Statistical damage identification method based on dynamic response sensitivity

The traditional deterministic damage detection method is based on the assumption that the measured data and the finite element model are accurate. However, in real situation, there are many uncertainties in the damage identification procedure such as the errors of the finite element model and the measurement noise. Since the uncertainties inevitably exist in the finite element models and measured data, the statistic method which considers the uncertainty has wide practical application. This paper proposes a statistical damage identification method based on dynamic response sensitivity in state-space domain. Considering the noise of the finite element model and measured acceleration response, the statistical variations of the damaged finite element model are derived with perturbation method which is based on a Taylor series expansion of the response vector and verified by Monte Carlo technique. Afterward, the probability of damage existence for each structural element is estimated using the statistical characteristic of the identified structural parameters. A numerical simply supported beam under the moving load is applied to demonstrate the accuracy and efficiency of the proposed statistical method.

[1]  He Xia,et al.  Structural damage identification for railway bridges based on train-induced bridge responses and sensitivity analysis , 2011 .

[2]  X. M. Wang,et al.  Stochastic damage detection method for building structures with parametric uncertainties , 2011 .

[3]  Jm M. Ko,et al.  An improved perturbation method for stochastic finite element model updating , 2008 .

[4]  Li Cheng,et al.  Development in vibration-based structural damage detection technique , 2007 .

[5]  Hui Li,et al.  A probabilistic damage identification approach for structures with uncertainties under unknown input , 2011 .

[6]  Jun Li,et al.  Updating the reliability of a concrete bridge structure based on condition assessment with uncertainties , 2010 .

[7]  Jun Li,et al.  Improved damage identification in bridge structures subject to moving loads: Numerical and experimental studies , 2013 .

[8]  Hong Hao,et al.  Civil structure condition assessment by FE model updating: methodology and case studies , 2001 .

[9]  Jun Zhang,et al.  Statistical moment-based structural damage detection method in time domain , 2013, Earthquake Engineering and Engineering Vibration.

[10]  Hongping Zhu,et al.  Embedded Electromechanical Impedance and Strain Sensors for Health Monitoring of a Concrete Bridge , 2015 .

[11]  Marek Iwaniec,et al.  Low Frequency Damage Analysis of Electric Pylon Model by Fuzzy Logic Application , 2013 .

[12]  Usik Lee,et al.  A frequency response function-based structural damage identification method , 2002 .

[13]  Bo Chen,et al.  A Comparative Study on Frequency Sensitivity of a Transmission Tower , 2015, J. Sensors.

[14]  Hitoshi Furuta,et al.  A Bridge Damage Detection Approach using Train-Bridge Interaction Analysis and GA Optimization , 2011 .

[15]  J. Vantomme,et al.  Damage assessment in reinforced concrete beams using eigenfrequencies and mode shape derivatives , 2002 .

[16]  Muneo Hori,et al.  A NUMERICAL STUDY OF STRUCTURAL DAMAGE DETECTION USING CHANGES IN THE ROTATION OF MODE SHAPES , 2002 .

[17]  Costas Soutis,et al.  Delamination Detection in Composite Laminates from Variations of Their Modal Characteristics , 1999 .

[18]  Siu-Seong Law,et al.  Damage detection in simply supported concrete bridge structure under moving vehicular loads , 2007 .

[19]  Jun Li,et al.  Damage Identification and Optimal Sensor Placement for Structures under Unknown Traffic-Induced Vibrations , 2017 .

[20]  Hong Hao,et al.  Damage identification of structures with uncertain frequency and mode shape data , 2002 .

[21]  Yonghui An,et al.  A degree of dispersion‐based damage localization method , 2016 .

[22]  G. Stefanou The stochastic finite element method: Past, present and future , 2009 .

[23]  S. S. Law,et al.  Structural damage detection with statistical analysis from support excitation , 2008 .