Smart Structures and Systems

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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)
  • Received : 2021.09.06
  • Accepted : 2021.12.10
  • Published : 2022.04.25
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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., 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

Acknowledgement

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).

References

  1. Chen, J., Xiong, H., Wang, Z. and Yang, L. (2020), "Experimental buckling performance of eucalyptus-based oriented oblique laminated strand lumber columns under centric and eccentric compression", Constr. Build. Mater., 262, 120072. https://doi.org/10.1016/j.conbuildmat.2020.120072
  2. Chen, L., Xiong, H., Sang, X., Yuan, C. and Kong, Q. (2021), "An innovative deep neural network-based approach for internal cavity detection of timber columns using percussion sound", Struct. Health Monitor., 147592172110285. https://doi.org/10.1177/14759217211028524
  3. Couceiro, J., Hansson, L., Sehlstedt-Persson, M., Vikberg, T. and Sandberg, D. (2020), "The conditioning regime in industrial drying of Scots pine sawn timber studied by X-ray computed tomography: a case-study", Eur. J. Wood Wood Prod., 78(4), 673-682. https://doi.org/10.1007/s00107-020-01549-2
  4. Dietsch, P., Franke, S., Franke, B., Gamper, A. and Winter, S. (2014), "Methods to determine wood moisture content and their applicability in monitoring concepts", J. Civil Struct. Health Monitor., 5, 115-127. https://doi.org/10.1007/s13349-014-0082-7
  5. Ebrahimkhanlou, A., Choi, J., Hrynyk, T.D., Salamone, S. and Bayrak, O. (2018), "Detection of the onset of delamination in a post-tensioned curved concrete structure using hidden Markov modeling of acoustic emissions", Proceedings of Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, Denver, CO, USA, March. https://doi.org/10.1117/12.2296624
  6. EN 335-1 (2006), Durability of Wood and Wood-Based Products-Definition of use classes-Part 1: General.
  7. Feng, Q., Kong, Q. and Song, G. (2016), "Damage detection of concrete piles subject to typical damage types based on stress wave measurement using embedded smart aggregates transducers", Measurement, 345-352. https://doi.org/10.1016/j.measurement.2016.01.042
  8. Franke, B., Franke, S. and Muller, A. (2014), "Case studies: long-term monitoring of timber bridges", J. Civil Struct. Health Monitor., 5(2), 195-202. https://doi.org/10.1007/s13349-014-0093-4
  9. Halabe, U., Agrawal, S., Gopalakrishnan, B. and Grushecky, S. (2007), "Defect detection in wooden logs using ground penetrating radar", 894, 1368-1375. https://doi.org/10.1063/1.2718125
  10. Huynh, T.-C. and Kim, J.-T. (2016), "Compensation of temperature effect on impedance responses of PZT interface for prestress-loss monitoring in PSC girders", Smart Struct. Syst., Int. J., 17(6), 881-901. https://doi.org/10.12989/sss.2016.17.6.881
  11. ISO 3131 (1975), Wood-Determination of density for physical and mechanical tests.
  12. Johansson, J., Hagman, O. and Oja, J. (2003), "Predicting moisture content and density of Scots pine by microwave scanning of sawn timber", Comput. Electron. Agricult., 41(1), 85-90. https://doi.org/10.1016/S0168-1699(03)00044-9
  13. Kandemir-Yucel, A., Tavukcuoglu, A. and Caner-Saltik, E.N. (2007), "In situ assessment of structural timber elements of a historic building by infrared thermography and ultrasonic velocity", Infrared Phys. Technol., 49(3), 243-248. https://doi.org/10.1016/j.infrared.2006.06.012
  14. Kurz, J.H. and Boller, C. (2015), "Some background of monitoring and NDT also useful for timber structures", J. Civil Struct. Health Monitor., 5(2), 99-106. https://doi.org/10.1007/s13349-015-0105-z
  15. Kylili, A., Fokaides, P.A., Christou, P. and Kalogirou, S.A. (2014), "Infrared thermography (IRT) applications for building diagnostics: A review", Appl. Energy, 134, 531-549. https://doi.org/10.1016/j.apenergy.2014.08.005
  16. Liu, J.N., He, Y.L., Wang, X.Z. and Hu, Y.X. (2011), "A comparative study among different kernel functions in flexible naive Bayesian classification", Proceedings of 2011 International Conference on Machine Learning and Cybernetics, Guilin, China, July.
  17. Mei, H., Yuan, S., Qiu, L. and Zhang, J. (2016), "Damage evaluation by a guided wave-hidden Markov model based method", Smart Mater. Struct., 25(2), 025021. https://doi.org/10.1088/0964-1726/25/2/025021
  18. Nguyen, K.-D., Ho, D.-D. and Kim, J.-T. (2013), "Damage detection in beam-type structures via PZT's dual piezoelectric responses", Smart Struct. Syst., Int. J., 11(2), 217-240. https://doi.org/10.12989/sss.2013.11.2.217
