For the quick and accurate determination of the cable force under the hybrid boundary between the ear plate and the anchor plate, this paper proposes a method for identifying the boundary conditions and bending stiffness of the cables based on the frequency method. First, the influence of the parameters, namely, cable boundary conditions, bending stiffness, inclination angle, and linear density, on the cable force was analyzed. Next, the actual bending stiffness of the cable under the boundary conditions of the upper hinged support and the lower solid support and the solid support at both ends were determined, and the cable force in the two states was calculated. Finally, the hydraulic jack tension and the cable force in the two states were compared and analyzed to define the actual boundary conditions and bending stiffness of the cable. The results show that the boundary conditions, bending stiffness, and linear density of the cable considerably influence the cable force, whereas the inclination angle has a negligible effect on the cable force. The bending stiffness under the two boundary conditions of the cable were both 0.12Eimax, and the calculated value of the cable force under the solid support boundary condition at both ends was consistent with the value of the hydraulic jack tension.

A cable tension calculation method based on an influence matrix and the interior point method is proposed for the optimization of cable tension of cable-stayed bridges with multiple performance indicators. Taking a hybrid beam cable-stayed bridge as an example, an objective function based on the bending energy of the tower and beam was established using an influence matrix. Constraints on the range and uniformity of cable force values, the deformation amplitude of the main beam and tower, and the local bending moment of the main beam were established. A cable force optimization model for a completed cable-stayed bridge was formed, and the interior point method was applied to calculate the optimization model. The results show that using an influence matrix and the interior point method to optimize the cable tension of cable-stayed bridges is feasible. The bridge state corresponding to the cable tension solved by this method meets the set evaluation criteria for the bridge state, and some constraint conditions obtain boundary values. This method is simple, practical, and easy to program and implement.

To address the stochastic, nonlinear, and strongly coupled characteristics of multivariate long-term structural response in bridge health monitoring, this study proposes the Informer-SEnet prediction model. The model integrates a Squeeze-and-Excitation (SE) channel attention mechanism into the Informer framework, enabling adaptive recalibration of channel importance to suppress redundant information and enhance key structural response features. A sliding-window strategy is used to construct the datasets, and extensive comparative experiments and ablation studies are conducted on one public bridge-monitoring dataset and two long-term monitoring datasets from real bridges. In the best case, the proposed model achieves improvements of up to 54.67% in MAE, 52.39% in RMSE, and 7.73% in R². Ablation analysis confirms that the SE module substantially strengthens channel-wise feature representation, while the sparse attention and distillation mechanisms are essential for capturing long-range dependencies and improving computational efficiency. Their combined effect yields the optimal predictive performance. Five-fold cross-validation further evaluates the model’s generalization capability. The results show that Informer-SEnet exhibits smaller fluctuations across folds compared with baseline models, demonstrating higher stability and robustness and confirming the reliability of the proposed approach. The improvement in prediction accuracy enables more precise characterization of the structural response evolution under environmental and operational loads, thereby providing a more reliable basis for anomaly detection and early damage warning, and reducing the risk of false alarms and missed detections. The findings offer an efficient and robust deep learning solution to support bridge structural safety assessment and intelligent maintenance decision-making.

Through the “real-time monitoring system,” the inclination sensor can realize the real-time automatic monitoring of the existing lines within the influence scope of the subway structural engineering, which can provide timely and reliable information for the construction unit, so as to evaluate the safety of the project during the construction and the impact of the construction on the existing line, as well as the possible hidden dangers or accidents that may endanger the construction and the environmental safety of the existing line subway. Make timely and accurate forecast, so as to take effective measures to eliminate hidden dangers and avoid accidents.

In bridge health monitoring, structural responses are strongly coupled with temperature effects and vehicle load effects, making it difficult for conventional fixed thresholds and single data-driven approaches to simultaneously achieve environmental adaptability and quantitative reliability assessment. To address this issue, this study proposes a deep-learning-based dynamic early-warning method for bridge structures, using health-monitoring data from an in-service long-span cable-stayed bridge as the research background. First, a two-month mid-span deflection time series is processed using variational mode decomposition optimized by the Porcupine Optimization Algorithm to separate temperature-induced effects. Subsequently, a hybrid prediction model integrating Informer and SEnet is constructed. Temperature and temperature-induced deflection components are used as input features, and a sliding-window strategy is adopted to achieve high-accuracy prediction of the temperature-induced deflection trend, which serves as the time-varying baseline of the dynamic threshold. On this basis, vehicle load effects are modeled by combining Pareto extreme value theory with finite element analysis and superimposed to establish a two-level dynamic early-warning threshold system that satisfies code requirements. Furthermore, a stochastic finite element Monte Carlo method is introduced to probabilistically model uncertainties associated with material parameters, load effects, and model prediction errors. The threshold failure probability at each time instant is taken as the evaluation metric, enabling quantitative characterization of threshold reliability. The results indicate that under combined multiple working conditions, the proposed method reduces the maximum failure probability of the first-level warning by 32.68% and that of the second-level warning by 93.48%, with more stable and consistent probabilistic responses. In engineering applications, simulation experiments based on stochastic traffic loading show that the warning accuracy is improved by up to 19.27%, while the error rate is reduced by up to 16.16%. The study demonstrates that the proposed method possesses a clear physical and statistical foundation as well as good engineering feasibility and provides a viable pathway for transforming bridge early-warning systems from experience-based schemes toward data-driven and risk-oriented frameworks.

This article uses a four-span continuous spatially slanting heterotypic stay cable arch bridge as the background, and establishes finite element models of the main girder using both a single-beam model and a beam-grid model. The calculation results of the two models show that there are certain differences in the deformation and stress of the main girder and arch ribs, but these differences are not significant. Similar projects can refer to the analytical results described in this paper and, considering the actual conditions of bridge engineering, choose and establish appropriate models.