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This paper presents a suitable and quick way to choose earthquake records in non-linear dynamic analysis using optimization methods. In addition, these earthquake records are scaled. Therefore, structural responses of three different soil-frame models were examined, the change in maximum displacement of roof was analyzed and the damage index of whole structures was measured. The soil classification of project location was divided into 4 different types according to the velocity of shear waves in the Iranian Code for Seismic Design. As a result, 8 frame models were considered. The selection and scaling were carried out in 2 stages. In the first stage, the matching with design spectrum was carried out using genetic algorithm in order to achieve the mean of structural response. In the second stage, the matching with average of structural responses were carried out using PSO to achieve 1 or 3 accelerograms with related factors in order to be used in structural analysis.

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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.