For long-term continuous structural health monitoring to be effective, a process of continuous modal identification that should preferably be automated is required. This case study paper describes a robust, fully automated approach for continuous modal identification and tracking. The approach is demonstrated on a long-span suspension bridge under operational conditions. Contributions are made in three stages: eliminating the spurious modes, extracting the physical modes, and estimating the precision. The proposed approach helps avoid any manual intervention, requires no manually tuned thresholds or prior assumption, and is robust. One week of field monitoring data are analyzed to validate the process. Modal tracking is conducted to show the stability of continuous analysis and to track the evolution of modal parameters. Parametric analysis is conducted to demonstrate the robustness. The case study shows that the proposed approach yields better results than alternative approaches and successfully identifies and tracks multiple closely spaced modes without any manual intervention.

The automated modal identification has been playing an important role in online structural damage detection and condition assessment. This paper proposes an improved hierarchical clustering method to identify the precise modal parameters by automatically interpreting the stabilization diagram. Two major improvements are provided in the whole clustering process. The modal uncertainty is first introduced in the first stage to eliminate as many as possible mathematical modal data to produce more precise clustering threshold, which helps to produce more precise clustering results. The boxplot is introduced in the last stage to assess the precision of the clustering results from a statistical perspective. Based on an iterative analysis of boxplot, the outliers of the clustering results are found out and eliminated and the precise modal results are finally produced. The Z24 benchmark experiment data are utilized to validate the feasibility of the proposed method, and comparison between the previous method and the improved method is also provided. From the result, it can be concluded that the modal uncertainty is more effective than the other modal criteria in distinguishing the mathematical modal data. The modal results by clustering process are not precise in statistic and the boxplot can find out the outliers of the clustering results and produce more precise modal results. The improved automated modal identification method can automatically extract the physical modal data and produce more precise modal parameters.

Vortex-induced vibration (VIV) is a kind of abnormal vibration which needs to be automatically identified and warned in real time to guarantee the operational safety of a bridge. However, the existing VIV identification methods only focus on identification and have limitations in visualizing identification results, which causes difficulty for bridge governors in other fields to quickly confirm the identification results. This paper proposes an automatic VIV identification, warning, and visualization method. First, a recurrence plot is introduced to analyze the signal to extract the characteristics of the vibration signal in a time domain. Then, a feature index defined as recurrence cycle smoothness is proposed to quantify the stability of the vibration signal, based on which the VIV can be automatically identified. An automatic VIV identification and multi-level warning process is finally established based on the severity of the vibration amplitude. The proposed method is validated through a suspension bridge with serious VIVs. The result indicates that the proposed method can automatically identify the VIV correctly without any manual intervention and can visualize the identification results using a graph, providing a good tool to quickly confirm the VIV identification results. The multi-level warning can successfully warn the serious VIV and provide possible early warning for large amplitude VIV.

This paper proposes a Bayesian networks (BNs) method that combines Conditional Probability Table (CPT) compression with an improved Variable Elimination (VE) inference algorithm to improve the efficiency of connectivity reliability assessment for bridge networks. First, a Continuous-Time Markov Chain (CTMC) is used to model the deterioration process of individual bridges, and a series equivalence strategy merges multiple bridges within the same road segment into an "equivalent bridge", thereby obtaining the time-varying failure probability of each edge. Second, a two-layer BNs model with a component layer and a system layer is constructed, and a depth-first search (DFS) connectivity check is performed row by row on the edge state combinations in the CPT to generate the state column of the system-layer node. Third, a hybrid compression algorithm integrating run-length encoding (RLE) and phrase encoding (PE) is proposed to compress the state column into run or phrase tuples, generating a compression entry table, a companion dictionary, and row index lists. Finally, an improved VE inference algorithm is designed, in which mapping rules for variable elimination are defined based on the parity of the start row number and the tuple length, and variable elimination is performed directly on the compressed results. The method is verified through a 5-node 7-edge example and a regional highway bridge network in Shandong, China. Results show that, while maintaining accuracy, the proposed method has significant advantages over the pgmpy method in both inference time and memory usage.

Modal tracking and condition evaluation are two important procedures in modal parameters based bridge condition evaluation, yet they always face the risk of failure due to the spatial aliasing caused by the damage of the sensors and the variation from the noise corruption. This paper proposed an automated modal tracking method and a variation removal method to get precise condition evaluation results. An index defined as modal similarity criterion (MSC) is first proposed to distinguish the modes when spatial aliasing occurs. Then, based on this index, a fully automated modal tracking method is established. An automated baseline mode setting strategy and an adaptive threshold updating strategy are proposed to make the modal tracking process fully automated and adapt to variation and spatial aliasing, even for closely spaced modes. The adaptive bandwidth kernel density estimation (ABKDE) is applied to the identified frequencies to reduce the variation from a statistical perspective to obtain more precise temperature–frequency regression and more precise condition evaluation results. The novelties and feasibilities of the proposed index and modal tracking method are validated through a theoretical analysis and real application to a benchmark bridge with closely spaced modes. The feasibility of the variation removal and condition evaluation method is validated through application to a suspension bridge with 15 years of continuous monitoring. The results indicate that the proposed index and modal tracking method are able to distinguish and automatically track the modes even when the mode shapes are not distinguishable (spatial aliasing occurs). The ABKDE is capable of reducing the variation in frequencies and helps to produce more precise regression and condition evaluation results.