A Feasibility Study of a Comprehensive Evaluation Method for Bridge Static and Dynamic Performance Based on Trend Analysis of Monitoring Data

To fully leverage structural health monitoring data for bridge condition assessment, this study proposes a comprehensive evaluation method that integrates static and dynamic indicators using monitoring data, and demonstrates its feasibility through a short-term monitoring-based trend analysis on a newly built bridge. First, based on statistical principles, the Weibull distribution is employed to extract the dead-load component from static monitoring data. Building upon this, a static performance evaluation method is established by incorporating spatial uniformity and trend non-uniformity coefficients. Subsequently, spectral analysis is performed on the main girder acceleration data to extract fundamental frequency information and apply temperature correction, establishing a method for assessing the bridge’s dynamic performance. Finally, considering the nonlinear impact of single-indicator deterioration on overall bridge performance, variable weight theory is introduced to construct a static-dynamic integrated assessment model that accounts for indicator equilibrium. Using actual monitoring data from an arch bridge as an example, the bridge’s static, dynamic, and comprehensive scores in the second month after opening were 95.11, 96.21, and 95.66, respectively. Over the following months, all scores remained around 94 points with a gradual decline, confirming the bridge’s good condition and the effectiveness of the proposed comprehensive assessment method for short-term monitoring-based trend analysis. The findings indicate that minor fluctuations in evaluation scores stem from uncertainties in the assessment process, primarily related to traffic randomness, environmental effects, and residual data processing errors. This method enables unified dimensional quantification and trend tracking of a bridge’s static and dynamic performance without requiring bridge closure, providing a quantitative basis for performance comparison and maintenance decision-making during the service phase.