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PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY
Edited by: B.H.V. Topping
Damage Detection of Truss Structures using an Improved Charged System Search Algorithm
A. Kaveh and A. Zolghadr
Centre of Excellence for Fundamental Studies in Structural Engineering, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
A. Kaveh, A. Zolghadr, "Damage Detection of Truss Structures using an Improved Charged System Search Algorithm", in B.H.V. Topping, (Editor), "Proceedings of the Eleventh International Conference on Computational Structures Technology", Civil-Comp Press, Stirlingshire, UK, Paper 82, 2012. doi:10.4203/ccp.99.82
Keywords: damage detection, charged system search, truss structures, change of frequencies.
Structures are prone to damage for many different reasons in their lifespan. Finding the locations and the measurements of this damage, which is undeniably important to maintain the structure safety, is not always possible through visual inspection. Therefore, the responses of the structure and the changes that occur in them arising from the damage is viewed as a means to assess the structural damage. Being accurately measurable and independent from the external excitation, natural frequencies of the structure are among the best response candidates for this purpose .
In this paper detection and assessment of structural damage using the changes in the structure's natural frequencies is addressed as an optimization problem. The damaged element(s) and the percentage of damage are considered as the variables of the problem. The objective is to set these variables such that the natural frequencies of the model correspond to the experimentally measured frequencies of the actual damaged structure.
This is a problem with several global optimal solutions each representing a probable state of damage. Obviously, unlike many other optimization problems, it is not enough to find one of these optimal solutions; it is important to find all these possible states and to compare them.
However meta-heuristic optimization algorithms tend to converge to a single solution in each run. These algorithms do not generally use any optimality criterion and evaluate the quality of solutions only by direct comparison. Experimental results show that some of the optimal solutions have a greater probability of being found by the algorithms than others. In fact the algorithm agents neglect some of the promising regions of the search space to the benefit of some others.
In this paper, an improved version of the charged system search algorithm  is introduced and utilized to study the problem of finding as many global optimal solutions as possible in a single run.
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