Structural Health Monitoring Using Smart Sensors - ideals ...

Nonlinearity of structural behavior due to structural damage has also been studied.
Crack opening and closing was investigated by Actis and Dimarogonas (1989) and
Krawczuk and Ostachowicz (1992). Lin and Ewins (1990) contemplated response level
dependencies of structural properties utilizing modal properties measured at different
response level. Sohn et al. (2007) proposed damage diagnostics based on the concept of
time reversal acoustics and consecutive outlier analysis to detect nonlinearity between a
pair of sensors.
Sohn et al. (2003) emphasized the importance of statistical models to enhance the
SHM process. Statistical model development can be classified as supervised learning and
unsupervised learning. Sohn et al. (2003) discussed supervised learning methods such as
neural networks (Bishop, 1994; Nakamura et al., 1998; Zhao et al., 1998), genetic
algorithms, support vector machines (Vapnik, 1998), outlier detection, and hypothesis
testing. These supervised learning algorithms are first trained by structures of known
properties or FEM models, and then applied to the structures to be tested. Therefore, this
algorithm may require significant training with the undamaged structure as well as
structures damaged in one or more of the failure modes. While this training may be
possible for structures produced in larger lots (e.g., aircraft), the training is nontrivial for
unique structures (e.g., buildings and bridges). On the other hand, unsupervised learning
may detect abnormalities, or damage presence, but locating damage and assessing the
severity need further theoretical development. Sohn et al. (2002) proposed to combine
AR-ARX model, nonlinear principal component analysis, and statistical analysis to
distinguish environmental and structural changes. Computer simulation and experimental
results proved that this method can detect damage. By placing sensors at each DOF and
applying this procedure for each sensor, Sohn et al. (2002) successfully located damage of
an 8 DOF experimental model under environmental change.
Bernal (2002) proposed a flexibility-matrix-based damage localization method, the
Damage Locating Vector (DLV) method. The DLV method has advantages in that
structural responses do not need to be measured at all the DOFs, though small numbers of
sensors result in limited damage detection capability. A set of load vectors, designated as
DLVs, were computed from the change in the flexibility matrix. The flexibility matrix can
be dynamically estimated. When the DLVs are applied as static forces on the undamaged
structure, the stress field in the structure bypasses the damage areas. This unique
characteristic of the DLVs can be employed to localize damage in the structure.
Although many methods have been proposed, none have proven to be sufficient for
full-scale application. Many of the SHM algorithms mentioned above have been shown to
detect damage well when a large enough number of modes are accurately measured at all
the DOFs. Even the DLV method, which does not require response measurements at all
the DOFs, performs poorly when used with a small number of sensors. The more sensors
used, the more information about structures SHM is expected to give. However, structures
are usually large and have numerous DOFs; accurate and thorough measurements have
been impractical. Sensors have limited accuracy and the associated installation cost,
including cabling, has been prohibitively expensive. For example, Lynch and Loh (2006)
cited Farrar (2001), which reported that the cost of installing over 350 sensing channels on
the Tsing Ma Bridge in Hong Kong was more than $8 million. Celebi (2002) estimated
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