Department: Structural Engineering
Joel Conte | Jose Restrepo | Ahmed Elgamal
Name: Babak Moaveni
Email: bmoaveni @ ucsd.edu
Grad Year: 2007
Xianfei He, x1he @ ucsd.edu
A full-scale seven-story reinforced concrete shear wall building structure was tested on the UCSD-NEES shake table in the period October 2005 - January 2006. The shake table tests were designed so as to damage the building progressively through several his¬torical seismic motions reproduced on the shake table. At various levels of damage, several low amplitude white noise base excita¬tions were applied, through the shake table, to the building which responded as a quasi-linear system with parameters evolving as a function of damage. Different state-of-the-art system identification algorithms were used to estimate the modal parameters (natural frequencies, damping ratios, and mode shapes) based on the measured response of the building subject to ambient as well as white noise base excitations at different damage states. The identified modal parameters obtained using differ¬ent methods are compared to study the performance of these system identification methods. The results obtained in this study are then used to identify damage in the building based on a sensitivity-based finite element model updating algorithm. The damage identification results are verified through comparison with the actual damage observed in the test structure. In addition, the performance of these system identification methods is systematically investigated based on the response of the structure simulated using a three-dimensional nonlinear finite element model thereof. The variability of the identified modal parameters is quantified through effect screening and meta-modeling due to variability of the following input factors: (1) amplitude of input excitation (level of nonlinearity in the response), (2) spatial density of measurements (number of sensors), (3) measurement noise, and (4) length of response data used in the identification process. A full-factorial design of experiments is considered for these four input factors. This systematic investigation demonstrates that the level of confidence which can be placed in structural health monitoring is a function of, not only the magnitude of damage, but also choices made to design the experimental procedure, collect and process the measurements.
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