate these effects, enhancing the overall accuracy and robustness of bridge damage detection.

Flood factors are key parameters affecting the hydraulic characteristics at a bridge site. Clarifying the development patterns and impacts of flood factors at a bridge site is of great significance for the stability assessment and service safety of bridges. Therefore, the impact analysis of flood factors, i.e., the initial flow velocity, scour angle, scour depth, on the evolution pattern of hydraulic characteristics at a bridge site is numerically conducted in this research. The structural model and hydraulic model are first established for numerical analysis. The correctness of models is verified through the existing analytical formula and experimental result. The evolution patterns of hydraulic characteristics at a bridge site are systematically evaluated. The results show that the flow velocity at the upstream side of the bridge exhibits relatively uniform and continuous distribution in both transverse and vertical directions, primarily caused by the obstruction of bridge piers. At the downstream side of the bridge, the flow velocity presents non-uniform development, induced by the horseshoe vortex and flow disturbance near the upstream pier. The coupling effect of the lateral flow velocity component suppressing vertical vortices and complex flow separation induces the distinct flow velocity distributions. Scour depth alters the flow field patterns, thereby influencing the flow’s resistance and acceleration properties. The above development pattern of hydraulic characteristics at the bridge site holds practical significance for conducting detailed analyses of flood impacts on bridges.

A massive number of early-constructed small-to-medium-span bridges are collectively entering an “aging” phase in China. Meanwhile, vast amounts of unstructured bottom-level inspection texts remain underutilized. To address them, this paper proposes a data governance method. Large Language Models were leveraged to process unstructured defect data from 18,238 real-world bridges nationwide. The data were structurally cleaned and mapped into discrete features, revealing multidimensional vulnerabilities. On this basis, the Stable Contrastive Pattern Risk (SCPR) intelligent decision-making model was developed. The results demonstrate that, following robust filtration, 6 nationwide common risk rules were extracted from 2064 initial candidate combinations. These rules converge into three core risk patterns: the heavy-duty aging pattern, the substructure-dominated pattern, and the over-water small-span low-seismic-design pattern. Guided by these robust rules and specific damage enrichment characteristics, risk stratification and differentiated management strategies were further formulated for Class III bridges. This research facilitates a paradigm shift in bridge maintenance. It moves from reactive, post-event symptom characterization toward data-driven, proactive early warnings. This shift provides a substantive scientific foundation for optimizing resource allocation and enabling precise investment decisions at the road network level.