Model Parameter Identification Of Civil Engineering Structures

The study on modal parameter identification of civil engineering structures-theory, implementation and application is support by the Natural Science Foundation of China (NSFC), under grant number 50378021. Civil engineering structures are important part of national infrastructures that are directly related to people's daily life. It is required to understand their dynamic properties during the inspection, assessment and health monitoring of civil engineering structures. Structural modal parameters reflect the dynamic properties that can be obtained by either traditional or ambient vibration identification techniques. Dynamic testing of civil engineering 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 will therefore need to base itself on output-only data. The output-only modal parameter identification is different from traditional one that is based on both input and output data and is now very active and challenging research topic in the system identification of engineering structures. The state-of-the-art studies are carried out in this thesis on the modal parameter identification of civil engineering structures under operational conditions. The research is focused on theory, algorithm, implementation and real case applications of peak-picking method in frequency-domain and stochastic subspace identification in time-domain. The main work and conclusions include as follows: 1. The frequency domain methods of structural modal parameter identification under ambient excitations are comprehensively discussed in the thesis. More focus is on the theory and algorithm of pick-peaking (PP) and frequency domain decomposition (FDD) methods. To improve the peak-picking method, the phase angles of transfer functions are proposed to help to select the correct peaks from the average normal power spectrum densities so that the picked peaks are more accurate and objective. The frequency domain decomposition method is basically the singular value decomposition based peak picking technique. It makes the picked peak more objective and can identify the closed-space modes. The frequency domain decomposition method is now the advanced modal parameter identification method in frequency-domain. 2. The theoretical background and algorithms of both covariance-driven and data-driven stochastic subspace identification (SSI) are studied. The average normalized stabilization diagram algorithm based on stabilization diagram is presented in the thesis. The proposed algorithm can help identifying the modal parameters automatically and is more suitable to the measurements of large-scale civil engineering structures where the measurements are normally divided into several setups. It is demonstrated that both covariance-driven and data-driven stochastic subspace identification techniques can effectively identify the structural modal parameters. Theoretically, the data-driven stochastic subspace identification is more stable and accurate than covariance-driven stochastic subspace identification, but it needs much computing time. In addition, the different weighted methods on the decomposed matrix are compared with the help of numerical examples. 3. A VC based modal analysis software for civil engineering structures-MACES is developed where the algorithms of peak picking method in frequency-domain and stochastic subspace identification in time-domain are implemented. The graphical user interface (GUI) software includes the data pre-processing, parameter identification and post-processing that fit well with the particular features of civil engineering structures. With this software, the whole process of modal parameter identification for real structures can be carried out easily and effectively. It is suggested that peak picking method in frequency-domain and stochastic subspace identification in time-domain can be used complementarily. 4. As real applications, the field ambient vibration tests of Qingzhou cable-stayed bridge and Jian concrete filled steel tubular arch bridge are described in detail. The modal parameters of both bridges have been identified by peak picking method in frequency-domain and stochastic subspace identification in time-domain. The identified results from ambient vibration measurements agree well with those calculated from finite element method. It is demonstrated that the ambient vibration response measurements are sufficient enough to identify the most significant modes of large span cable-stayed bridges, in despite of the rather low level of ambient vibration signal captured, the low range (0-1.0Hz) of natural frequencies of interest,and the relatively dense modes of vibration in that range. With the help of the average normalized stabilization proposed in the thesis, the fake modes can be deleted and modal parameters can be identified accurately. 5. The stochastic subspace identification (SSI) algorithm is an advanced technique to perform such an operational modal analysis. However, a white noise assumption of inputs is compulsory, which may limit the SSI application to real civil engineering structures. The white noise assumption is discussed in the thesis in terms of theoretical analysis, numerical simulation and real case verification. It is illustrated that the stochastic subspace identification can be still validated if the non white noise is generated from a linear and time-invariant shaping filter (a simulated structure). Loosening the white noise assumption of inputs in the stochastic subspace identification is extremely important for the modal parameter identification of civil engineering structures. 6. A newly developed signal processing technique, empirical mode decomposition (EMD), is capable of dealing with non-stationary signals. An EMD-based stochastic subspace identification from operational vibration measurements is presented in the thesis. The output only measurements are first decomposed into the modal response functions by using the EMD technique with the specified intermittency frequencies. The stochastic subspace identification method is then applied to the decomposed signals to identify the modal parameters. A case study of the operational measurements from a real bridge is presented to illustrate the applicability of the present technique. It is demonstrated that the stable pole in the stabilization diagrams becomes sole and the vibration characteristics are easily identified for the decomposed signals ignoring the influence of other modal components and fake frequencies due to unwanted noise.