Adaptive Neural Intelligence Method And Its Application In Structural Damage Diagnose

The structural damage detection method based on structural vibration response and systemic dynamic parameters is a difficult and growing interest problem in nondestructive evaluation (NDE). Although the method have been widely applied in practical engineering. However along with damage accumulating and increase of aging structures, new signal analysis and interpretation algorithm need to be developed in order to accurately evaluate damage. After reviewing the past work, 4 aspects of researching is developed as following:(1) The mode shape or its certain derivative of a structure is discontinuous at the damage location, and the discontinuous property can be detected by the wavelet transform modulus maximum and the wavelet ridge. The discontinuous location is the damage location. The nonlinear relation between the modulus maximum and damage extent is simulated using an artificial neural network to detect the damage extent. The discrete wavelet transform is performed on the mode shape of a simple beam by db2 wavelet, and the modulus maximums of multi-scales are calculated and used as inputs into BP neural network for damage extent assessment. Numerical simulation results show the modulus maximums are sensitive to structural damage.(2) The continuous wavelet transform by a Mexican hat wavelet having two vanishing moments is applied to the difference in the first mode shapes of a structure under the health and damage state based on the theory of the mode curvature used in damage detection, and a damage index is presented. That is to say, the difference is first smoothed by Gauss function, then the second derivative of the smoothed difference is calculated, he damage location is detected by the horizontal ordinate convergence of the modulus maximum for low scales. The relation between Lipschitz exponent and damage extent is discussed based on the index maximum. Numerical simulation results show the index can exactly detect the location of the damage. Meanwhile, a correlative experiment is conducted.(3) Dynamic signals measured from a structure are decomposed into wavelet packet components. The feasibility of component energies taken as damage factor is studied when damage exits in two parts of a structure. The energies are calculated in an appropriate scale based on sensitivity study, and they are used as inputs into BP neural network for multi-location damage diagnosis. The effect of noises on the method is studied in the numerical simulations, and the extrapolation ability of the neural network is discussed.(4) The wavelet neural network(WNN) classifiers program is achieved using structural damage diagnosis, and the WNN has the virtues of the wavelet transform and the neural network. Genetic algorithm is used to improve the performance of the WNN.