Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Thermography-based coating thickness estimation for steel structures using model-agnostic meta-learning

  • Jun Lee (Department of Civil Engineering, Korean Advanced Institute for Science and Technology) ;
  • Soonkyu Hwang (Yield Enhancement Team, Global Infra Technology, Samsung Electronics) ;
  • Kiyoung Kim (Department of Civil Engineering, Korean Advanced Institute for Science and Technology) ;
  • Hoon Sohn (Department of Civil Engineering, Korean Advanced Institute for Science and Technology)
  • Received : 2022.11.24
  • Accepted : 2023.07.20
  • Published : 2023.08.25
⟨ Previous Next ⟩

Abstract

This paper proposes a thermography-based coating thickness estimation method for steel structures using model-agnostic meta-learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured using an infrared (IR) camera. The measured heat responses are then analyzed using model-agnostic meta-learning to estimate the coating thickness, which is visualized throughout the inspection surface of the steel structure. Current coating thickness estimation methods rely on point measurement and their inspection area is limited to a single point, whereas the proposed method can inspect a larger area with higher accuracy. In contrast to previous ANN-based methods, which require a large amount of data for training and validation, the proposed method can estimate the coating thickness using only 10- pixel points for each material. In addition, the proposed model has broader applicability than previous methods, allowing it to be applied to various materials after meta-training. The performance of the proposed method was validated using laboratory-scale and field tests with different coating materials; the results demonstrated that the error of the proposed method was less than 5% when estimating coating thicknesses ranging from 40 to 500 ㎛.

Keywords

Acknowledgement

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) [Grant Number 2019R1A3B3067987].

