  Using piezoelectric impedance/admittance sensing for structural health
monitoring is promising, owing to the simplicity in circuitry design as well as
the high-frequency interrogation capability. The actual identification of fault
location and severity using impedance/admittance measurements, nevertheless,
remains to be an extremely challenging task. A first-principle based structural
model using finite element discretization requires high dimensionality to
characterize the high-frequency response. As such, direct inversion using the
sensitivity matrix usually yields an under-determined problem. Alternatively,
the identification problem may be cast into an optimization framework in which
fault parameters are identified through repeated forward finite element
analysis which however is oftentimes computationally prohibitive. This paper
presents an efficient data-assisted optimization approach for fault
identification without using finite element model iteratively. We formulate a
many-objective optimization problem to identify fault parameters, where
response surfaces of impedance measurements are constructed through Gaussian
process-based calibration. To balance between solution diversity and
convergence, an -dominance enabled many-objective simulated annealing algorithm
is established. As multiple solutions are expected, a voting score calculation
procedure is developed to further identify those solutions that yield better
implications regarding structural health condition. The effectiveness of the
proposed approach is demonstrated by systematic numerical and experimental case
studies.
