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This paper reviews the theoretical principles of subspace system identification as applied to the problem of estimating black‐box state‐space models of support‐excited structures (e.g., structures exposed to earthquakes). The work distinguishes itself from past studies by providing readers with a powerful geometric interpretation of subspace operations that relates directly to theoretical structural dynamics. To validate the performance of subspace system identification, a series of experiments are conducted on a multistory steel frame structure exposed to moderate seismic ground motions; structural response data is used off‐line to estimate black‐box state‐space models. Ground motions and structural response measurements are used by the subspace system identification method to derive a complete input–output state‐space model of the steel frame system. The modal parameters of the structure are extracted from the estimated input–output state‐space model. With the use of only structural response data, output‐only state‐space models of the system are also estimated by subspace system identification. The paper concludes with a comparison study of the modal parameters extracted from the input–output and output‐only state‐space models in order to quantify the uncertainties present in modal parameters extracted from output‐only models. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
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Natural Hazards - Many countries aim to efficiently allocate limited national research and development resources to produce optimal research. A proportion of limited national research and... 相似文献
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A theoretical framework is presented for the estimation of the physical parameters of a structure (i.e., mass, stiffness, and damping) from measured experimental data (i.e., input–output or output‐only data). The framework considers two state‐space models: a physics‐based model derived from first principles (i.e., white‐box model) and a data‐driven mathematical model derived by subspace system identification (i.e., black‐box model). Observability canonical form conversion is introduced as a powerful means to convert the data‐driven mathematical model into a physically interpretable model that is termed a gray‐box model. Through an explicit linking of the white‐box and gray‐box model forms, the physical parameters of the structural system can be extracted from the gray‐box model in the form of a finite element discretization. Prior to experimental verification, the framework is numerically verified for a multi‐DOF shear building structure. Without a priori knowledge of the structure, mass, stiffness, and damping properties are accurately estimated. Then, experimental verification of the framework is conducted using a six‐story steel frame structure under support excitation. With a priori knowledge of the lumped mass matrix, the spatial distribution of structural stiffness and damping is estimated. With an accurate estimation of the physical parameters of the structure, the gray‐box model is shown to be capable of providing the basis for damage detection. With the use of the experimental structure, the gray‐box model is used to reliably estimate changes in structural stiffness attributed to intentional damage introduced. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
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