Cable-stayed bridge cables experience significant tension over time, making the bridge cables prone to corrosion and fatigue. The direct measurement of cable length is not a standard capability in most current structural health monitoring systems, nor is long-term monitoring of cable changes. Bridge displacements are caused by both dynamic loads (wind and traffic) and quasi-static factors, primarily temperature. This study filtered out dynamic responses by the three-sigma rule, multiple linear regression, interpolation method, and not-a-number calibration. Monitoring data were used to analyze the bridge’s thermal field distribution and the time-dependent variation of tower displacements. Correlation analysis revealed a strong linear correlation between air temperature and quasi-static tower-girder displacements. This research proposes to use the tower-girder distance (effective cable length) to represent the length of the cable, take the thermal expansion coefficient of the effective length of the cable as the quantitative index for long-term monitoring, and take its error as the performance early warning indicator. This method effectively monitors cable health and provides damage warnings.

The displacement of bridge towers is relatively large under strong wind action. Changes in tower displacement can reflect the usage status of the bridge towers. Therefore, it is necessary to conduct performance warning research on tower displacement under strong wind action. In this paper, the triple standard deviation method, multiple linear regression method, and interpolation method are used to preprocess monitoring data with skipped points and missing anomalies. An improved multi-rate data fusion method, validated using simulated datasets, was applied to correct monitoring data at bridge tower tops. The fused data were used to feed predictive models and generate structural performance alerts. Spectral analysis confirmed that the fused displacement measurements achieve high precision by effectively merging the low-frequency GPS signal with the high-frequency accelerometer signal. Structural integrity monitoring of wind-loaded bridge towers used modeling residuals as alert triggers. The efficacy of this proactive monitoring strategy has been quantitatively validated through statistical evaluation of alarm accuracy rates.

When bridge towers are subjected to strong winds, they exhibit significant displacements. This displacement change can serve as an important indicator for structural performance warning. The displacement and acceleration collected in real time by the intelligent bridge monitoring system are disturbed by various noises, resulting in missed alarms in the monitoring system and causing huge economic losses. This study employs the fractional-order Butterworth lowpass filter method, eliminating the maximum value method, triple standard deviation method, etc. for preprocessing abnormal monitoring data characterized by missing values and outlier points. A fractional-order multi-rate Kalman fusion is proposed to process and model the correlation of structural displacement and acceleration data, and the simulated data and measured data are analyzed and verified respectively. Spectral analysis confirmed that by effectively fusing the low-frequency GPS signal with the high-frequency accelerometer signal, the fractional-order multi-rate Kalman fusion displacement measurement has a relatively high accuracy. Displacements obtained by the fractional-order multi-rate Kalman fusion method are adopted for correlation modeling, and residuals generated from this fractional-order fusion modeling are used for structural performance warning testing. The effectiveness of this structural performance warning is quantitatively validated through statistical assessment of warning accuracy.

Gas station canopy grid structures develop local damage during service life, necessitating regular inspection and maintenance to prevent structural collapse. However, conventional field inspection remains inefficient and highly dependent on manual operation. This paper proposes a time domain response sensitivity methodology for damage assessment of structural members in gas station canopy grid structures. The proposed methodology advances time-domain sensitivity analysis to handle spatially complex grid structures with dense spectral characteristics, while proposing a calculation method for implementing intelligent sensing technology in field inspections that enables automated damage localization in practical canopy structures. Through analyzing time domain response sensitivity matrix, an optimal sensor placement method for spatial grid structures is presented. A double-layer spatial grid structure model is constructed to validate the time domain response sensitivity damage identification method and the optimal sensor placement method based on sensitivity analysis. The results show that the time domain response sensitivity damage identification method identifies the member damage in gas station canopy grid structural numerical model with satisfactory accuracy and efficiency, the optimal sensor placement methodology is suitable for damage identification of structural members.

The incorporation of agricultural waste into construction materials represents a promising pathway toward achieving carbon neutrality in the building sector. This study investigates the flexural performance of a novel prefabricated external wall panel composed of corn straw concrete (CSC), an eco-friendly composite material that utilizes waste corn straws. While prior studies have explored rice straw and hemp fiber concrete, they primarily focused on the mechanical properties of these materials rather than the design of prefabricated panels. This study fills the gap by optimizing reinforcement ratio and window opening layout for CSC panels, and validating their structural viability for prefabricated enclosures. An optimal mix proportion was identified, which meets the mechanical requirements for non-load-bearing applications. Four prototype panel specimens were subjected to out-of-plane monotonic loading, considering variables including reinforcement ratio (0.18% vs. 0.24%) and the presence of a window opening (25% area ratio). Results indicated that increasing the reinforcement ratio significantly enhanced the ultimate load capacity by up to 33.3% (from 45 kN to 60 kN)—an enhancement effect that was 12–15% higher than that of reported rice straw concrete. In contrast, the introduction of an opening reduced the ultimate load capacity by 11.1–16.7%. A detailed nonlinear finite element model (FEM) was developed and validated against experimental results. The validation results indicated deflection error of 7.7–12.8% (mean: 9.33%; SD: 2.05), ultimate load error of 7.7–11.1% (mean: 9.48%; SD: 1.32), and a correlation coefficient (R²) of 0.96 between simulated and experimental values. Furthermore, analytical methods for predicting the cracking moment (with an average error of 5.97%) and ultimate flexural capacity, based on yield line theory (with an average error of 8.43%), were proposed and verified. This study demonstrates the structural viability of CSC panels and provides a sustainable solution for waste reduction in prefabricated building enclosures, contributing to greener construction practices.