Proceedings home preface contents authors keywords copyright reference ©2012 Civil-Comp Ltd				Paper 83 Statistical Multi-Objective Structural Damage Identification based on Dynamic Parameters R. Perera1, E. Sevillano1 and A. Ruiz2 1Department of Structural Mechanics, 2Department of Applied Mathematics, Technical University of Madrid, Spain Keywords: structural health monitoring, damage detection, multi-objective optimization, statistical analysis. full paper (pdf) - reference Most real-world problems feature multiple optimisation objectives. This is true in real-world damage identification problems in which the lack of a clear objective function suggests simultaneous optimizations of several objectives with the purpose of improving the robustness and performance of the procedure. A good solution to these multi objective optimisation problems should preferably be good regarding all objectives. These objectives cannot be optimised independently, but must be treated as a whole. Evolutionary algorithms are a class of stochastic search methods that have been found to be very efficient and effective in solving complex multiobjective problems where conventional optimisation tools fail to work well. Most real-world systems perform randomly to a certain extent. Therefore the optimisation algorithm must consider stochastic fitness functions, instead of deterministic ones. Uncertainties existing in the structural model and measured vibration data might lead to unreliable damage detection. This type of uncertainty is called noise and its existence might lead to unreliable damage detection. In damage identification problems, noise stems from several sources such as sensor measurement errors or incomplete simulations of computational models and, it can also be even intrinsic to the problem. If the noisy optimisation problem is treated as if it were deterministic then this will lead the evolutionary algorithm in a wrong direction and degrade the algorithm's performance hence the convergence of the optimisation will be adversely affected. Therefore, the performance of the multi-objective evolutionary optimization algorithm deteriorates quickly with increasing noise intensities. In this paper, a statistic structural damage detection method formulated in a multiobjective context is proposed. The statistic analysis is implemented to take into account the uncertainties existing in the structural model and measured structural modal parameters. The method presented is verified using a number of simulated damage scenarios. The effects of noise and damage levels on damage detection are investigated.