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http://dx.doi.org/10.12989/sss.2022.29.4.589

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)
Publication Information
Smart Structures and Systems / v.29, no.4, 2022 , pp. 589-598 More about this Journal
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., 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.
Keywords
CFD simulation; moisture content; PZT-enabled sensing; statistic machine learning; structural health monitoring; timber structure;
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Times Cited By KSCI : 4  (Citation Analysis)
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