Game-based K-means clustering for automated modal parameters identification
GUO Peng1，LI Dongsheng1，GUO Xin2
1.Guangdong Engineering Center for Structure Safety and Health Monitoring, MOE Key Laboratory of
Intelligent Manufacturing Technology, Shantou University, Shantou 515063, China;
2.School of Transportation Institute, Inner Mongolia University, Huhehot 010030, China
Abstract：In order to improve the automation of modal parameter identification based on Stochastic Subspace Identification (Stochastic subspace identification, SSI), a two-stage automatic modal parameter identification algorithm based on game-based k-means (Game-based k-means, GBK) is proposed. Firstly, identify mode candidates from a large number of system orders through SSI for the collected data and remove certainly spurious modes using hard validation criteria. Secondly, the multi type deviation indicator of two adjacent poles is constructed into a feature vector matrix for GBK clustering, and the spurious modes are further eliminated. Then, the extracted structural modes are hierarchically clustered to obtain each order of modes; A six-degree-of-freedom mass-spring structure numerical model is then used to validate the suggested approach. Finally, it is applied to the actual monitoring data of a long-span suspension bridge for automatic modal parameter identification, which further verifies the feasibility and applicability of this method for automatic identification of massive data collected in practical engineering.
[1] Magalhaes F, Cunha A, Caetano E. Online automatic identification of the modal parameters of a long span arch bridge[J]. Mechanical Systems and Signal Processing, 2009, 23(2): 316-329.
[2] Zhang Y, Kurata M, Lynch J P. Long-term modal analysis of wireless structural monitoring data from a suspension bridge under varying environmental and operational conditions: System design and automated modal analysis[J]. Journal of Engineering Mechanics, 2017, 143(4): 04016124.
[3] Yang X M, Yi T H, Qu C X, et al. Automated eigensystem realization algorithm for operational modal identification of bridge structures[J]. Journal of Aerospace Engineering, 2019, 32(2): 04018148.
[4] Qu C X, Yi T H, Li H N. Mode identification by eigensystem realization algorithm through virtual frequency response function. Structural Control and Health Monitoring. 2019 Oct;26(10):e2429.
[5] Reynders E, Houbrechts J, De Roeck G. Fully automated (operational) modal analysis[J]. Mechanical Systems and Signal Processing, 2012, 29: 228-250.
[6] Neu E, Janser F, Khatibi A A, et al. Fully automated operational modal analysis using multi-stage clustering[J]. Mechanical Systems and Signal Processing, 2017, 84: 308-323.
[7] Sun M, Makki Alamdari M, Kalhori H. Automated operational modal analysis of a cable-stayed bridge[J]. Journal of Bridge Engineering, 2017, 22(12): 05017012.
[8] Mao J X, Wang H, Fu Y G, et al. Automated modal identification using principal component and cluster analysis: Application to a long‐span cable‐stayed bridge[J]. Structural Control and Health Monitoring, 2019, 26(10): e2430.
[9] Qu C X, Yi T H, Li H N, et al. Closely spaced modes identification through modified frequency domain decomposition. Measurement. 2018 Nov 1;128:388-92.
[10] 汤宝平，章国稳，陈 卓. 基于谱系聚类的随机子空间模态参数自动识别[J]. 振动与冲击，2012, 31(10): 92-96.
TANG Bao-ping, ZHANG Guo-wen, CHEN Zhuo. Automatic stochastic subspace identification of modal parameters based on the hierarchical clustering method [J]. Journal of vibration and shock, 2012, 31(10): 92-96.
[11] Peeters B, De Roeck G. Reference-based stochastic subspace identification for output-only modal analysis[J]. Mechanical systems and signal processing, 1999, 13(6): 855-878.
[12] Zhao W L, Deng C H, Ngo C W. k-means: A revisit[J]. Neurocomputing, 2018, 291: 195-206.
[13] Rezaee M J, Eshkevari M, Saberi M, et al. GBK-means clustering algorithm: An improvement to the K-means algorithm based on the bargaining game[J]. Knowledge-Based Systems, 2021, 213: 106672.
[14] Nash Jr J F. The bargaining problem[J]. Econometrica: Journal of the econometric society, 1950: 155-162.
[15] Yaghoubi V, Vakilzadeh M K, Abrahamsson T J S. Automated modal parameter estimation using correlation analysis and bootstrap sampling[J]. Mechanical Systems and Signal Processing, 2018, 100: 289-310.
[16] Box G E P, Cox D R. An analysis of transformations[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1964, 26(2): 211-243.
[17] 章关永,朱乐东. 虎门大桥主桥自振特性测定[J]. 同济大学学报：自然科学版, 1999, 27(2):4.
Zhang Guan-yong, Zhu Le-dong, Measurement of natural vibration characteristics of main bridge of Humen Bridge [J]. Journal of Tongji University (Natural Science), 1999, 27(2):4.
