Application of machine learning in material corrosion research
Source abstract
Abstract
Abstract The application of machine learning (ML) to corrosion research has become an important trend in corrosion science in recent years. In this paper, the feature extraction method for corrosion data and the ML algorithms commonly used (including artificial neural networks, support vector machines, ensemble learning and other widely used algorithms) in corrosion field is introduced. Then, the characteristics of different algorithms and their application scenarios in the corrosion prediction are summarized. Finally, the development trend of ML in material corrosion field is prospected.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Concrete Corrosion and Durability · Corrosion Behavior and Inhibition · Smart Materials for Construction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Corrosion and Protection Center, Institute of Advanced Materials and Technology , University of Science and Technology Beijing , no. 30 Xueyuan Rd , Haidian District , Beijing 100083 , China
References (49)
Show source-provided references
1. Aijazi, A.K., Malaterre, L., Tazir, M.L., Trassoudaine, L., and Checchin, P. (2016). Detecting and analysing corrosion spots on the hull of large marine vessels using colored 3D lidar point clouds. ISPRS Ann. Photogrammetry Remote Sens. Spat. Inf. Sci. 3: 3.10.5194/isprsannals-III-3-153-2016 ↗
2. Alkanhal, T.A. (2014). Image processing techniques applied for pitting corrosion analysis. Entropy. Int. 5: 2.
3. Angst, U., Elsener, B., Larsen, C.K., and Vennesland, Ø. (2009). Critical chloride content in reinforced concrete-a review. Cement. Concrete. Res. 39: 1122–1138. https://doi.org/10.1016/j.cemconres.2009.08.006.10.1016/j.cemconres.2009.08.006 ↗
4. Breiman, L. (2001). Random forests. Mach. Learn. 45: 5–32. https://doi.org/10.1023/a:1010933404324.10.1023/A:1010933404324 ↗
5. Chou, J.S., Ngo, N.T., and Chong, W.K. (2017). The use of artificial intelligence combiners for modeling steel pitting risk and corrosion rate. Eng. Appl. Artificial. 65: 471–483. https://doi.org/10.1016/j.engappai.2016.09.008.10.1016/j.engappai.2016.09.008 ↗
6. Chun, P., Ujike, I., and Mishima, K. (2020). Random forest-based evaluation technique for internal damage in reinforced concrete featuring multiple nondestructive testing results. Constr. Build. Mater. 253: 119238. https://doi.org/10.1016/j.conbuildmat.2020.119238.10.1016/j.conbuildmat.2020.119238 ↗
7. Contreras, G., Fassina, P., Fumagalli, G., Goidanich, S., Lazzari, L., and Mazzola, E. (2007). A study on metastability phenomena of passive films for corrosion resistant alloys. Electrochim. Acta 52: 7577–7584. https://doi.org/10.1016/j.electacta.2006.12.037.10.1016/j.electacta.2006.12.037 ↗
8. Diaz-Uriarte, R. and De Andres, S. (2006). Gene selection and classification of microarray data using random forest. BMC Bioinf. 7: 3. https://doi.org/10.1186/1471-2105-7-3.10.1186/1471-2105-7-3 ↗
9. Forkan, A.R.M., Kang, Y.B., Jayaraman, P.P., Liao, K., Kaul, R., Morgan, G., and Sinha, S. (2022). CorrDetector: a framework for structural corrosion detection from drone images using ensemble deep learning. Expert Syst. Appl. 193: 116461. https://doi.org/10.1016/j.eswa.2021.116461.10.1016/j.eswa.2021.116461 ↗
10. Genuer, R., Poggi, J., and Tuleau-Malot, C. (2010). Variable selection using random forests. Pattern Recogn. Lett. 31: 2225–2236. https://doi.org/10.1016/j.patrec.2010.03.014.10.1016/j.patrec.2010.03.014 ↗
11. Hajibagheri, H.R., Heidari, A., and Amini, R. (2018). An experimental investigation of the nature of longitudinal cracks in oil and gas transmission pipelines. J. Alloy. Compd. 741: 1121–1129. https://doi.org/10.1016/j.jallcom.2017.12.311.10.1016/j.jallcom.2017.12.311 ↗
