Smart Structures and Systems

  • 제29권4호
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  • Pages.589-598
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  • 2022
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Monitoring moisture content of timber structures using PZT-enabled sensing and machine learning

  • Chen, Lin (Department of Disaster Mitigation for Structures, Tongji University) ;
  • Xiong, Haibei (Department of Disaster Mitigation for Structures, Tongji University) ;
  • He, Yufeng (Department of Disaster Mitigation for Structures, Tongji University) ;
  • Li, Xiuquan (Department of Disaster Mitigation for Structures, Tongji University) ;
  • Kong, Qingzhao (Department of Disaster Mitigation for Structures, Tongji University)
  • 투고 : 2021.09.06
  • 심사 : 2021.12.10
  • 발행 : 2022.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

Timber structures are susceptible to structural damages caused by variations in moisture content (MC), inducing severe durability deterioration and safety issues. Therefore, it is of great significance to detect MC levels in timber structures. Compared to current methods for timber MC detection, which are time-consuming and require bulky equipment deployment, Lead Zirconate Titanate (PZT)-enabled stress wave sensing combined with statistic machine learning classification proposed in this paper show the advantage of the portable device and ease of operation. First, stress wave signals from different MC cases are excited and received by PZT sensors through active sensing. Subsequently, two non-baseline features are extracted from these stress wave signals. Finally, these features are fed to a statistic machine learning classifier (i.e., naïve Bayesian classification) to achieve MC detection of timber structures. Numerical simulations validate the feasibility of PZT-enabled sensing to perceive MC variations. Tests referring to five MC cases are conducted to verify the effectiveness of the proposed method. Results present high accuracy for timber MC detection, showing a great potential to conduct rapid and long-term monitoring of the MC level of timber structures in future field applications.

키워드

과제정보

The authors greatly appreciate the financial support of the National Natural Science Foundation of China (Grant number 52020105005; 51978502) and the Science and Technology Commission of Shanghai Municipality (Grant number 20692114200; 19DZ1202502).

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