Abstract
AI-oriented schemes, in particular deep learning schemes, provide superior capabilities of representative learning that leads to innovative estimation paradigm for structural health monitoring (SHM) applications. This paper introduces a model-driven and deep learning-enabled framework for localizing dynamic impact loads on structures. In this paper, finite element modeling (FEM) is conducted to generate enough labeled data for supervised learning. Meanwhile, a hybrid deep neural network (DNN) is established by integrating attentive and recurrent neural networks to exploit the latent features over both sensor-wise and temporal scales. The proposed DNN model is implemented to reveal the multivariate and temporal hidden correlations among complex time-series measurements and to estimate impact localization on structures. The experimental results from both numerical and physical tests demonstrate the superior performance of the proposed methodology.
This work is supported by SGCC Laiwu, Shandong (SGSDLW00SDJS2250019).
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Luan, Y., Li, T., Song, R., Zhang, W. (2022). Data-Driven Hybrid Neural Network Under Model-Driven Supervised Learning for Structural Dynamic Impact Localization. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13605. Springer, Cham. https://doi.org/10.1007/978-3-031-20500-2_29
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