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Fully automatic operational modal analysis method based on statistical rule enhanced adaptive clustering method
https://orcid.org/0000-0002-0807-9572
https://orcid.org/0000-0002-2053-4902
Highlights A fully automatic operational modal identification method for a bridge based on KANN-DBSCAN clustering is present. Feasibility of this method was thoroughly validated via an actual bridge under ambient excitation and various manually damage. For better application an executable program with a graphic interface was developed and shared for this method.
Abstract Timely monitoring of modal parameters is commonly adopted to keep in-service bridges safe. For fulfilling the demand, plenty of operational modal analysis (OMA) methods were developed in which only the structural response needs to be measured. However, when utilizing these methods, there are still some deficiencies, like mingled spurious modes and the hyper-parameters needing manual tuning. Although many studies have utilized clustering algorithms to solve these issues and promote the automatic level of the OMA method, some new hyper-parameters belonging to these algorithms would be introduced as well, which also need manual maneuvering. Meanwhile, the performance of various clustering algorithms on this issue also shows variety due to their distinct inductive biases. As a result, the prerequisite of relevant expertise still hinders the common users in their extraction and tracking of the bridge’s modal information. Under these circumstances, after conducting a comparison among representative clustering algorithms, this study proposed an applicable fully automatic OMA method by an adaptive clustering method, “K-average nearest neighbor density-based spatial clustering of applications with noise (KANN-DBSCAN)” enhanced by the five-number summary. This method’s performance was investigated via numerical analyses and the measured data from an actual bridge. The results illustrated that this method functions well in the tested scenarios and has the potential for wide application in actual engineering.
Fully automatic operational modal analysis method based on statistical rule enhanced adaptive clustering method
https://orcid.org/0000-0002-0807-9572
https://orcid.org/0000-0002-2053-4902
Highlights A fully automatic operational modal identification method for a bridge based on KANN-DBSCAN clustering is present. Feasibility of this method was thoroughly validated via an actual bridge under ambient excitation and various manually damage. For better application an executable program with a graphic interface was developed and shared for this method.
Abstract Timely monitoring of modal parameters is commonly adopted to keep in-service bridges safe. For fulfilling the demand, plenty of operational modal analysis (OMA) methods were developed in which only the structural response needs to be measured. However, when utilizing these methods, there are still some deficiencies, like mingled spurious modes and the hyper-parameters needing manual tuning. Although many studies have utilized clustering algorithms to solve these issues and promote the automatic level of the OMA method, some new hyper-parameters belonging to these algorithms would be introduced as well, which also need manual maneuvering. Meanwhile, the performance of various clustering algorithms on this issue also shows variety due to their distinct inductive biases. As a result, the prerequisite of relevant expertise still hinders the common users in their extraction and tracking of the bridge’s modal information. Under these circumstances, after conducting a comparison among representative clustering algorithms, this study proposed an applicable fully automatic OMA method by an adaptive clustering method, “K-average nearest neighbor density-based spatial clustering of applications with noise (KANN-DBSCAN)” enhanced by the five-number summary. This method’s performance was investigated via numerical analyses and the measured data from an actual bridge. The results illustrated that this method functions well in the tested scenarios and has the potential for wide application in actual engineering.
Fully automatic operational modal analysis method based on statistical rule enhanced adaptive clustering method
https://orcid.org/0000-0002-0807-9572
https://orcid.org/0000-0002-2053-4902
Highlights A fully automatic operational modal identification method for a bridge based on KANN-DBSCAN clustering is present. Feasibility of this method was thoroughly validated via an actual bridge under ambient excitation and various manually damage. For better application an executable program with a graphic interface was developed and shared for this method.
Abstract Timely monitoring of modal parameters is commonly adopted to keep in-service bridges safe. For fulfilling the demand, plenty of operational modal analysis (OMA) methods were developed in which only the structural response needs to be measured. However, when utilizing these methods, there are still some deficiencies, like mingled spurious modes and the hyper-parameters needing manual tuning. Although many studies have utilized clustering algorithms to solve these issues and promote the automatic level of the OMA method, some new hyper-parameters belonging to these algorithms would be introduced as well, which also need manual maneuvering. Meanwhile, the performance of various clustering algorithms on this issue also shows variety due to their distinct inductive biases. As a result, the prerequisite of relevant expertise still hinders the common users in their extraction and tracking of the bridge’s modal information. Under these circumstances, after conducting a comparison among representative clustering algorithms, this study proposed an applicable fully automatic OMA method by an adaptive clustering method, “K-average nearest neighbor density-based spatial clustering of applications with noise (KANN-DBSCAN)” enhanced by the five-number summary. This method’s performance was investigated via numerical analyses and the measured data from an actual bridge. The results illustrated that this method functions well in the tested scenarios and has the potential for wide application in actual engineering.
Fully automatic operational modal analysis method based on statistical rule enhanced adaptive clustering method
Zhong, Qiang-Ming (Autor:in) / Chen, Shi-Zhi (Autor:in) / Sun, Zhen (Autor:in) / Tian, Lu-Chao (Autor:in)
Engineering Structures ; 274
01.01.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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