A three-stage automated modal identification framework for bridge parameters based on frequency uncertainty and density clustering

Wei, 2010, Vibration-based damage identification methods: a review and comparative study, Struct Health Monit, 10, 83 Ewins, 2000 Schipfors, 2014 Overschee PV, Moor BLD. Subspace identification for linear systems theory — implementation — applications. US: Springer; 1996. Peeters, 1999, Reference-based stochastic subspace identification for output-only modal analysis, Mech Syst Signal Process, 13, 855, 10.1006/mssp.1999.1249 Yang, 2019, Automated eigensystem realization algorithm for operational modal identification of bridge structures, J Aerosp Eng, 32, 04018148, 10.1061/(ASCE)AS.1943-5525.0000984 Zhang, 2014, Automated eigensystem realisation algorithm for operational modal analysis, J Sound Vib, 333, 3550, 10.1016/j.jsv.2014.03.024 Magalhães, 2009, Online automatic identification of the modal parameters of a long span arch bridge, Mech Syst Signal Process, 23, 316, 10.1016/j.ymssp.2008.05.003 Mao, 2018, Investigation of dynamic properties of long-span cable-stayed bridges based on one-year monitoring data under normal operating condition, Struct Control Health Monit, 25, e2146, 10.1002/stc.2146 Reynders, 2008, Uncertainty bounds on modal parameters obtained from stochastic subspace identification, Mech Syst Signal Process, 22, 948, 10.1016/j.ymssp.2007.10.009 Reynders, 2012, Fully automated (operational) modal analysis, Mech Syst Signal Process, 29, 228, 10.1016/j.ymssp.2012.01.007 Lanslots J, Rodiers B, Peeters B. Automated pole-selection: proof-of-concept & validation. In: Proc ISMA2004 Int Conf Noise Vib Eng. Leuven, Belgium; 2004. p. 1005–18. Scionti M, Lanslots J, Goethals I, Vecchio A, Van der Auweraer H, Peeters B et al. Tools to improve detection of structural changes from in-flight flutter data. In: Proc Eighth Int Conf Recent Adv Struct Dyn. Southampton, UK; 2003. Hasan, 2019, Cluster analysis for automated operational modal analysis: a review, MATEC Web Conf, 255, 02012, 10.1051/matecconf/201925502012 Sun, 2017, Automated operational modal analysis of a cable-stayed bridge, J Bridge Eng, 22, 05017012, 10.1061/(ASCE)BE.1943-5592.0001141 He, 2021, Fully automated precise operational modal identification, Eng Struct, 234, 111988, 10.1016/j.engstruct.2021.111988 Diez, 2016, A clustering approach for structural health monitoring on bridges, J Civil Struct Health Monit, 6, 429, 10.1007/s13349-016-0160-0 Goethals, 2004, Reliable spurious mode rejection using self learning algorithms, ISMA Yi, 2019, Clustering number determination for sparse component analysis during output-only modal identification, J Eng Mech, 145, 04018122, 10.1061/(ASCE)EM.1943-7889.0001557 Ester, 1996, A density-based algorithm for discovering clusters in large spatial databases with noise, 226 Allemang R, Brown DL. A correlation coefficient for modal vector analysis. In: Proceedings of the First International Modal Analysis Conference. Orlando, FL; 1982. p. 110–60. Pappa, 1993, Consistent-mode indicator for the eigensystem realization algorithm, J Guid Control Dyn, 16, 852, 10.2514/3.21092 Greś, 2021, Uncertainty quantification for the Modal Phase Collinearity of complex mode shapes, Mech Syst Signal Process, 152, 107436, 10.1016/j.ymssp.2020.107436 Reynders, 2008, Reference-based combined deterministic–stochastic subspace identification for experimental and operational modal analysis, Mech Syst Signal Process, 22, 617, 10.1016/j.ymssp.2007.09.004 Reynders, 2016, Uncertainty quantification in operational modal analysis with stochastic subspace identification: Validation and applications, Mech Syst Signal Process, 66-67, 13, 10.1016/j.ymssp.2015.04.018 Verboven, 2002, Autonomous structural health monitoring —part I: modal parameter estimation and tracking, Mech Syst Signal Process, 16, 637, 10.1006/mssp.2002.1492 Döhler, 2013, Efficient multi-order uncertainty computation for stochastic subspace identification, Mech Syst Signal Process, 38, 346, 10.1016/j.ymssp.2013.01.012 Xie, 2016, Modal parameter identification of flexible spacecraft using the covariance-driven stochastic subspace identification (SSI-COV) method, Acta Mech Sin, 32, 710, 10.1007/s10409-016-0579-x Yang, 2020, Modal identification of high-speed railway bridges through free-vibration detection, J Eng Mech, 146, 04020107, 10.1061/(ASCE)EM.1943-7889.0001847 Döhler, 2014, Structural health monitoring with statistical methods during progressive damage test of S101 Bridge, Eng Struct, 69, 183, 10.1016/j.engstruct.2014.03.010 Su, 2020, Automatic identification of modal parameters for structures based on an uncertainty diagram and a convolutional neural network, Structures, 28, 369, 10.1016/j.istruc.2020.08.077 Golub, 2013 Pintelon, 2007, Uncertainty calculation in (operational) modal analysis, Mech Syst Signal Process, 21, 2359, 10.1016/j.ymssp.2006.11.007 Ml, 2001, Output-only subspace-based structural identification: from theory to industrial testing practice, J Dyn Syst Meas Control, 123, 668, 10.1115/1.1410919 Ren, 2005, Experimental and analytical studies on dynamic characteristics of a large span cable-stayed bridge, Eng Struct, 27, 535, 10.1016/j.engstruct.2004.11.013 Cara, 2013, Modal contribution and state space order selection in operational modal analysis, Mech Syst Signal Process, 38, 276, 10.1016/j.ymssp.2013.03.001 Paz, 2012 Moser, 2013, Design and deployment of a continuous monitoring system for the Dowling Hall Footbridge, Exp Tech, 37, 15, 10.1111/j.1747-1567.2011.00751.x https://engineering.tufts.edu/cee/shm/research_BM_continuousMonitoring.asp. Moser, 2011, Environmental effects on the identified natural frequencies of the Dowling Hall Footbridge, Mech Syst Signal Process, 25, 2336, 10.1016/j.ymssp.2011.03.005 Hai-peng, 2017, A novel automatic fuzzy clustering algorithm based on soft partition and membership information, Neurocomputing, 236, 104, 10.1016/j.neucom.2016.09.103 Mok, 2012, A robust adaptive clustering analysis method for automatic identification of clusters, Pattern Recognit, 45, 3017, 10.1016/j.patcog.2012.02.003 Chang, 2016, Modal-parameter identification and vibration-based damage detection of a damaged steel truss bridge, Eng Struct, 122, 156, 10.1016/j.engstruct.2016.04.057 Kim, 2021, Ambient and vehicle-induced vibration data of a steel truss bridge subject to artificial damage, J Bridge Eng, 26, 04721002, 10.1061/(ASCE)BE.1943-5592.0001730