12. Haykin, S. (2004). Neural networks. A comprehensive foundation. Neural Network. 2: 41.
13. Hou, Y., Aldrich, C., Lepkova, K., Machuca, L.L., and Kinsella, B. (2017). Analysis of electrochemical noise data by use of recurrence quantification analysis and machine learning methods. Electrochim. Acta 256: 337–347. https://doi.org/10.1016/j.electacta.2017.09.169.10.1016/j.electacta.2017.09.169 ↗
14. Jahanshahi, M.R. and Masri, S.F. (2013). Effect of color space, color channels, and sub-image block size on the performance of wavelet-based texture analysis algorithms: an application to corrosion detection on steel structures. J. Comput. Civil Eng. 2013: 685–692, https://doi.org/10.1061/9780784413029.086.10.1061/9780784413029.086 ↗
15. Jia, S. (2012). The Application of a non-linear model for evaluating the external corrosion protection of a gas pipeline. In: ICPTT 2012: better pipeline infrastructure for a better life. ASCE, Wuhan, China, pp. 141–145.10.1061/9780784412619.016 ↗
16. Jiang, G., Sun, J., Sharma, K.R., and Yuan, Z. (2015). Corrosion and odor management in sewer systems. Curr. Opin. Biotechnol. 33: 192–197. https://doi.org/10.1016/j.copbio.2015.03.007.10.1016/j.copbio.2015.03.007 ↗
17. Kamrunnahar, M. and Urquidi-Macdonald, M. (2010). Prediction of corrosion behavior using neural network as a data mining tool. Corros. Sci. 52: 669–677. https://doi.org/10.1016/j.corsci.2009.10.024.10.1016/j.corsci.2009.10.024 ↗
18. Karanci, E. and Betti, R. (2018). Modeling corrosion in suspension bridge main cables. I: annual corrosion rate. J. Bridge Eng. 23: 6. https://doi.org/10.1061/(asce)be.1943-5592.0001233.10.1061/(ASCE)BE.1943-5592.0001233 ↗
19. Krivy, V., Kubzova, M., Kreislova, K., and Urban, V. (2017). Characterization of corrosion products on weathering steel bridges influenced by chloride eposition. Metals 7: 336. https://doi.org/10.3390/met7090336.10.3390/met7090336 ↗
20. Li, X., Zhang, D., Liu, Z., Li, Z., Du, C., and Dong, C. (2015). Materials science: share corrosion data. Nature 527: 441–442. https://doi.org/10.1038/527441a.10.1038/527441a ↗
21. Liu, Y., Song, Y., Keller, J., Bond, P., and Jiang, G. (2017). Prediction of concrete corrosion in sewers with hybrid Gaussian processes regression model. RSC Adv. 7: 30894–30903. https://doi.org/10.1039/c7ra03959j.10.1039/C7RA03959J ↗
22. Luo, Q., Guo, Y., Liu, B., Feng, Y., Zhang, J., Li, Q., and Chou, K. (2020). Thermodynamics and kinetics of phase transformation in rare earth–magnesium alloys: a critical review. J. Mater. Sci. Technol. 44: 171–190. https://doi.org/10.1016/j.jmst.2020.01.022.10.1016/j.jmst.2020.01.022 ↗
23. Ma, F. and Wang, W. (2007). Prediction of pitting corrosion behavior for stainless SUS 630 based on grey system theory. Mater. Lett. 61: 998–1001. https://doi.org/10.1016/j.matlet.2006.06.053.10.1016/j.matlet.2006.06.053 ↗
24. Ma, G., Li, J., and Bai, R. (2019). Prediction of corrosion rate in oil and gas pipelines based on PSO-SVM model. Surf. Coat. Technol. 48: 43–48.
