Estimation of Soil–Structure Model Parameters for the Millikan Library Building Using a Sequential Bayesian Finite Element Model Updating Technique

We present a finite element model updating technique for soil–structure system identification of the Millikan Library building using seismic data recorded during 2002 Yorba Linda earthquake. A detailed (FE) is developed in OpenSees and updated sequential Bayesian estimation approach joint parameter input identification. two-step devised. First, fixed-base structural to estimate parameters (including effective elastic modulus components, distributed floor mass, Rayleigh damping parameters) some uncertain components foundation-level motion. Then, identified used wherein parameters, stiffness viscosity soil subsystem (modeled substructure approach), foundation motions (FIMs) are estimated. The compared with state-of-practice recommendations. While specific application made Library, work offers framework integrating large-scale FE models measurement inversion. By utilizing this different civil structures earthquake records, key can be estimated from real-world data, which subsequently assessing improving, as necessary, state-of-the-art analysis modeling techniques. This paper presents an effort towards measurements structure, uniform time domain estimation, thus paves way future applications identification, health monitoring, diagnosis structures.