Damage Detection of a Prestressed Concrete Beam Using Modal Strains

Different methods are proposed in literature using experimental modal information to detect possible damage. In this paper a finite-element (FE) model updating technique is applied. The unknown properties of a FE model are adapted, such that the differences between experimental modal data (modal curvature in combination with eigenfrequencies and mode shapes) and the corresponding analytical predictions are minimized. An iterative sensitivity based algorithm is used for solving this optimization problem. The method is applied to the damage assessment of a gradually damaged prestressed concrete beam. It is assumed, that damage can be characterized by reducing the bending stiffness. The main focus of this paper is to analyze the influence of using modal curvatures. In contrast to eigenfrequencies and mode shapes, modal curvatures are very sensitive to local changes of the bending stiffness nearby the sensor location, but insensitive to local changes far from the measurement location.

[1]  R. Measures,et al.  Fiber-optic Bragg grating sensors for bridge monitoring , 1997 .

[2]  Arun Kumar Pandey,et al.  Damage detection from changes in curvature mode shapes , 1991 .

[3]  W. P. De Wilde,et al.  Damage identification in reinforced concrete structures by dynamic stiffness determination , 2000 .

[4]  James V. Beck,et al.  Parameter Estimation in Engineering and Science , 1977 .

[5]  Colin H. J. Fox,et al.  The location of defects in structures - A comparison of the use of natural frequency and mode shape data , 1992 .

[6]  G. De Roeck,et al.  Damage detection in bridges using modal curvatures: application to a real damage scenario , 1999 .

[7]  Sathya Hanagud,et al.  An integral equation for changes in the structural dynamics characteristics of damaged structures , 1997 .

[8]  Guido De Roeck,et al.  Damage assessment by FE model updating using damage functions , 2002 .

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

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

[11]  K. V. D. Abeele,et al.  Damage assessment in reinforced concrete using spectral and temporal nonlinear vibration techniques , 2000 .

[12]  M S Mirza,et al.  An experimental study of static and dynamic responses of prestressed concrete box irder bridges , 1990 .

[13]  R. A. Shenoi,et al.  Quantification and localisation of damage in beam-like structures by using artificial neural networks with experimental validation , 2003 .

[14]  Yeoshua Frostig,et al.  Free vibrations of cracked prestressed concrete beams , 2004 .

[15]  Charles R. Farrar,et al.  A summary review of vibration-based damage identification methods , 1998 .

[16]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[17]  O. S. Salawu.  Detection of structural damage through changes in frequency: a review , 1997 .

[18]  R. Fox,et al.  Rates of change of eigenvalues and eigenvectors. , 1968 .

[19]  Nuno M. M. Maia,et al.  Theoretical and Experimental Modal Analysis , 1997 .