Abstract
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.
Highlights
During the last couple of decades, structure health monitoring has been developing rapidly in the area of civil engineering [1,2,3]
The data spots representing the physical modal frequencies at different system orders look like several vertical lines because physical modes stabilize for system orders, while those spurious modal frequencies look scattered. e stabilization diagram provides a specific instruction that the modal data forming those vertical lines are true modal results and should be picked out. us, the modal identification process is transformed into the process of extracting the vertical lines in a stabilization diagram
The only input, i.e., the cutoff distance, is automatically calculated based on the df and MAC values of all the remaining probably physical modal results and is recommended to be calculated as d μp + σp, (16)
Summary
During the last couple of decades, structure health monitoring has been developing rapidly in the area of civil engineering [1,2,3]. Neu et al [19] later improved the whole clustering process and claimed that the parameters used in discriminating the mathematical modal data should be carefully selected and pointed out that the precision of the automatically calculated clustering threshold in [16] is challenging. Sun et al [20] proposed an approach to determine the clustering threshold and applied it to a cablestayed bridge to automatically identify the modal parameters, but it lacks some physical explanation Another limitation of all the automated modal identification methods is that the precision of the clustering results cannot be assessed. An improved hierarchical clustering process is proposed to automatically identify the modal parameters and estimate the precision of the clustering results. The Z24 benchmark data are utilized to prove the applicability of the proposed method, and the comparison between the results of the improved method and the previous method is provided
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