The dynamic characterization of structures is essential for assessing their response when subjected to dynamic loads in structural health monitoring. It mainly comprises the modal parameters, that is, the natural frequencies, damping ratios and mode shapes. These modal properties are attracting more attention when structures are under construction or operation for the researchers, structure owner and engineers. This paper presents the work on the operational modal analysis of a super tall building-the Shanghai Tower with a height of 632 m situated in Shanghai, China. A recently developed fast Bayesian method is utilized to perform modal identification, providing an effective means to identify the modal properties and assess their accuracy. In this study, ambient vibration tests are implemented in different construction stages. The corresponding modal properties and their associated uncertainties are identified and investigated, with interesting trends observed. Finite element models are also established to obtain the modal parameters in different stages and compared with the identified results. After the main structure is completed, a field test covering the eight corners of the core wall in a typical floor is performed to investigate the mode shapes. Afterward, a 12-h measurement is performed with the information of temperature and humidity recorded simultaneously. The variation of modal properties with changing environment is studied. The results obtained will be beneficial for understanding the modal properties of this super tall building and provide a baseline for future structural health monitoring.
Structural model updating aims at calculating the in-situ structural properties (e.g., stiffness and mass) based on measured responses. One common approach is to first identify the modal parameters (i.e., natural frequencies and mode shapes) and then use them to update the structural parameters. In reality, the degrees of freedom that can be measured are usually limited by number of available sensors and accessibility of targeted measurement locations. Then, multiple setups are designed to cover all the degrees of freedom of interest and performed sequentially. Conventional methods do not account for identification uncertainty, which becomes critical when excitation information is not available. This is the situation in model updating utilizing ambient vibration data, in which the excitations, such as wind, traffic, and human activities, are random in nature and difficult to be measured. This paper develops a Bayesian model updating method incorporating modal identification information in multiple setups.Based on a recent fundamental two-stage Bayesian formulation, the posterior uncertainty of modal parameters is incorporated into the updating process without heuristics that are commonly applied in formulating the likelihood function. Synthetic and experimental data are used to illustrate the proposed method.
This paper presents the work on the structural health monitoring design and operational modal analysis of a 250-m super-tall building situated in Shanghai, China. The building is a steel-concrete composite structure with a steel composite frame-concrete core tube system. At the 21st and 36th-38th floors, outrigger trusses and ring-shaped trusses are set to strengthen this structure.Because the height of this structure is overlimited and its lateral stiffness in the vertical direction is nonuniform, a SHM system was designed to monitor the structural condition and evaluate its safety. The SHM system is presented in this paper, and the instrumented equipment includes accelerometers and tilt sensors. The locations of sensors were well arranged so that SHM could be conducted using the least amount of sensors. On the basis of the system, an ambient vibration test was carried out to perform the analysis with four setups designed to investigate the modal parameters in the X and Y directions. The Fast Bayesian FFT method was employed to perform the operational modal analysis. The first 10 modes were identified. The modal parameters obtained by the Bayesian method are studied and discussed, and they are compared with the results obtained by other methods. Long-term monitoring of this super-tall building was also carried out to investigate the change of modal parameters in different stages. The results of this study are expected to provide a reference for model updating, damage detection, and SHM of this high-rise building.
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