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This paper presents a new model updating approach for structural damage localization and quantification. Based on the Modal Assurance Criterion (MAC), a new damage-sensitive cost function is introduced by employing the main diagonal and anti-diagonal members of the calculated Generalized Flexibility Matrix (GFM) for the monitored structure and its analytical model. Then, the cost function is solved by Democratic Particle Swarm Optimization (DPSO) algorithm to achieve the optimal solution of the problem lead to damage identification. DPSO is a modified version of standard PSO algorithm which is developed for presenting a fast speed evolutionary optimization strategy. The applicability of the method is demonstrated by studying three numerical examples which consists of a ten-story shear frame, a plane steel truss and a plane steel frame. Several challenges such as the efficiency of the DPSO algorithm in comparison with other evolutionary optimization approaches for solving the inverse problem, impacts of random noise in input data on the reliability of the presented method, and effects of the number of available modal data for damage identification, are studied. The obtained results reveal good, robust and stable performance of the presented method for structural damage identification using only the first several modes’ data.

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In  this  paper  a  new  method  is  presented  for  structural  damage  identification.  First,  the damaged structure is  excited by short  duration impact acceleration  and then, the  recorded structural displacement time history responses under free vibration conditions are analyzed by Continuous Wavelet Transform (CWT) and Wavelet Residual Force (WRF) is calculated. Finally, an effective damage-sensitive index is proposed to localize structural damage with a high  level  of  accuracy.  The  presented  method  is  applied  to  three  numerical  examples, namely  a  fifteen-story  shear  frame,  a  concrete  cantilever  beam  and  a  four-story,  two-bay plane steel frame, under different damage patterns, to detect structural damage either in free noise or noisy states. In addition, some comparative studies are carried out to compare the presented  index  with  other  relative  indices.  Obtained  results,  not  only  illustrate  the  good performance of the presented approach for damage identification in engineering structures, but  also  introduce  it  as  a  stable  and  viable  strategy  especially  when  the  input  data  are contaminated with different levels of random noises.