System identification of a building from multiple seismic records

Abstract

This paper describes the identification of finite dimensional, linear, time-invariant models of a 4-story building in the state space representation using multiple data sets of earthquake response. The building, instrumented with 31 accelerometers, is located on the University of California, Irvine campus. Multiple data sets, recorded during the 2005 Yucaipa, 2005 San Clemente, 2008 Chino Hills and 2009 Inglewood earthquakes, are used for identification and validation. Considering the response of the building as the output and the ground motion as the input, the state space models that represent the underlying dynamics of the building in the discrete-time domain corresponding to each data set are identified. The time-domain Eigensystem Realization Algorithm with the Observer/Kalman filter identification procedure are adopted in this paper, and the modal parameters of the identified models are consistently determined by constructing stabilization diagrams. The four state space models identified demonstrate that the response of the building is amplitude dependent with the response frequency and damping, being dependent on the magnitude of ground excitation. The practical application of this finding is that the consistency of this building response to future earthquakes can be quickly assessed, within the range of ground excitations considered (0.005g-0.074g), for consistency with prior response-this assessment of consistent response is discussed and demonstrated with reference to the four earthquake events considered in this study. Inclusion of data sets relating to future earthquakes will enable the findings to be extended to a wider range of ground excitation magnitudes. the system. In what follows, the identification problem reduces to the estimation of unknown parameters guided by observation data [1].Identification techniques require excitation (input) and response (output) measurements for a complete determination of a model. In order to obtain input and output data, one needs to perform an experiment/test on the system/structure under study. For instance, in modal testing, it is a common practice to excite the test structure by applying measurable excitations at several points, then collect response data at the sensor locations [2]. However, many civil engineering structures are difficult to excite artificially due to their large size, geometry and location. Equally a large amount of external energy is needed to excite an entire structure at a desired level of vibration. Besides, even if artificial excitation is provided, civil engineering structures, which by necessity are tested in situ, continue to be excited by other unmeasurable forces (wind, waves and traffic for example) and it is thus not possible to confidently declare that any measured response is necessarily only due to any artificial excitation provided. In order to deal in part with such difficulties, some identification algorithms have been developed based on output only vibration data due to ambient and environmental forces, i.e. traffic, wind...

Structural Dynamic Parameter Identification and the Effect of Test Techniques

“…On the other hand Beyen and Kutanis [3] found that there could be variations in peak values between forced and ambient vibration responses which may indicate an amplitude dependency of modal parameters depending on test method used. Amplitude dependency was also observed by Ulusoy et al [4] when examining the behaviour of a multi-storey building during earthquakes of different magnitudes. The reported amplitude dependency was attributed to joint and structural interfaces being more significantly mobilised as earthquake magnitude increased.…”

“…The concept of amplitude dependent response is not novel; Ulusoy et al [4] found that the modal parameters identified for a multi-storey building varied depending on earthquake amplitude. It's interesting to note though that in their case the effect of increased vibration level was an apparent reduction in stiffness due to greater joint mobilisation.…”

Smart wireless sensor system for lifeline health monitoring under a disaster event

“…Multiple seismic data sets, during the 2005 Yucaipa, 2005 San Clemente, 2008 Chino Hills and 2009 Inglewood earthquakes were recorded by 31 accelerometers in CalIT2 building located on the University of California, Irvine campus. 5 From these earthquake data, we observe that the seismic frequency range is below 20 Hz. These recorded data allow us to distinguish earthquake events from pipeline rupture events.…”

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“…From (11), it easily follows that such a covariance matrix is equal to 1 σ 2 Π −1 m−1 . From (16), one has…”

“…These models find applications in various disciplines, such as signal processing and speech enhancement [2], [5], communications [3], [12], [15], biomedical sciences [11] and structural engineering [16]. Some of these applications are concerned with spatiotemporal models, in the sense that the measured output can be strictly related to the location at which the relative sensor is placed [14], [19], [18].…”