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In the near future, Structural Health Monitoring (SHM) technologies will becapable of overcoming the drawbacks in the current maintenance and life-cyclemanagement paradigms, namely: cost, increased downtime, less-than-optimalsafety management paradigm and the limited applicability of fully-autonomousoperations. In the context of SHM, one of the most challenging tasks is damagequantification. Current methods face accuracy and/or robustness issues when itcomes to varying operating and environmental conditions. In addition, thedamage/no-damage paradigm of current frameworks does not offer much informationto maintainers on the ground for proper decision-making. In this study, a novelstructural damage quantification framework is proposed based on widely-usedDamage Indices (DIs) and Gaussian Process Regression Models (GPRMs). Thenovelty lies in calculating the probability of an incoming test DI pointoriginating from a specific state, which allows for probability-educateddecision-making. This framework is applied to three test cases: a CarbonFiber-Reinforced Plastic (CFRP) coupon with attached weights as simulateddamage, an aluminum coupon with a notch, and an aluminum coupon with attachedweights as simulated damage under varying loading states. The state predictionmethod presented herein is applied to single-state quantification in the firsttwo 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 neitherthe damage size nor the load is known in order to predict both simultaneouslyfrom incoming DI test points. In applying this framework, two forms of GPRMs(standard and variational heteroscedastic) are used in order to criticallyassess their performances with respect to the three test cases.

Gaussian Process Regression for Active Sensing Probabilistic Structural  Health Monitoring: Experimental Assessment Across Multiple Damage and Loading  Scenarios