The in-situ measurement of thermal stress in civil and mechanical structures may prevent
structural anomalies such as unexpected buckling. In the first half of the dissertation, we present
a study where highly nonlinear solitary waves (HNSWs) were utilized to measure axial stress in
slender beams. HNSWs are compact non-dispersive waves that can form and travel in nonlinear
systems such as one-dimensional chains of particles. The effect of the axial stress acting in a
beam on the propagation of HNSWs was studied. We found that certain features of the solitary
waves enable the measurement of the stress.
In general, most guided ultrasonic waves (GUWs)-based health monitoring approaches
for structural waveguides are based on the comparison of testing data to baseline data. In the
second half of the dissertation, we present a study where some baseline-free signal processing
algorithms were presented and applied to numerical and experimental data for the structural
health monitoring (SHM) of underwater or dry structures. The algorithms are based on one or
more of the following: continuous wavelet transform, empirical mode decomposition, Hilbert
transform, competitive optimization algorithm, probabilistic methods. Moreover, experimental
data were also processed to extract some features from the time, frequency, and joint timefrequency
domains. These features were then fed to a supervised learning algorithm based on
artificial neural networks to classify the types of defect. The methods were validated using the
numerical model of a plate and a pipe, and the experimental study of a plate in water. In
experiment, the propagation of ultrasonic waves was induced by means of laser pulses or
transducer and detected with an array of immersion transducers. The results demonstrated that
the algorithms are effective, robust against noise, and able to localize and classify the damage
