| Since the significant of response prediction, structure controlling and structural health monitoring, the problem of bridge structural system damage identification receives more attention recently. However, the research results always be restricted by the accuracy of the model parameters selection, and the parameters include lots of uncertain additional information during the model parameters selection. It will lead an adverse influence to the system damage identification. Adopt probability distribution function (PDF) to describe the nonlinear dynamic system model parameters, could avoid the errors while select the parameters effectively. The model parameters can be used to calculate the probability distribution function.Researchers, using the structural health monitoring method of bridge structure to identify the early damage and change. Recently, the structural damage identification methods are received much attention. After analysis for the structural modal parameters, we can identify the structure of injuries in position. However, the bridge structural always complex and with many parameters, determine the damage location structure is difficult, since the large amount of calculation.Most damage identification methods are based on actual engineering in measured data and compared the bridge structure model to determine the location and degree of structural damage. Bridge structure model is established to identify the process, the influence is bigger. In practical engineering, bridge structures are embodied in nonlinear properties, including material nonlinearity and the geometrical nonlinearity. However, nonlinear system of bridge structure by the result of identification is often the selection of model parameters accuracy of model with the limit, the parameter selection, many uncertain factors were also included in the parameters, and the system identification results have very adverse effects. Using probability distribution function to describe the nonlinear dynamic system model parameters, can effectively avoid the errors when parameter selection.Based on the location and severities of structural damage identification, the remaining life of structure could be assessed. General structural damage identification method was extracted from the measurement data of modal parameter value structure, the structure of the damage can cause modal parameter value change, so by monitoring the modal parameters change trend, also can reflect the structural damage. Bridge structural damage identification methods are variously, but in practice and a lot of problems. Many scholars bridge structure damage identification method is studied systematically identifying concrete bridge, and to explore the vibration modal parameters method to identify and reduce the environmental noise affect the results of modal parameter identification, improve the accuracy and validity.Bayesian theory in the concrete steps for damage identification from a large number of data including extract limited a regression variables or eigenvalue of feature extraction phase; Recognition input variables and the extraction of eigenvalue of the relationship between training phases; By means of the regression equations and boundary conditions on the structural damage assessment of prediction stage. Bayesian framework based on the relevance vector machine in signal processing method has the required data in less computing speed and accuracy, high yield. In response to the signal accord with Gaussian distribution under the condition of the autocorrelation determination methods, through the response is not relevant items in vector away, so that they could be massively reduce operation times, and won't cause too big for the results of influence. Based on the systematic study of nonlinear theory, a nonlinear finite element model of bridge structure and, depending on the Bayesian model modification method of finite element model, revised, amended bridge structure with nonlinear finite element model for the reference to the actual bridge structure, load test and the experimental results were analyzed, through the relevance vector machine method to identify structural damage situation. This dissertation main research work for:According to material nonlinear constitutive equations and geometry nonlinear stiffness matrix, on the double nonlinear equilibrium equation and nonlinear dynamic equations are deduced. Introducing actual engineering problems, establish the nonlinear finite element model of bridge structure, and to describe the nonlinear finite element model of the influence of bridge structure.Using bayesian system identification method, the double nonlinear bridge structure model parameters are updating. Through the limitations of the random simulation method of prior probability density function, iterative computation model parameters fixed probability density function.The Duffing equations of motion, and elastic-plastic vibration parameters of the model are employed, validation bayesian method in model parameters in the process of fixed effectiveness. The Relevance Vector Machine in regression and classification is introduced, the kernel function is employed, to verify the Relevance Vector Machine application, the response signal of double nonlinear bridge model is adopted. Sine function and Ripley function are employed to verify the Relevance Vector Machine validation.Apply the Relevance Vector Machine method in bridge structural damage identification process, put forward the bridge structure modal parameter selection and variable as input function for the Relevance Vector Machine, the result of training according to training of bridge structure damage identification. To verify the Relevance Vector Machine method, bridge structure finite element damage model is established, the modal parameter variation during the Relevance Vector Machine training will yeild structure damage identification results. Employ the actual bridge structure to analysis, the modal parameter variable generation into the Relevance Vector Machine trainning, through and bridge for the static load test results contrast to identify the damage of bridge structure, verify the Relevance Vector Machine structural damage identification method has effectiveness of the double nonlinear bridge structure. Through the above work, the conclusions are:1. A double nonlinear coupling stiffness matrix is proposed based on geometric nonlinear balance equation and nonlinear constitutive relation. According to the least squares theory, the nonlinear coupling stiffness matrix is deduced. Consider the double nonlinear effects, to establish the bridge structure model of structure. By the results that the concentrated force beyond the yield load, the structure of the double nonlinear characteristics significantly reflects, with the increase of structural deformation.2. Based on bayesian method, on the double nonlinear structure model, through Duffing motion equation and elastoplastic vibration parameters of the model are fixed, validation bayesian method in model parameters in the process of updating effectiveness.3. The training methods of the relevance vector machine described in detail. Ramified through the relevance vector machine and support vector machine method to regression Sine function, and classification of Ripley function compared to the training, verify the relevance vector machine in signal regression and classification, the data in the process less computing speed and accuracy higher advantages.4. Puts forward the double nonlinear bridge structure modal parameters variable function and for the relevance vector machine training for structural damage identification. Establishing the damage structure finite element model is validated the relevance vector machine training methods in the process of effectiveness in damage identification. Apply the Relevance Vector Machine damage identification method in practical engineering, compared with the static load test results. The relevance vector machine method damage identification results are accurately.
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