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Probabilistic Output-Only Identification of Soil-Structure Systems Using Shear Beam Model

Masoudifar, Mohsen | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52063 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Mahsuli, Mojtaba; Ghahari, Farid
  7. Abstract:
  8. This paper proposes a novel probabilistic framework for output-only identification of a soil-structure system. The system is modeled by a vertical shear beam resting on the soil representative springs. The proposed framework estimates the unknown parameters of the system and the foundation input motion time history simultaneously, using sparsely measured responses of the structure. The unknown parameters of the system include stiffness of the sway and rocking springs, shear modulus of the beam, and the modal damping ratios of the system. These parameters are modeled as random variables whose joint probability distribution is updated in a Bayesian scheme using the observations of structural responses. To this end, extended Kalman filter with unknown-input method is employed. At each time step, an a priori estimate of the identification parameters is produced through dynamic equations of the system. Next, the system response is predicted using the a priori estimate. Finally, using measured responses of the system, the a priori estimates of the identification parameters are updated to produce the a posteriori estimates. In fact, this method picks a point between the prediction and the measurement as the a posteriori estimate such that the sum of the variances of the identification parameters is minimized. This sequence of predication-update procedure is repeated for the next time steps. The results include the mean estimate of the identification parameters and foundation input motion along with an estimate of their uncertainties. Using the continuous shear beam as the representative of the structure leads to a computationally feasible framework for structural health monitoring and damage identification of building structures at a regional scale. Thus, comparing the probability distribution of the stiffness of the system before and after a seismic event facilitates online or semi-online damage detection. The proposed approach is verified through a synthetic example comprising a shear beam resting on sway and rocking representative springs of the soil-foundation system. The simulated absolute acceleration responses of the system to a horizontal seismic excitation are polluted with artificial measurement noise. Thereafter, the noisy responses of the beam are used to identify the unknown parameters and the time history of the ground acceleration. The results show that the unknown parameters are identified with a residual error below 1% and 5% in the presence of low and medium measurement noise, respectively. This holds true even for sparsely instrumented structure. Moreover, the shear modulus of the beam and the stiffness of the rocking spring are identified with an error of less than 1% in all cases. Furthermore, the foundation input motion is predicted accurately for the range of frequencies that dominate the input. Finally, the identifiability of parameters is investigated through a sensitivity analysis. The results show that the shear modulus and stiffness of the rocking spring are the most identifiable parameters and that the other parameters are sufficiently identifiable
  9. Keywords:
  10. Output-Only Identification ; Extended Kalman Filter ; Shear Beam ; Soil-Structure Interaction ; Impedance Function

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