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Smart Structures and Systems Volume 29, Number 4, April 2022 , pages 589-598 DOI: https://doi.org/10.12989/sss.2022.29.4.589 |
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Monitoring moisture content of timber structures using PZT-enabled sensing and machine learning |
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Lin Chen, Haibei Xiong, Yufeng He, Xiuquan Li and Qingzhao Kong
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| Abstract | ||
| 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., naive 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. | ||
| Key Words | ||
| CFD simulation; moisture content; PZT-enabled sensing; statistic machine learning; structural health monitoring; timber structure | ||
| Address | ||
| Department of Disaster Mitigation for Structures, Tongji University, 1239 Siping Road, Shanghai 200092, Republic of China. | ||