Steel beams are widely used load-bearing components in bridge construction. They are prone to internal stress concentration under low-frequency vibrations caused by natural disasters and adverse loads, leading to microcracks and fractures, thereby accelerating the instability of steel components. Therefore, dynamic stress monitoring of steel beams under low-frequency vibrations is crucial to ensure structural safety. This study proposed an external stress sensor based on PMN-PT material. The sensor has the advantages of high sensitivity, comprehensive frequency response, and fast response speed. To verify the accuracy and feasibility of the sensor in actual engineering, the LETRY universal testing machine and drop hammer impact system were used to carry out stress monitoring tests and finite element simulations on scaled I-shaped steel beams with PMN-PT sensors attached. The results show that: (1) The PMN-PT sensor has exceptionally high sensitivity, maintained at 1.716~1.726 V/MPa in the frequency range of 0~1000 Hz. The sensor performance is much higher than that of PVDF sensors with the same adhesive layer thickness. (2) Under low-frequency random vibration, the sensor’s time domain and frequency domain output voltages are always consistent with the waveform of the applied load, which can reflect the changes in the structural stress state in real time. (3) Under the impact of a drop hammer, the sensor signal response delay is only 0.001 s, and the sensitivity linear fitting degree is above 0.9. (4) The simulation and experimental results are highly consistent, confirming the superior performance of the PMN-PT sensor, which can be effectively used for stress monitoring of steel structures in low-frequency vibration environments.

Establishing an accurate vehicle fatigue load spectrum is a critical prerequisite for fatigue life analysis and design of highway bridges. However, the time-varying and regional characteristics of vehicle loads pose significant challenges to achieving this goal. This study focuses on vehicle data collected by a weigh-in-motion system installed on a highway bridge in Chongqing, China. The statistical characteristics of vehicle-load-related parameters are analyzed, and the actual vehicle fatigue load spectrum for this section of the road is established. Specifically, vehicles are first categorized based on axle count characteristics. Then, statistical analyses are conducted on key parameters such as vehicle weight, headway time, and axle load for each vehicle type. Finally, the actual vehicle fatigue load spectrum is developed based on Miner’s linear damage rule and the equivalent fatigue damage principle, and the contributions of different vehicle types to fatigue damage are investigated. The results show that the weight distributions of different vehicle types follow a Gaussian mixture distribution, while the headway time distribution for each lane follows a log-normal distribution. A linear approximate relationship was observed between the axle loads of different vehicle types and their respective total weights. Although two-axle trucks exhibited higher frequencies, six-axle trucks contributed the most to structural fatigue damage, accounting for 53.81%. Therefore, six-axle trucks can be regarded as the standard fatigue vehicle model for this section of the road. These findings provide valuable insights for fatigue design and fatigue life assessment of highway bridges under similar vehicle loading conditions.

Accurately reconstructing consecutive missing measurement segments presents a major challenge in bridge structural health monitoring, due to the inherent complexity and non-stationary nature of the collected signals. To address this, a hybrid data recovery framework is developed by integrating Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Permutation Entropy, Long Short-Term Memory networks, and the Informer model (ICEEMDAN-PE-LSTM-Informer). The process begins by employing ICEEMDAN-PE to decompose the raw monitoring data into multiscale intrinsic mode components with distinct temporal characteristics. Subsequently, a parallel LSTM-Informer architecture is designed to forecast each decomposed component independently. This dual-branch design combines the local temporal modeling capability of LSTM with the long-range dependency modeling capability of Informer, and the extracted features are fused through a fully connected layer before prediction. The final recovered data sequence is obtained by linearly aggregating the predicted results from all components. The proposed method is evaluated using complete bearing-displacement and strain records from an operational bridge SHM system. Because the original datasets contain no missing observations, consecutive missing intervals are artificially constructed in the test sequences by controlled masking, allowing the original measurements to serve as ground truth for quantitative evaluation. Experimental results demonstrate improved reconstruction accuracy for the two bridge-monitoring datasets considered in this study. Specifically, for the longest prediction horizon of 72 steps, corresponding to a 12 h consecutive missing interval, the RMSE values for recovering bearing displacement and strain data are decreased by 54.06% and 44.11%, respectively, compared to a standard LSTM model, and by 46.06% and 31.47% compared to the standalone Informer model. These findings indicate that the proposed ICEEMDAN-PE-LSTM-Informer framework provides improved reconstruction performance over the considered baseline models for the two structural-response datasets investigated in this study.