Stochastic Subspace Identification And Application Of Structures Under Ambient Vibration

Dynamic testing of structures under ambient vibration excitation has many advantages, such as no excitation equipment needed, no interruption of structural service conditions and less test time, which is more close to the real working conditions of civil engineering structures. As only output is measured and real input remains unknown in terms of ambient vibration testing, the modal parameter identification therefore bases structural output-only data. The output-only modal parameter identification is different from traditional one and is now very active research topic in the system identification of engineering structures. The state-of-the-art studies are carried out in this thesis on the theory, algorithm, system order determination and real case applications of the data-driven stochastic subspace identification (SSI). The main work and conclusions include: 1. The theoretical background of the data-driven stochastic subspace identification is discussed. The study is focused on three algorithms: Unweighted Principal Component (UPC) Algorithm, Canonical Variate Algorithm (CVA) and Principal Component Algorithm (PC) due to the different treatments on the projected matrix of SSI. The ambient vibration measurements on a real case arch bridge-Beichuan Bridge in Xilin, China, are used to compare the modal parameter identification results obtained from three algorithms. It is demonstrated that these three algorithms are close together. In terms of identification accuracy, the PC algorithm is the best but it is also time consuming. 2. How to determine the system order is so far still a problem among time-domain system identification techniques. Based on the concept of singularity entropy in this thesis, the singularity entropy increment and its derivative are proposed to determine the system order incorporated with the stochastic subspace identification. One 4-strory frame and one simply beam are used to demonstrate the applicability and reliability of proposed singularity entropy based methods. 3. The field ambient vibration tests on the Jian arch bridge in Jiangxi Province is described in details. The stochastic subspace identification has been used successfully to identify the three-dimensional vibration modes of bridge deck and arch ribs. It is demonstrated that the bridge ambient vibration measurements are enough to identify the dominated vibration modes of such a large-scale bridge. In addition, the proposed singularity entropy based method is implemented to determine the system order of Jian bridge. It is again verified that the proposed singularity entropy based method is stable in the determination of system order. 4. A three-dimensional finite element model of the Jian bridge is established in the thesis. The modal analysis results obtained from finite element calculation have been compared with those identified from the field ambient vibration measurements. A good agreement has been achieved. The calibrated finite element model that reflects the built-up structural dynamic properties can be served as a baseline model in the succeeding dynamic response analysis under complicated excitations, long-term health monitoring and structural condition assessment of the Jian bridge.