A hybrid model-data-driven framework for inverse load identification of interval structures based on physics-informed neural network and improved Kalman filter algorithm

Accurately capturing data on the external loads that large structural systems endure is crucial for improving the performance of energy equipment. This paper introduces a novel hybrid model-data-driven framework for the dynamic load identification of interval structures, which seamlessly combines fi...

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Bibliographic Details
Published in:	Applied energy Vol. 359; p. 122740
Main Authors:	Liu, Yaru, Wang, Lei, Ng, Bing Feng
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.04.2024
Subjects:	Dynamic load identification Interval structures Kalman filter algorithm Model-data-drive method Physics-informed neural network Dynamic load identification Model-data-drive method Physics-informed neural network Interval structures Kalman filter algorithm
ISSN:	0306-2619, 1872-9118
Online Access:	Get full text
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Summary:	Accurately capturing data on the external loads that large structural systems endure is crucial for improving the performance of energy equipment. This paper introduces a novel hybrid model-data-driven framework for the dynamic load identification of interval structures, which seamlessly combines finite-element modeling with machine learning techniques. To address potential ill-posed issues in model-driven methods and the interpretability limitations of data-driven methods, we propose a physics-informed neural network. This neural network effectively inverts uncertain modal responses with low data requirements and high predictive performance high by integrating the underlying modal transformation equation into the loss function of a fully connected neural network. To identify the modal loads using predicted modal displacement/acceleration responses, we introduce a pioneering dynamics inversion method. This method modifies the traditional Kalman filter with an assumption of unknown inputs to reduce the sensitivity of load identification process to different noises. In addition, our approach incorporates a subinterval Chebyshev expansion method to adaptively determine the interval boundaries of external loads. The efficiency of the proposed method is assessed through two numerical examples and validated through comparative research against baseline methods. Our findings suggest that this approach enhances precision, robustness, and generalization in dynamic load identification, even when facing challenges such as limited training data, significant noise interference, and non-zero initial conditions. •A novel hybrid model-data-driven dynamic load identification method for interval structures is proposed.•A physics-informed neural network is established to inverse the uncertain modal responses effectively.•Two improved Kalman filter algorithms are developed to identify the modal loads at specified parameters.
ISSN:	0306-2619 1872-9118
DOI:	10.1016/j.apenergy.2024.122740