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A deep feed-forward neural network for damage detection in functionally graded carbon nanotube-reinforced composite plates using modal kinetic energy

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Abstract

This paper proposes a new Deep Feed-forward Neural Network (DFNN) approach for damage detection in functionally graded carbon nanotube-reinforced composite (FG-CNTRC) plates. In the proposed approach, the DFNN model is developed based on a data set containing 20 000 samples of damage scenarios, obtained via finite element (FE) simulation, of the FG-CNTRC plates. The elemental modal kinetic energy (MKE) values, calculated from natural frequencies and translational nodal displacements of the structures, are utilized as input of the DFNN model while the damage locations and corresponding severities are considered as output. The state-of-the art Exponential Linear Units (ELU) activation function and the Adamax algorithm are employed to train the DFNN model. Additionally, in order to enhance the performance of the DFNN model, the mini-batch and early-stopping techniques are applied to the training process. A trial-and-error procedure is implemented to determine suitable parameters of the network such as the number of hidden layers and the number of neurons in each layer. The accuracy and capability of the proposed DFNN model are illustrated through two distinct configurations of the CNT-fibers constituting the FG-CNTRC plates including uniform distribution (UD) and functionally graded-V distribution (FG-VD). Furthermore, the performance and stability of the DFNN model with the consideration of noise effects on the input data are also investigated. Obtained results indicate that the proposed DFNN model is able to give sufficiently accurate damage detection outcomes for the FG-CNTRC plates for both cases of noise-free and noise-influenced data.

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Acknowledgements

This research was funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under No. 107.02-2019.330.

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Authors and Affiliations

  1. Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, 700000, Vietnam

    Huy Q. Le, Tam T. Truong & T. Nguyen-Thoi

  2. Faculty of Civil Engineering, Ho Chi Minh City University of Transport, Ho Chi Minh City, 700000, Vietnam

    Huy Q. Le

  3. Division of Construction Computation, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, 700000, Vietnam

    D. Dinh-Cong

  4. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, 700000, Vietnam

    Tam T. Truong, D. Dinh-Cong & T. Nguyen-Thoi

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Le, H.Q., Truong, T.T., Dinh-Cong, D. et al. A deep feed-forward neural network for damage detection in functionally graded carbon nanotube-reinforced composite plates using modal kinetic energy. Front. Struct. Civ. Eng. 15, 1453–1479 (2021). https://doi.org/10.1007/s11709-021-0767-z

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