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Proceedings of the International Conference on
Advances and New Challenges in Earthquake
Engineering Research, Hong Kong Volume
*''
SIGNATURE RECOGNITION OF STRUCTURAL DAMAGE: DATA
ANALYSIS AND MODEL-BASED VALIDATION FOR
DESTRUCTIVE FIELD TESTS
Ray Ruichong Zhang 1 , Matt MacRostie*, and Y.L. Xu 2
1 Division of Engineering, Colorado School of Mines, Golden, CO 80401, USA
2 Department of Structural Engineering, Hong Kong Polytechnic University, Hong Kong
ABSTRACT
This study uses the Hilbert-Huang transform (HHT), a method for nonlinear, nonstationary data
processing, to analyze recordings of destructive vibration tests of substructures in Trinity River Relief
(TRR) Bridge in Texas in its intact, minor- and severe-damage stages. It shows that the HHT method
can identify the natural frequencies of the structure from the mixed frequency content in a recording
that also contains the time-dependent excitation and noise frequencies, and then to quantify the
downshift of frequencies for damaged structure relative to frequencies in the non-damaged structure.
The above assertion is also validated by an ANSYS model-based analysis.
INTRODUCTION
The most common damage of bridge piers and abutments is caused by scour from floods, which cannot
be visualized nor calculated by normal hydraulic and geotechnical analysis procedures. Using
vibration measurements might aid in the detection of the damage. Without appropriate data analysis
methods, however, it will not identify minor damage. For example, conventional (e.g., Fourier-based)
data processing/analysis techniques may yield distorted, indirect, or incomplete information about
vibration motion that is inherently nonstationary and also likely the result of a nonlinear dynamic
process. This will mislead the consequent use of the data for damage signature recognition.
Because of its ability to faithfully characterize nonlinear (time-varying amplitude and frequency) and
nonstationary data, the HHT (Huang et al. 1998) can provide an alternative tool for data analysis and
subsequent applications to damage-signature recognition. The HHT method builds on Empirical Mode
Decomposition (EMD) and Hilbert Spectral Analysis (HSA). Based on the local characteristic time
scale of any complicated time series, the EMD decomposes the data set into a finite, often small
number of intrinsic mode functions (IMF) that admit a well-behaved Hilbert transform. The HSA of
each IMF then defines instantaneous or time-dependent frequencies of the data that have physical
meaning, unlike the HSA of the original data (Huang et al., 1998). The EMD, HSA, or their
combination referred to as the HHT typically helps recover useful information from the data sets under