Damage Assessment of an Aircraft’s Wing Spar Using Gaussian Process Regressors

Adrielly H. Razzini, Michael D. Todd*, Iddo Kressel, Yoav Offir, Moshe Tur, Tal Yehoshua

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


In this work, the beginning of developing a structural health monitoring (SHM) approach is presented for a representation of an aircraft composite wing spar. Lack of directly available field performance data is mitigated using a high-fidelity finite element model and a probabilistic understanding of the aerodynamic loads under different flight regimes, simulating realizations of the spar’s performance in service. Debonding damage between laminates was included in the model at different locations in the spar, with various damage sizes. Under the expectation of a fiber optic measurement system being used for data collection, the target measurements are uniaxial strain, measured in several paths throughout the spar. Given measured strain, the damage assessment problem is probabilistically formulated by defining local buckling from debonding as the observable damage, which is fundamentally characterized by load-dependent buckling eigenvalues. This FE physical model is highly computationally intensive, so machine learning was used to build a “run time” surrogate model to learn the relationships between inputs – loads and damage conditions, and outputs – strain and buckling eigenvalues. In addition, other surrogate models were created to solve the inverse problem, linking strain data to damage classification (size and location). Finally, the probabilistic frameworks are demonstrated and damage criticality assessment, which is directly related to the buckling load, is performed via Gaussian process regression.

Original languageEnglish
Title of host publicationEuropean Workshop on Structural Health Monitoring, EWSHM 2022, Volume 3
EditorsPiervincenzo Rizzo, Alberto Milazzo
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
ISBN (Print)9783031073212
StatePublished - 2023
Event10th European Workshop on Structural Health Monitoring, EWSHM 2022 - Palermo, Italy
Duration: 4 Jul 20227 Jul 2022

Publication series

NameLecture Notes in Civil Engineering
Volume270 LNCE
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565


Conference10th European Workshop on Structural Health Monitoring, EWSHM 2022


  • Composite materials
  • Finite elements
  • Gaussian process regressors
  • Structural health monitoring
  • Surrogate modelling


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