Gaussian Process Strain Pre-extrapolation and Uncertainty Estimation for Inverse Finite Elements

Poloni, Dario; Oboe, Daniele; Sbarufatti, Claudio; Giglio, Marco

doi:10.1007/978-3-031-07258-1_32

Cited by 1 publication

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“…The iFEM has been successfully applied to the monitoring of a container ship [ 19 , 20 ], wind turbines [ 21 , 22 ], and to complex aeronautical structures [ 23 ]. Colombo et al in [ 24 ] developed an iFEM-based damage detection methodology, and current research is expanding towards damage identification and quantification [ 25 , 26 , 27 , 28 ]; very recent developments are in the direction of a non-deterministic displacement reconstruction [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Variable Thickness Strain Pre-Extrapolation for the Inverse Finite Element Method

Poloni

Oboe

Sbarufatti

et al. 2023

Sensors

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The inverse Finite Element Method (iFEM) has recently gained much popularity within the Structural Health Monitoring (SHM) field since, given sparse strain measurements, it reconstructs the displacement field of any beam or shell structure independently of the external loading conditions and of the material properties. However, in principle, the iFEM requires a triaxial strain measurement for each inverse finite element, which is seldom feasible in practical applications due to both costs and cabling-related limitations. To alleviate this problem several techniques to pre-extrapolate the measured strains have been developed, so that interpolated or extrapolated strain values are inputted to elements without physical sensors: the benefit is that the required number of sensors can be reduced. Nevertheless, whenever the monitored components comprise regions of different thicknesses, each region of constant thickness must be extrapolated separately, due to thickness-induced discontinuities in the strain field. This is the case in many practical applications, especially those concerning fiber-reinforced composite laminates. This paper proposes to extrapolate the measured strain field in a thickness-normalized space, where the thickness-induced trends are removed; this novel method can significantly decrease the number of required sensors, effectively reducing the costs of iFEM-based SHM systems. The method is validated in a simple but informative numerical case study, highlighting the potentialities and benefits of the proposed approach for more complex application scenarios.

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