  19. Nocetti, M., Brunetti, M. and Bacher, M. (2015), "Effect of moisture content on the flexural properties and dynamic modulus of elasticity of dimension chestnut timber", Eur. J. Wood Wood Prod., 73(1), 51-60. https://doi.org/10.1007/s00107-014-0861-1
  20. Palma, P. and Steiger, R. (2020), "Structural health monitoring of timber structures - Review of available methods and case studies", Constr. Build. Mater., 248, 118528. https://doi.org/10.1016/j.conbuildmat.2020.118528
  21. Park, S., Yun, C.B., Roh, Y. and Lee, J.J. (2005), "Health monitoring of steel structures using impedance of thickness modes at PZT patches", Smart Struct. Syst., Int. J., 1(4), 339-353. https://doi.org/10.12989/sss.2005.1.4.339
  22. Park, S., Yun, C.B., Roh, Y. and Lee, J.J. (2006), "PZT-based active damage detection techniques for steel bridge components", Smart Mater. Struct., 15(4), 957-966. https://doi.org/10.1088/0964-1726/15/4/009
  23. Park, S., Lee, J.J., Yun, C.B. and Inman, D.J. (2007), "Abuilt-in active sensing system-based structural health monitoring technique using statistical pattern recognition", J. Mech. Sci. Technol., 21(6), 896-902. https://doi.org/10.1007/BF03027065
  24. Ruangkhasap, S., Noypitak, S., Noknoi, W. and Terdwongworakul, A. (2020), "Non-destructive assessment of moisture content and modulus of rupture of sawn timber Hevea wood using near infrared spectroscopy technique", Proceedings of IOP Conference Series: Materials Science and Engineering, 773, 012065. https://doi.org/10.1088/1757-899X/773/1/012065
  25. Su, Z., Wang, X., Cheng, L., Yu, L. and Chen, Z. (2009), "On selection of data fusion schemes for structural damage evaluation", Struct. Health Monitor., 8(3), 223-241. https://doi.org/10.1177/1475921708102140
  26. Thelandersson, S. and Larsen, H.J. (2003), Timber Engineering, John Wiley & Sons, NJ, USA.
  27. Vitola, J., Pozo, F., Tibaduiza, D.A. and Anaya, M. (2017), "A sensor data fusion system based on k-nearest neighbor pattern classification for structural health monitoring applications", Sensors, 17(2), 417. https://doi.org/10.3390/s17020417
  28. Walker, R., Pavia, S. and Dalton, M. (2016), "Measurement of moisture content in solid brick walls using timber dowel", Mater. Struct., 49(7), 2549-2561. https://doi.org/10.1617/s11527-015-0667-6
  29. Wang, F. and Song, G. (2020), "Monitoring of multi-bolt connection looseness using a novel vibro-acoustic method", Nonlinear Dyn., 1-12. https://doi.org/10.1007/s11071-020-05508-7
  30. Wang, F., Chen, Z. and Song, G. (2021), "Smart crawfish: A concept of underwater multi-bolt looseness identification using entropy-enhanced active sensing and ensemble learning", Mech. Syst. Signal Process., 149, 107186. https://doi.org/10.1016/j.ymssp.2020.107186
  31. Xu, B., Chen, H., Mo, Y.L. and Zhou, T. (2018), "Dominance of debonding defect of CFST on PZT sensor response considering the meso-scale structure of concrete with multi-scale simulation", Mech. Syst. Signal Process., 107, 515-528. https://doi.org/10.1016/j.ymssp.2018.01.041
  32. Xu, L., Yuan, S., Chen, J. and Ren, Y. (2019), "Guided wave-convolutional neural network based fatigue crack diagnosis of aircraft structures", Sensors, 19(16), 3567. https://doi.org/10.3390/s19163567
  33. Yuan, C., Kong, Q., Chen, W., Jiang, J. and Hao, H. (2020), "Interfacial debonding detection in externally bonded bfrp reinforced concrete using stress wave-based sensing approach", Smart Mater. Struct., 29(3), 035039. https://doi.org/10.1088/1361-665X/ab7111
  34. Yuan, C., Zhang, J., Chen, L., Xu, J. and Kong, Q. (2021), "Timber moisture detection using wavelet packet decomposition and convolutional neural network", Smart Mater. Struct., 30(3), 035022. https://doi.org/10.1088/1361-665X/abdc08
  35. Zaidi, S., Aviyente, S., Salman, M., Shin, K.K. and Strangas, E.G. (2011), "Prognosis of Gear Failures in DC Starter Motors Using Hidden Markov Models", IEEE Trans. Ind. Electron., 58(5), 1695-1706. https://doi.org/10.1109/TIE.2010.2052540
  36. Zeng, L., Parvasi, S.M., Kong, Q., Huo, L., Li, M. and Song, G. (2015), "Bond slip detection of concrete-encased composite structure using shear wave based active sensing approach", Smart Mater. Struct., 24(12), 125026. https://doi.org/10.1088/0964-1726/24/12/125026
  37. Zhang, J., Li, Y., Huang, Y., Jiang, J. and Ho, S.-C.M. (2018), "A feasibility study on timber moisture monitoring using piezoceramic transducer-enabled active sensing", Sensors, 18(9), 3100. https://doi.org/10.3390/s18093100