Physics-guided impact localisation and force estimation in composite plates with uncertainty quantification
Year: 2026
Type: article
Abstract: Physics-guided learning offers a promising pathway for accurate impact identification in composite structures under sparse experimental data. This paper presents a hybrid framework for impact localisation and force reconstruction that integrates a data-driven First-Order Shear Deformation Theory (FSDT) model with probabilistic machine learning and uncertainty quantification. Structural material properties and boundary conditions are identified from dispersion relations and modal characteristics, enabling construction of a low-fidelity but physically consistent FSDT model directly from measured responses. Physics-augmented low-fidelity time-difference-of-arrival data are fused with sparse experimental measurements using multi-fidelity Gaussian Process Regression to achieve accurate, uncertainty-aware impact localisation. Impact force reconstruction is performed via transfer-function-based deconvolution with an adaptive regularisation scheme informed by FSDT interpolation errors. Experimental validation on a composite flat plate demonstrates accurate localisation and force reconstruction under sparse training data, while additional localisation results on stiffened and sandwich composite panels confirm the generalisability of the localisation framework to more complex structures. The proposed approach provides a computationally efficient and physically interpretable solution for uncertainty-aware impact monitoring in composite aerostructures.
Source: Composite Structures
Authors: Dong Xiao, Zahra Sharif-Khodaei, M.H. Aliabadi
Institution: Imperial College London
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,660
Award: G339839147
OpenAlex ID: W4416280556