Damage detection method for large structures using static and dynamic strain data from distributed fiber optic sensor

A method for damage detection applicable to large slender steel structures such as towers of large-scale wind turbines, long-span bridges, and high-rise buildings is presented. This method is based on continuous strain data obtained by distributed fiber optic sensor (FOS) and neural network (NN) analysis. An analytical model for cracked beam based on an energy balance approach was used to train a NN. The continuous static strains and the natural frequencies obtained from the distributed FOSs were used as the input to the trained NN to estimate the crack depths and locations. An experimental study was carried out on a cracked cantilever beam to verify the present method for damage identification. The cracks were inflicted on the beam, and static and free vibration tests were performed for the intact case and the damage cases. The distributed FOSs were used to measure the continuous strains. The damage estimation was carried out for the 5 damage cases using the NN technique. It has been found that the identified crack depths and locations agree reasonably well with the inflicted cracks on the structure.

[1]  A. S. J. Swamidas,et al.  Crack identification in vibrating beams using the energy method , 2001 .

[2]  S B Slaughter.  STATE OF THE ART IN HULL RESPONSE MONITORING SYSTEMS. , 1997 .

[3]  Chung Bang Yun,et al.  Modal Flexibility-based Damage Detection Technique of Steel Beam by Dynamic Strain MeasurementsUsing FBG sensors , 2007 .

[4]  T. Parker,et al.  A fully distributed simultaneous strain and temperature sensor using spontaneous Brillouin backscatter , 1997, IEEE Photonics Technology Letters.

[5]  H. Ohno,et al.  Industrial Applications of the BOTDR Optical Fiber Strain Sensor , 2001 .

[6]  Alan D. Kersey,et al.  Fiber optic sensors in concrete structures: a review , 1996 .

[7]  Chung Bang Yun,et al.  Neural networks-based damage detection for bridges considering errors in baseline finite element models , 2003 .

[8]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[9]  Hoon Sohn,et al.  A baseline-free crack detection using nonlinear acoustics , 2007 .

[10]  Bc Lee,et al.  Lamb wave propagation modelling for damage detection: II. Damage monitoring strategy , 2007 .

[11]  Wei Zhang,et al.  The strain field method for structural damage identification using Brillouin optical fiber sensing , 2007 .

[12]  Chung Bang Yun,et al.  Damage diagnostics on a welded zone of a steel truss member using an active sensing network system , 2007 .

[13]  Gangbing Song,et al.  Recent applications of fiber optic sensors to health monitoring in civil engineering , 2004 .