  In the near future, Structural Health Monitoring (SHM) technologies will be
capable of overcoming the drawbacks in the current maintenance and life-cycle
management paradigms, namely: cost, increased downtime, less-than-optimal
safety management paradigm and the limited applicability of fully-autonomous
operations. In the context of SHM, one of the most challenging tasks is damage
quantification. Current methods face accuracy and/or robustness issues when it
comes to varying operating and environmental conditions. In addition, the
damage/no-damage paradigm of current frameworks does not offer much information
to maintainers on the ground for proper decision-making. In this study, a novel
structural damage quantification framework is proposed based on widely-used
Damage Indices (DIs) and Gaussian Process Regression Models (GPRMs). The
novelty lies in calculating the probability of an incoming test DI point
originating from a specific state, which allows for probability-educated
decision-making. This framework is applied to three test cases: a Carbon
Fiber-Reinforced Plastic (CFRP) coupon with attached weights as simulated
damage, an aluminum coupon with a notch, and an aluminum coupon with attached
weights as simulated damage under varying loading states. The state prediction
method presented herein is applied to single-state quantification in the first
two test cases, as well as the third one assuming the loading state is known.
Finally, the proposed method is applied to the third test case assuming neither
the damage size nor the load is known in order to predict both simultaneously
from incoming DI test points. In applying this framework, two forms of GPRMs
(standard and variational heteroscedastic) are used in order to critically
assess their performances with respect to the three test cases.
