Modal identification of a fan shape tall building by Bayesian FFT Approach using ambient data.

The paper presents work on field instrumentation and modal identification of a 30 storied reinforced concrete structure with a fan-shape building plan. An array of triaxial acceleration measurements under ambient conditions was deployed at the top floor. A number of setups were carried out to cover all locations of interest using a limited number of sensors. A Bayesian FFT approach is used for modal identification using the measured ambient data. The approach views modal identification as an inference problem where probability is used as a measure for the relative plausibility of outcomes given a model of the system and measured data. The Bayesian method yields not only the most probable value of modal parameters, but also their uncertainties. The mode shape of the whole slab is obtained by gluing mode shapes identified from individual setups using a least square approach. The modal identification results reveal a number of interesting features about the building and the instrumented floor. Eight modes were identified, two of which are translational modes and four are torsional modes. Warping of the slab appears in the other two modes.