Robust impact localisation on composite aerostructures using kernel design and Bayesian-inspired model averaging under environmental and operational uncertainties
Author(s)
Xiao, Dong
Sharif-Khodaei, Zahra
Aliabadi, MH
Type
Journal Article
Abstract
Impact localisation on composite aircraft structures remains a significant challenge due to operational and environmental uncertainties, such as variations in temperature, impact mass and energy levels. This study proposes a novel Gaussian process regression (GPR) framework that leverages the order invariance of time difference of arrival (TDOA) inputs to achieve probabilistic impact localisation under such uncertainties. A composite (COMP) kernel function, combining radial basis function and cosine similarity kernels, is designed based on wave propagation dynamics to enhance adaptability to diverse conditions. To jointly predict spatial coordinates, a task covariance kernel is incorporated to support multitask learning, allowing the model to capture correlations between outputs. To further improve robustness, a Bayesian-inspired model averaging strategy is employed to fuse predictions from multiple GPR models, assigning adaptive weights based on both global model fit and local predictive confidence. The proposed framework is experimentally validated on a sensorised composite panel under a wide range of impact conditions, including large-mass drop tower tests and small-mass guided impacts, across varying temperatures and angles. Convolutional neural networks, a widely used deep learning method, are adopted as a baseline for comparison. Results demonstrate that the GPR-based approach achieves higher localisation accuracy and robustness without requiring explicit compensation for environmental or loading variations. The study also highlights the critical role of TDOA preprocessing: sample standardisation outperforms feature standardisation by preserving directional structure and improving GPR model compatibility. These findings underscore the method’s potential for reliable, uncertainty-aware structural health monitoring in complex aerospace environments.
Date Issued
2025-09-11
Date Acceptance
2025-09-01
Citation
Structural Health Monitoring, 2025
ISSN
1475-9217
Publisher
SAGE Publications
Journal / Book Title
Structural Health Monitoring
Copyright Statement
© The Author(s) 2025. Creative Commons License (CC BY 4.0) This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
License URL
Publication Status
Published online
Article Number
14759217251362397
Date Publish Online
2025-09-11