Structural Health Monitoring Using Smart Sensors - ideals ...

eing examined (Moore et al., 2001). SHM potentially offers better and more efficient
understanding of structural conditions.
In addition to structural deterioration due to aging, SHM can be employed to estimate
structural damage due to severe loading events, such as earthquakes, hurricanes, or
tornados, facilitating more timely recovery from such disasters. It is imperative that
emergency facilities and evacuation routes, including bridges and highways, be assessed
for safety. Traditional detailed assessments can be significantly expensive and timeconsuming,
as was seen after the 1994 Northridge earthquake with the numerous buildings
that needed to have their moment-resisting connections inspected. Additionally, structures
that are internally, but not obviously, damaged in an earthquake may be in great danger of
collapse during aftershocks; structural integrity assessment can help to identify such
structures to enable evacuation of building occupants and contents prior to aftershocks.
SHM enhances safety and reliability of civil infrastructure after such disastrous events.
Though the necessity is clear, the features of civil infrastructure pose difficulties to
SHM. These features include large scale, a myriad of elements, one-of-a-kind designs,
low natural frequencies, structural redundancy, nontrivial test excitation, challenging
input force estimation, long and continuous service time, and variable environmental
conditions. For example, changes in resonant frequencies of redundant structures, which
often imply structural damage, can be insignificant as compared to frequency shifts due to
changes in ambient conditions, such as temperature and support stiffness (Aktan et al.,
1994; Chowdhury, 1990; Farrar et al., 1994; Salawu, 1997; Tang & Leu, 1989). Many
researchers have been working to overcome these difficulties.
Doebling et al. (1996) reviewed research on vibration-based damage identification
and health monitoring. The cited papers were categorized based on the method, such as
frequency change, mode shape change, mode shape curvature, flexibility, matrix update,
nonlinear methods, and neural network-based methods. Sohn et al. (2003) reviewed
papers on SHM published between 1996 and 2001. A summary of statistical approaches
for damage detection was also included. Other review papers include Salawu and
Williams (1995), Salawu (1997), Doebling et al. (1998), Doebling and Farrar (1999), and
Farrar et al. (2003). Major algorithms for SHM are briefly described below.
Most of the SHM methods mentioned above employ modal analysis to obtain modal
parameters such as natural frequencies, damping ratios, and mode shapes. A number of
modal analysis methods have been proposed. Peak-picking is a simple frequency domain
modal analysis method. Improvements by incorporating the coherence function and using
frequency domain decomposition (Brincker et al., 2001) have also been reported.
Maximum likelihood identification, which includes curve-fitting, estimates modal
parameters by minimizing an error norm in the frequency domain. Time domain modal
analysis methods include the complex-exponential method (Maia & Silva, 1997); Ibrahim
time domain method (Ibrahim & Mikulcik, 1977); ERA (Juang & Pappa, 1985); and
stochastic subspace identification (Hermans & Auweraer, 1999). Identified modal
parameters are further analyzed for damage detection.
Natural frequency changes reflect structural conditions, offering clues to estimate the
structural conditions. By analyzing the natural frequency change, the cause of structural
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