Physics-guided impact localisation and force estimation in composite plates with uncertainty quantification
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912916820721664 |
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| author | Xiao, Dong Sharif-Khodaei, Zahra Aliabadi, M. H. |
| author_facet | Xiao, Dong Sharif-Khodaei, Zahra Aliabadi, M. H. |
| contents | Physics-guided approaches offer a promising path toward accurate and generalisable impact identification in composite structures, especially when experimental data are sparse. This paper presents a hybrid framework for impact localisation and force estimation in composite plates, combining a data-driven implementation of First-Order Shear Deformation Theory (FSDT) with machine learning and uncertainty quantification. The structural configuration and material properties are inferred from dispersion relations, while boundary conditions are identified via modal characteristics to construct a low-fidelity but physically consistent FSDT model. This model enables physics-informed data augmentation for extrapolative localisation using supervised learning. Simultaneously, an adaptive regularisation scheme derived from the same model improves the robustness of impact force reconstruction. The framework also accounts for uncertainty by propagating localisation uncertainty through the force estimation process, producing probabilistic outputs. Validation on composite plate experiments confirms the framework's accuracy, robustness, and efficiency in reducing dependence on large training datasets. The proposed method offers a scalable and transferable solution for impact monitoring and structural health management in composite aerostructures. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_13376 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-guided impact localisation and force estimation in composite plates with uncertainty quantification Xiao, Dong Sharif-Khodaei, Zahra Aliabadi, M. H. Data Analysis, Statistics and Probability Materials Science Machine Learning Applied Physics Physics-guided approaches offer a promising path toward accurate and generalisable impact identification in composite structures, especially when experimental data are sparse. This paper presents a hybrid framework for impact localisation and force estimation in composite plates, combining a data-driven implementation of First-Order Shear Deformation Theory (FSDT) with machine learning and uncertainty quantification. The structural configuration and material properties are inferred from dispersion relations, while boundary conditions are identified via modal characteristics to construct a low-fidelity but physically consistent FSDT model. This model enables physics-informed data augmentation for extrapolative localisation using supervised learning. Simultaneously, an adaptive regularisation scheme derived from the same model improves the robustness of impact force reconstruction. The framework also accounts for uncertainty by propagating localisation uncertainty through the force estimation process, producing probabilistic outputs. Validation on composite plate experiments confirms the framework's accuracy, robustness, and efficiency in reducing dependence on large training datasets. The proposed method offers a scalable and transferable solution for impact monitoring and structural health management in composite aerostructures. |
| title | Physics-guided impact localisation and force estimation in composite plates with uncertainty quantification |
| topic | Data Analysis, Statistics and Probability Materials Science Machine Learning Applied Physics |
| url | https://arxiv.org/abs/2507.13376 |