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Deep learning-based LSTM model for prediction of long-term piezoresistive sensing performance of cement-based sensors incorporating multi-walled carbon nanotube

  • Jang, Daeik (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology) ;
  • Bang, Jinho (School of Civil Engineering, Chungbuk National University) ;
  • Yoon, H.N. (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology) ;
  • Seo, Joonho (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology) ;
  • Jung, Jongwon (School of Civil Engineering, Chungbuk National University) ;
  • Jang, Jeong Gook (Division of Architecture and Urban Design, Urban Science Institute, Incheon National University) ;
  • Yang, Beomjoo (School of Civil Engineering, Chungbuk National University)
  • Received : 2022.07.04
  • Accepted : 2022.10.04
  • Published : 2022.11.25

Abstract

Cement-based sensors have been widely used as structural health monitoring systems, however, their long-term sensing performance have not actively investigated. In this study, a deep learning-based methodology is adopted to predict the long-term piezoresistive properties of cement-based sensors. Samples with different multi-walled carbon nanotube contents (0.1, 0.3, and 0.5 wt.%) are fabricated, and piezoresistive tests are conducted over 10,000 loading cycles to obtain the training data. Time-dependent degradation is predicted using a modified long short-term memory (LSTM) model. The effects of different model variables including the amount of training data, number of epochs, and dropout ratio on the accuracy of predictions are analyzed. Finally, the effectiveness of the proposed approach is evaluated by comparing the predictions for long-term piezoresistive sensing performance with untrained experimental data. A sensitivity of 6% is experimentally examined in the sample containing 0.1 wt.% of MWCNTs, and predictions with accuracy up to 98% are found using the proposed LSTM model. Based on the experimental results, the proposed model is expected to be applied in the structural health monitoring systems to predict their long-term piezoresistice sensing performances during their service life.

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Acknowledgement

This work was supported by the National Research Foundation of Korea grant funded by the Korean government (MSIT) (2020R1C1C1005063 and 2022R1A4A3029737). In addition, this study was supported by a grant (21CTAP-C163988-01) from Ministry of Land, Infrastructure and Transport (MOLIT) of Korea government and Korea Agency for Infrastructure Technology Advancement (KAIA).