Damage detection by a hybrid real-parameter genetic algorithm under the assistance of grey relation analysis

Abstract

For damage detection, a hybrid technique consisting of two strategies is proposed. First, grey relation analysis is used to exclude the impossible damage locations such that the number of design variables can be reduced. By using two simple rules proposed in this work, all actual damage locations are included as possible damage locations and the number of design variables is effectively reduced. Secondly, a real-parameter genetic algorithm is combined with simulated annealing and adaptive mechanisms for finding the actual damages. The proposed hybrid algorithm uses only vertical nodal displacements of static responses. In addition to the ideal error-free case in which there is no difference between the experimental data and the theoretical data, the case with 5% error, in which the experimental data is simulated by adding 5% error maximum to the corresponding theoretical data, is considered for measuring error and modeling error. From the results, it is demonstrated that the proposed technique is efficient in damage identification. For the error-free case, the predicted results agree with the true solutions exactly. For the case with 5% error, the prediction obtained is also reasonable.