Theory On Parameters Identification Of Foundation, Structure, And Foundation-Structure Interaction System

This paper makes series research on following aspects: parameters identification of foundation, structure, foundation-structure interaction system. 1) The precision of the estimated parameters and convergence are enhanced by the introduction of modern optimizing algorithm. 2) Using new model from mathematics and mechnics, the reliability of parameters identification is improved than that of adopting chained-multi-degrees-of-freedom structures. 3) On the basis of completed FEM methods, parameters of a frame are identified. With a curtain clue and solusion, this procedures also work well with a fairly large complex system. Hence, a new method to identify the parameters of foundation, structure, foundation-structure interaction system is being developed by summarizing the above research work.The relevant neural network of a new learning algorithm-backpropagation with Kalman filter is proposed in the context of system identification. The basic idea is that the adjusting weigh value of the neual network is taken as the identification of the parameters for the nonlinear system. So, the Kalman filtering technique was applied to modify the weight matrices of neural network. The calculation programe is worked out on directly identifying the physical parameters for the silo, which combines neural network with new proposed learning algorithm within time domain.We compose a neural network in which the input signals are the silo response displacement and velocity ,and output signals are the silo response acceleration plus excited acceleration. The filtering algorithm of neural network proposed by this paper possess higher accuracy , faster convergence, very robust to noise-contaminated teaching signals. It doesn't require good initial guess for convergence but approximately amount level. The methods developed are extended to identify parameters of foundation without input and parameters of foundation-silo interaction system .Using wave motion theories to describe foundation makes an promption than that of adopting chained-multi-degrees-of-freedom model in parameters identification field. A method to identify the physical parameters of the multilayered halfspace elastic media is being developed by using neural network. An updated-Latin hypercube sampling and calculated data based on wave motion theories are adapted for efficient generation of the patterns of training the neural network. The Levenberg-Marquardt algorithms was applied to modify the weight matrices of neural network. Then, convergence of the netwok learning algorithms is improved.The substructural technique is employed to parameters identification of frame, natural frequencies and generalized mode shapes of frame are computed. According to the relation between substructural component mode and generalized mode, the branch mode is calculated. A neural network-based on substructural identification was presented for the estimation of the parameters of a complex structural system, particularly for the case with noisy and incomplete measurement of the modal data. Element-stiffness matrix baseline parameters and modal strain energy are employed for the selection of the base modes. An updated-Latin hypercube sampling and modal assurance criteria are adapted for efficient generation of the inputs. We compose a neural network in which the input signals are the frame substructure natural frequencies and mode shapes, and output signals are the submatrix scaling factor. The Levenberg-Marquardt algorithms is applied to modify the weight matrices of neural network. Thus, it provides a feasible clue and procedure to overcome the issues associated with many unknown parameters in a fairly large complex system.Aforementioned approaches is presented for the estimation of the parameters of acomplex structural system-the foundation- silo interaction system. The identificationendeavour with the method of substructural parameters identification technique is accomplished by dividing system into upside configuration and foundation substructure.