References

  1. A.A.o.S.a.H.T. Officials (Ed.) (2019), Standard Practice for Evaluation of Coating Systems for Structural Steel.
  2. Avci, O., Abdeljaber, O. and Kiranyaz, S. (2022), "Structural damage detection in civil engineering with machine learning: Current state of the artzz", Proceedings of the Society for Experimental Mechanics Series, pp. 223-229. https://doi.org/10.1007/978-3-030-75988-9_17.
  3. Beamish, D. (2004), "Using ultrasonic coating thickness gauges", Mater. Perform., 43, 30-33.
  4. Bu, C., Tang, Q., Liu, Y., Yu, F., Mei, C. and Zhao, Y. (2016), "Quantitative detection of thermal barrier coating thickness based on simulated annealing algorithm using pulsed infrared thermography technology", Appl. Thermal Eng., 99, 751-755. https://doi.org/10.1016/J.APPLTHERMALENG.2016.01.143
  5. Chauchard, F., Cogdill, R., Roussel, S., Roger, J.M. and Bellon-Maurel, V. (2004), "Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes", Chemomet. Intell. Lab. Syst., 71(2), 141-150. https://doi.org/10.1016/j.chemolab.2004.01.003
  6. Cheng, W. (2017), "Thickness measurement of metal plates using swept-frequency eddy current testing and impedance normalization", IEEE Sensors Journal, 17(14), 4558-4569. https://doi.org/10.1109/JSEN.2017.2710356
  7. Darban, S., Tehrani, H.G. and Karballaeezadeh, N. (2020), "Presentation a new method for determining of bridge condition index by using analytical hierarchy process". https://doi.org/10.20944/PREPRINTS202003.0420.V1
  8. Farrar, C.R. and Worden, K. (2012), Structural health monitoring: a machine learning perspective, John Wiley & Sons.
  9. Finn, C., Abbeel, P. and Levine, S. (2017), "Model-agnostic meta-learning for fast adaptation of deep networks", Proceedings of the 34th International Conference on Machine Learning, ICML 2017, 3, 1856-1868.
  10. Flah, M., Nunez, I., Ben Chaabene, W. and Nehdi, M.L. (2021), "Machine learning algorithms in civil structural health monitoring: A systematic review", Arch. Computat. Methods Eng., 28(4), 2621-2643. https://doi.org/10.1007/S11831-020-09471-9
  11. Giurlani, W., Berretti, E., Innocenti, M. and Lavacchi, A. (2019), "Coating thickness determination using X-ray fluorescence spectroscopy: Monte Carlo simulations as an alternative to the use of standards", Coatings, 9(2), 79. https://doi.org/10.3390/COATINGS9020079
  12. Goulet, J.A. (2020), Probabilistic Machine Learning for Civil Engineers, MIT Press.
  13. Hahnloser, R.H., Sarpeshkar, R., Mahowald, M.A., Douglas, R.J. and Seung, H.S. (2000), "Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit", Nature, 405(6789), 947-951. https://doi.org/10.1038/35016072
  14. Hwang, S., Kim, H., Lim, H.J., Liu, P. and Sohn, H. (2022), "Automated visualization of steel structure coating thickness using line laser scanning thermography", Automat. Constr., 139, 104267. https://doi.org/10.1016/J.AUTCON.2022.104267
  15. I.a.T.o.S.K. Ministry of Land (Ed.) (2019), "Detailed Guidelines for Safety and Maintenance of Facilities."
  16. Kingma, D.P. and Ba, J. (2015), "Adam: A method for stochastic optimization", Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, Vol. 1, pp. 448-456.
  17. Standard, N.O.R.S.O.K. (2012), "Surface preparation and protective coating", NORSOK M-501, Norway.
  18. Moskovchenko, A., Vavilov, V., Svantner, M., Muzika, L. and Houdkova, S. (2020), "Active IR thermography evaluation of coating thickness by determining apparent thermal effusivity", Materials, 13(18). 10.3390/MA13184057
  19. Muzika, L. and Svantner, M. (2020), "Flash pulse phase thermography for a paint thickness determination", IOP Conference Series: Mater. Sci. Eng., 723(1), 012021. https://doi.org/10.1088/1757-899X/723/1/012021
  20. Ochiai, S., Iwamoto, S., Nakamura, T. and Okuda, H. (2007), "Crack spacing distribution in coating layer of galvannealed steel under applied tensile strain", ISIJ Int., 47(3), 458-465. https://doi.org/10.2355/isijinternational.47.458
  21. Pan, S.J. and Yang, Q. (2010), "A survey on transfer learning", IEEE Transact. Knowledge Data Eng., 22(10), 13451359. https://doi.org/10.1109/TKDE.2009.191
  22. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L. and Desmaison, A. (2019), "Pytorch: An imperative style, high-performance deep learning library", Adv. Neural Inform. Processing Systems, 32.
  23. Reich, Y. (1997), "Machine learning techniques for civil engineering problems", Comput.-Aided Civil Infrastr. Eng., 12(4), 295-310. https://doi.org/10.1111/0885-9507.00065
  24. Russe, I.S., Brock, D., Knop, K., Kleinebudde, P. and Zeitler, J.A. (2012), "Validation of terahertz coating thickness measurements using X-ray microtomography", Molecul. Pharmaceut., 9(12), 3551-3559. https://doi.org/10.1021/MP300383Y
  25. Shen, H.K., Chen, P.H. and Chang, L.M. (2013), "Automated steel bridge coating rust defect recognition method based on color and texture feature", Automat. Constr., 31, 338-356. https://doi.org/10.1016/J.AUTCON.2012.11.003
  26. Shrestha, R. and Kim, W. (2017), "Evaluation of coating thickness by thermal wave imaging: A comparative study of pulsed and lock-in infrared thermography-Part I: Simulation", Infrared Phys. Technol., 83, 124-131. https://doi.org/10.1016/J.INFRARED.2017.04.016
  27. Wang, H., Hsieh, S.J., Peng, B. and Zhou, X. (2016), "Nonmetallic coating thickness prediction using artificial neural network and support vector machine with time resolved thermography", Infrared Phys. Technol., 77, 316-324. https://doi.org/10.1016/j.infrared.2016.06.015
  28. Zhang, J.Y., Meng, X.B. and Ma, Y.C. (2016), "A new measurement method of coatings thickness based on lock-in thermography", Infrared Phys. Technol., 76, 655-660. https://doi.org/10.1016/j.infrared.2016.04.028
  29. Zhang, J., Cho, Y., Kim, J., Malikov, A.K.U., Kim, Y.H., Yi, J.H. and Li, W. (2021), "Non-destructive evaluation of coating thickness using water immersion ultrasonic testing", Coatings, 11(11), 1421. https://doi.org/10.3390/COATINGS11111421