Nonlinear Identification of Civil Structures using Time and Frequency Domain Data Features.
based structural health monitoring (SHM) approaches are becoming more popular in recent
years for damage identification and response prediction of civil structures. A common
approach in vibration based SHM is model calibration using recorded data. Calibrated
models can be used for damage diagnosis as well as response prediction of linear and
nonlinear structural systems. A... read moremong different types of models, finite element (FE)
models offer advantages to mitigate the modeling errors since they can incorporate the
information from geometry and material behavior of structures. Most of the research in
this area involves updating linear FE models of structures, but linear FE models cannot
reliably be used for damage prognosis since civil engineering structures behave highly
nonlinearly under moderate to high amplitude loadings such as earthquakes. The objective
of this research is to develop a framework for reliable nonlinear structural
identification for robust response prediction and damage identification. The
unidentifiability issue of nonlinear FE models is specifically tackled by implementing
simpler models, more informative data features, and advanced optimization and stochastic
simulation methods. The first part of the thesis is focused on developing a method for
identification of time-varying modal parameters using recorded data. The deterministic
stochastic subspace identification method is applied on short windows of input-output
data for estimating the time-varying modal parameters of nonlinear systems, and it is
shown that the identified values are more accurate than the most common output-only
methods such as the wavelet transform. In the next parts of the research the recorded
time-domain and extracted time-varying frequency-domain data are used for calibration of
nonlinear models for two complex real-world structures, namely a three story infilled
frame and a seven story shear wall building. The effects of modeling errors and used
data features on the performance of the calibrated models are studied. In the last part
of this thesis, the nonlinear model calibration is performed in a probabilistic
framework. The maximum a-posteriori values of the modeling parameters and their
uncertainties are estimated and the effects of using different types of data features on
the estimation uncertainty and accuracy of the models are
Thesis (Ph.D.)--Tufts University, 2015.
Submitted to the Dept. of Civil Engineering.
Advisor: Babak Moaveni.
Committee: Shuchin Aeron, Andre Barbosa, Eleni Chatzi, Masoud Sanayei, and Andreas Stavridis.
Keyword: Engineering.read less