Abstarct: It’s fairly possible for Bridges, dams and other civil infrastructures to become damaged during their operating life. Since neglecting these minor defects in such structure can easily lead to their malfunction, Damage Identifying of these structures is clearly very important. The ultimate consequence of structural damage can even be the whole structure’s demolition. Hence, It is essential to develop an effective methodology for damage detection of existing structures. While there is Several methods to detect the mentioned damages baxsed on different characteristics of structures, Those utilizing dynamic data structures (or in other words, the modal properties of the structure) are sensitive enough to structure’s internal and surface defects. Evaluation of mechanical and physical properties of existing structure, which is done through minimization of the difference between dynamic data measured by the finite element model of damaged structure and the intact one, is described by a process called updating of the dynamic finite-element-model, which helps to reveal the extent of structural damage and failure. In this thesis, the finite-element-model’s updating has been modeled through reducing the Stiffness of the elements. Here, instead of using the dynamic vibration test’s results, the simulated numerical data is used and the act of minimizing the difference between the existing and the intact model is implemented by the aid of natural algorithms (mexta-heuristic) algorithms. In this work, the structural mode shapes has been used to form an objective function and the particle-swarm and the firefly algorithms have been developed to solve the optimization problem. Several numerical examples of beams and frxames with a variety of different damage patterns are studied in this paper, and the results indicate a good performance of the proposed method .Though in most cases, both the optimization algorithms led to similar results, in some cases, the particle swarm algorithm, has shown better performance than the other one.