System Identification Of Structural Modal Parameters With Ambient Vibration Responses And Software Implementation

In recent years, modal identification with ambient vibration measurements has been extensively adopted for civil engineering structures. The time-domain method, such as RD-ITD, ERA, and SSI etc., can be applied to identify the complex structures without energy leakage and has high resolution of the frequency. In order to improve the accuracy of identified modal parameters for noisy measurements, using higher order of the model is an effective approach. However, the higher order model will result in spurious modes because of over-specified order of the model. Discriminating systemic (physical) modes form spurious modes plays a key role for further applications. The commonly-used stabilization diagram and the MAC criterion may lead to the computational burden to determine the systemic modes. Therefore, a direct methodology to identify the systemic modes is very important for modal identification in real applications.The object of this study is to propose a direct algorithm of modal parameter identification with ambient vibration measurements. The contents of the dissertation are as follows. Firstly, the state of the art of the modal parameter identification is briefly summarized in the chapter one. The significance and the objective of the study are presented. Secondly, several commonly-used methods of the modal parameter identification with ambient vibration measurements are reviewed. The effectiveness and the limitations of the current methods are analyzed. Thirdly, a direct methodology of systemically modal identification is proposed by using of the ambient vibration measurements. And the effectiveness of the proposed approach is studied by the numerical simulations. Fourthly, the software of modal parameter identification with ambient vibration measurements is developed by using of Matlab. The software is mainly based on the proposed algorithm, and the Peak Picking (PP) algorithm and the Stochastic Subspace Identification (SSI) algorithm are both adopted in the software. Finally, the effectiveness of the developed software is validated by two application examples. One is the Benchmark data proposed by ASCE-IASC, and another is the measured data of Xining Beichuan River concrete-filled steel tubular arch bridge.