Mode identifiability of a cable-stayed bridge under different excitation conditions assessed with an improved algorithm based on stochastic subspace identification

Deficient modes that cannot be always identified from different sets of measurement data may exist in the application of operational modal analysis such as the stochastic subspace identification techniques in large-scale civil structures. Based on a recent work using the long-term ambient vibration measurements from an instrumented cable-stayed bridge under different wind excitation conditions, a benchmark problem is launched by taking the same bridge as a test bed to further intensify the exploration of mode identifiability. For systematically assessing this benchmark problem, a recently developed SSI algorithm based on an alternative stabilization diagram and a hierarchical sifting process is extended and applied in this research to investigate several sets of known and blind monitoring data. The evaluation of delicately selected cases clearly distinguishes the effect of traffic excitation on the identifiability of the targeted deficient mode from the effect of wind excitation. An additional upper limit for the vertical acceleration amplitude at deck, mainly induced by the passing traffic, is subsequently suggested to supplement the previously determined lower limit for the wind speed. Careful inspection on the shape vector of the deficient mode under different excitation conditions leads to the postulation that this mode is actually induced by the motion of the central tower. The analysis incorporating the tower measurements solidly verifies this postulation by yielding the prevailing components at the tower locations in the extended mode shape vector. Moreover, it is also confirmed that this mode can be stably identified under all the circumstances with the addition of tower measurements. An important lesson learned from this discovery is that the problem of mode identifiability usually comes from the lack of proper measurements at the right locations.

[1]  Bart Peeters,et al.  System identification and damage detection in civil engineering , 2000 .

[2]  Pelin Gundes Bakir Automation of the stabilization diagrams for subspace based system identification , 2011, Expert Syst. Appl..

[3]  Guido De Roeck,et al.  REFERENCE-BASED STOCHASTIC SUBSPACE IDENTIFICATION FOR OUTPUT-ONLY MODAL ANALYSIS , 1999 .

[4]  Hilmi Luş,et al.  Ambient Vibration Analysis with Subspace Methods and Automated Mode Selection: Case Studies , 2008 .

[5]  Kai-Yuen Wong,et al.  Design of a structural health monitoring system for long-span bridges , 2007 .

[6]  Jer-Nan Juang,et al.  An eigensystem realization algorithm for modal parameter identification and model reduction. [control systems design for large space structures] , 1985 .

[7]  Carmelo Gentile,et al.  Automated modal identification in operational conditions and its application to bridges , 2013 .

[8]  Rune Brincker,et al.  Modal identification of output-only systems using frequency domain decomposition , 2001 .

[9]  Filippo Ubertini,et al.  System identification of a super high-rise building via astochastic subspace approach. , 2011 .

[10]  Yi-Qing Ni,et al.  Investigation of mode identifiability of a cable-stayed bridge : comparison from ambient vibration responses and from typhoon-induced dynamic responses , 2015 .

[11]  James M. W. Brownjohn,et al.  Fuzzy Clustering of Stability Diagrams for Vibration-Based Structural Health Monitoring , 2008, Comput. Aided Civ. Infrastructure Eng..

[12]  Kai-Yuen Wong,et al.  Instrumentation and health monitoring of cable‐supported bridges , 2004 .

[13]  J. Bendat,et al.  Random Data: Analysis and Measurement Procedures , 1971 .

[14]  Yi-Qing Ni,et al.  Technology developments in structural health monitoring of large-scale bridges , 2005 .

[15]  Yi-Qing Ni,et al.  Health Checks through Landmark Bridges to Sky-High Structures , 2011 .

[16]  M. Scionti,et al.  Stabilisation diagrams: Pole identification using fuzzy clustering techniques , 2005, Adv. Eng. Softw..

[17]  Rune Brincker,et al.  Using Enhanced Frequency Domain Decomposition as a Robust Technique to Harmonic Excitation in Operational Modal Analysis , 2006 .

[18]  Bart De Moor,et al.  Subspace algorithms for the stochastic identification problem, , 1993, Autom..

[19]  E. Parloo,et al.  Maximum likelihood identification of non-stationary operational data , 2003 .

[20]  Guido De Roeck,et al.  Fully automated (operational) modal analysis , 2012 .

[21]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[22]  Bart De Moor,et al.  Subspace Identification for Linear Systems: Theory ― Implementation ― Applications , 2011 .