Study On Identification Method For Structural Physical Parameters In Time-Domain

The theories on parameter identification for civil engineering structures serve as basis for the work of establishing structural health-monitoring system, identifing possible damages and evaluating structural conditions, thus their significance in maintaining security and normal performances for structures on service. This thesis mainly discusses the time-domain methods for identification of structural physical parameters, for the purpose that, by means of the measurement data of structural dynamic responses, people can trace characteristic physical parameters which can effectively reflect change of real conditions of those observed structures. This paper can be divided into six parts.In the first part (chapter 1), it is an intrduction. The research background and present research condition are reviewed summaried.In the second part (chapter 2), it is a basic theory part. It introduces the mathematic model suitable for physical parameters identification; basic identification principle and least squares algorithm in structural physical parameters invertion identification are also introduced.In the third part (chapter 3), a parameterization method based on sensitivities of response force vectors is introduced in this chapter. And based on an exisiting identification algorithm, an improved version of the algorithm is proposed. The improved algorithm is grounded on the finite element theory and the sensitivity analysis method, and it can identify finite elements'physical parameters without input information, achieving much higher identification efficiency.In the fourth part (chapter 4), structure identification in time domain with incomplete output information is studied. The dynamic equations and parameter identification equations of sub-structure in general use are established, which make full use of the gathering laws in finite element method. Such theoretical extensions are quite meaningful for improving identification efficiency, lightening the workload of response measurement and data preparation before identification, and extending the scope of types of structures that can be identified by the algorithm.In the fifth part (chapter 5), it summarizes the main content of this paper and predicts the future development of this field.