Gaussian process regression for active sensing probabilistic structural health monitoring: experimental assessment across multiple damage and loading scenarios

Abstract

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. One of the most challenging tasks is structural damage quantification. Existing quantification techniques face accuracy and/or robustness issues when it comes to varying operating and environmental conditions. In addition, the damage/no-damage paradigm of current industrial 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 the widely-used Damage Indices (DIs) and Gaussian Process Regression Models (GPRMs) in order to overcome the aforementioned shortcomings. The proposed framework takes a simple approach to the damage quantification problem by using DI values for training, and provides confidence bounds on the estimated states. In addition, the proposed method is shown to quantify multiple structural states simultaneously from incoming DI test points. This framework is applied to three test cases: a Carbon Fibre-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 novel 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 performance with respect to the three test cases.