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Smart Structures and Systems Volume 35, Number 1, January 2025 , pages 029-38 DOI: https://doi.org/10.12989/sss.2025.35.1.029 |
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Deep learning prediction and evaluation of stability for piezoresistive behavior of the cement sensors |
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ZY Chen, Huakun Wu, Yahui Meng and Timothy Chen
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| Abstract | ||
| Cement-based sensors are exposed to continuous dynamic loading and/or damage, degrading their sensing stability, nevertheless, predictions of long-term sensing stability have rarely been reported. Therefore, this study presents a deep-learning analysis combining experimental data and a LSTM model to predict the stability of long-term piezoresistivity. Related experiments are conducted, and the test results are used as training data. The simulations indicate that the parameters of the LSTM model have a notable effect on the predicted long-term piezoresistive sensing performances of the composites. By comparing the predictions with the experimental results, the validity of the proposed deep-learning approach is evaluated, and the following conclusions can be drawn from this study. | ||
| Key Words | ||
| AI & carbon nanotube; deep-learning; long-term cyclic loading; piezoresistive sensors; prediction and long short-term memory; recurrent neural network | ||
| Address | ||
| (1) ZY Chen, Yahui Meng: School of Science, Guangdong University of Petrochem Technol, Maoming, Guangdong, China; (2) Huakun Wu: School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong, China; (3) Timothy Chen: Division of Engineering and Applied Science, Caltech, CA 91125, USA. | ||