Damage detection in steel structures using static data via Genetic Algorithm
Structural damage detection technique helps to locate and detect damage that occurred in a structure by using the observed changes of its dynamic and static characteristics. The existing approaches proposed in this area can be divided into two main groups: the dynamic damage detection methods using dynamic data and the static damage detection methods using static data (static displacement, static strain etc.). As the static equilibrium equation is only related to the structural stiffness, accurate static displacement and strain data, it can be obtained rapidly and cheaply. For the reasons stated, the static damage detection methods have attracted more attention in recent years This paper presents a non-destructive global structural damage detection and assessment algorithm using static data. A set of static forces is applied to a set of degrees of freedom and the static responses (displacements) are measured at another set of DOFs. Some simultaneous equations characterized from Changes in the static response which structural damage caused. The method is determined damage as a change in the structural stiffness parameter. Genetic Algorithms are powerful tools for solving large optimization problems. Optimization is considered to minimize objective function involve difference between the load vector of damaged and healthy structure. As mentioned above the static damage identification methods have many advantages, but some difficulties still exist. The main problems the first of all is, the information used in the static damage identification methods is less than in the dynamic identification, which makes it more difficult to get the ideal identification result. For example, the angular displacement or rotational freedom is difficult to determine. Second, the effects of the damage may be concealed due to the limited load paths. Lastly, the static data provide only the local structural damage information, and the measured static data are very limited. So it is important to achieve the best damage identification and if the best result is obtained it means that the method is Reliable. Damage defined by several scenarios included single scenario and multiple scenarios. For example in plane truss scenario two means: 40% damage in element No. 2 and 60% damage in element No. 10. Numerical results in this paper for a plane arch bridge and a plane truss show the ability of this method in detecting damage in given structures. Also Figures show damage detections in multiple damage scenarios have really nice answer. Even existence of noise in the measurements doesn’t reduce the accuracy of damage detections method in these structures.
Journal of Modeling in Engineering, Volume:13 Issue: 41, 2015
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