Study On Structural Damage Identification Method Based On Wavelet Neural Network

Long-term use and environmental factor will cause various defects (damages) in civilstructures, such as bridges. Damage accumulated in civil structures could lead to catastrophiccollapse of the structure, causing huge loss to the society. Thus, structural damageidentification of civil structures is of great academic, economic and social significance. In thispaper, the positioning capabilities on the time and frequency of the time series of the wavelettransform, the scaling properties in the spatial domain of the wavelet function, and capture thenon-deterministic nature and complex non-linear capabilities of the neural network, combinedwith the fuzzy clustering technique, the dynamic time delay fuzzy WNN model of thestructure non-parametric system identification is proposed, then based on the pseudo-spectralmethod, the damage identification method for large complex structures under ambientexcitation is proposed. Finally, based on a combined theoretical analysis, numericalsimulation and physical model experiment approach, the in-depth study on Structural dynamicnondestructive detection methods is conducted. In sum, the main contents are concluded asfollows.1) Based on the non-orthogonal wavelet-Mexican hat wavelet, the dynamic time delayWNN model for the dynamic system function approximation is developed, the model canapproximate the instantaneous nonlinear function at will. The flexibility of dynamic timedelay WNN model in the wavelet design is empolyed to add the additional functionality, suchas the ideal transform parameter adjustment in the function approximation.2) On the basis of the reconstruction state space concept of chaos theory, the input vectorof the dynamic time delay WNN is constructed, it retains the dynamic characteristics of thetime series data, can identify the non-physical parameters of the structural system (such as thedynamic response of the structure). Then, FNN method is empolyed to find the optimalembedding dimension of the input vectors, and using the modified Gram-Schmidt algorithmand AFPE criteria for selecting the number of the WNN hidden layer nodes, reducing thescale of the network model, and improving the computational efficiency and recognitionaccuracy.3) The wavelet function with the spatial-domain scaling properties is empolyed in thedynamic WNN model, it is only change the model output in the limited range of the inputtime series, such the property reduce the adverse effects between the nodes in the neural network, improve the accurate of the function approximation, and speed up the trainingprocess of the neural network.4) Be based upon the NARMAX method, the fuzzy clustering of the reconstruction statespace vector and the non-orthogonal wavelet neural network is combined to construct thedynamic time delay fuzzy WNN model of the structure non-parametric identification. Themodel can capture the dynamic characteristics of time-series sensor data effectively andaccurately, and can overcome the large local output error caused by the same spatial domainscaling property when the traditional WNN model dealing with the local accurate trainingdata.5) The adaptive LM-LS hybrid learning algorithm is empolyed to train the dynamicdelay fuzzy WNN model, the learning process is divided into the two-step iterative, Firstusing the LS algorithm to determine the linear parameters of the model, and then using theLM algorithm to adjust the nonlinear parameters of the model. The algorithm avoids thesecond derivative of the Gauss-Newton algorithm, overcomes the numerical instabilityproblems of the steepest descent algorithm, and improves the learning convergence rate of theWNN model significantly.6) The wavelet is empolyed in two aspects of the dynamic time delay fuzzy WNN model.○1Using discrete wavelet packet transform (DWPT) techniques to denoise the sensor data, itcan speed up the training convergence rat of the structural system identification model, andimprove the recognition accuracy of the system significantly.○2The wavelet function is usedas the activation function of the neural networks, and combine with the fuzzy clusteringtechniques to construct the fuzzy WNN model, The model can capture the dynamiccharacteristics of the time-series sensor data effectively and accurately.7) Ground on the dynamic time delay fuzzy WNN model of the structure non-parametricsystem identification, and the pseudo-spectral method, the damage identification method forlarge complex structures under ambient excitation is proposed. The damage identificationresults obtained by this method is the local damage of the structure within the selectedsub-structure, the damage all of the structure can be identified by selecting a series ofsubstructure, this method has the global and local damage identification capability of thestructure; The pseudo-spectral method cognitives damage in the form of the differentfrequency bands, contains the damage information wealthly; The pseudo-spectral method forthe structural damage identification without set the threshold of the damage index byexperience to determine whether the structure occur damage, as long as the damage index is greater than zero, the structure is determined to damage, and has a high damage sensitivity;The external dynamic loads is only used to incentive the structure sufficiently, without tomeasure the load spectrum, thus, it can identificate the damage of the engineering structuresunder ambient excitation, and with a strong engineering adaptability.