25. De Masi, G., Gentile, M., Vichi, R., Bruschi, R., and Gabetta, G. (2015). Machine learning approach to corrosion assessment in subsea pipelines. In: OCEANS 2015-Genova. IEEE, Genova, Italy, pp. 1–6.10.1109/OCEANS-Genova.2015.7271592 ↗
26. Meng, F., Liu, Y., Liu, L., Li, Y., and Wang, F. (2017). Studies on mathematical models of wet adhesion and lifetime prediction of organic coating/steel by grey system theory. Materials 10: 715–729. https://doi.org/10.3390/ma10070715.10.3390/ma10070715 ↗
27. Morizet, N., Godin, N., Tang, J., Maillet, E., Fregonese, M., and Normand, B. (2016). Classification of acoustic emission signals using wavelets and random forests: application to localized corrosion. Mech. Syst. Signal Process. 70–71: 1026–1037. https://doi.org/10.1016/j.ymssp.2015.09.025.10.1016/j.ymssp.2015.09.025 ↗
28. Nash, W., Drummond, T., and Birbilis, N. (2019). Deep learning AI for corrosion detection. March 2019: Corrosion 2019. OnePetro, Nashville, USA.10.5006/C2019-13267 ↗
29. Possan, E. and Andrade, J. (2014). Markov chains and reliability analysis for reinforced concrete structure service life. Mater. Res. 17: 593–602. https://doi.org/10.1590/s1516-14392014005000074.10.1590/S1516-14392014005000074 ↗
30. Rahman, A., Wu, Z.Y., and Kalfarisi, R. (2021). Semantic deep learning integrated with RGB feature-based rule optimization for facility surface corrosion detection and evaluation. J. Comput. Civil. Eng. 35: 04021018. https://doi.org/10.1061/(asce)cp.1943-5487.0000982.10.1061/(ASCE)CP.1943-5487.0000982 ↗
31. Shi, X., Nguyen, T.A., Kumar, P., and Liu, Y. (2011). A phenomenological model for the chloride threshold of pitting corrosion of steel in simulated concrete pore solutions. Anti-Corros. Methods Mater. 58: 179–189. https://doi.org/10.1108/00035591111148894.10.1108/00035591111148894 ↗
32. Smith, M., Barton, L., Pesinis, K., and Laing, I. (2019). Intelligent corrosion prediction using Bayesian networks. March 2019: Corrosion 2019. One Petro, Nashville, USA.10.5006/C2019-13372 ↗
33. Snoek, J., Larochelle, H., and Adams, R.P. (2012). Practical Bayesian optimization of machine learning algorithms. Adv. Neural Inf. Process. Syst. 25: 1–9.
34. Soares, L., Botelho, S., Nagel, R., and Drews, P.L. (2021). A visual inspection proposal to identify corrosion levels in marine vessels using a deep neural network. In: 2021 Brazilian Symposium on Robotics (SBR), pp. 222–227.10.1109/LARS/SBR/WRE54079.2021.9605400 ↗
35. Urda, D., Luque, R.M., Jiménez, M.J., Turias, I., Franco, L., and Jerez, J.M. (2013). A constructive neural network to predict pitting corrosion status of stainless steel. In: International Work-Conference on Artificial Neural Networks 2013. Berlin, Heidelberg: Springer, pp. 88–95.10.1007/978-3-642-38679-4_7 ↗
36. Valor, A., Caleyo, F., Hallen, J.M., and Velázquez, J.C. (2013). Reliability assessment of buried pipelines based on different corrosion rate models. Corros. Sci. 66: 78–87. https://doi.org/10.1016/j.corsci.2012.09.005.10.1016/j.corsci.2012.09.005 ↗
37. Wen, Y.F., Cai, C.Z., Liu, X.H., Pei, J.F., Zhu, X.J., and Xiao, T.T. (2009). Corrosion rate prediction of 3C steel under different seawater environment by using support vector regression. Corros. Sci. 51: 349–355. https://doi.org/10.1016/j.corsci.2008.10.038.10.1016/j.corsci.2008.10.038 ↗
38. Yan, L., Diao, Y., Lang, Z., and Gao, K. (2020). Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach. Sci. Technol. Adv. Mater. 21: 359–370. https://doi.org/10.1080/14686996.2020.1746196.10.1080/14686996.2020.1746196 ↗
39. Yao, Y., Yang, Y., Wang, Y., and Zhao, X. (2019). Artificial intelligence-based hull structural plate corrosion damage detection and recognition using convolutional neural network. Appl. Ocean Res. 90: 101823. https://doi.org/10.1016/j.apor.2019.05.008.10.1016/j.apor.2019.05.008 ↗
40. Yang, C., Liu, J., Zeng, Y., and Xie, G. (2017). Prediction of components degradation using support vector regression with optimized parameters. Energy Proc. 127: 284–290. https://doi.org/10.1016/j.egypro.2017.08.109.10.1016/j.egypro.2017.08.109 ↗
41. Zhan, Y., Song, Z., and Wang, H. (2015). Prediction of the silica fume concrete corrosion in sulfuric acid by SVM-based method. In: 5th International Conference on Civil Engineering and Transportation. Atlantis Press, Atlantis, pp. 766–770.10.2991/iccet-15.2015.143 ↗
42. Zhang, S., Deng, X., Lu, Y., Hong, S., Kong, Z., Peng, Y., and Luo, Y. (2021). A channel attention based deep neural network for automatic metallic corrosion detection. J. Build. Eng. 42: 103046. https://doi.org/10.1016/j.jobe.2021.103046.10.1016/j.jobe.2021.103046 ↗
43. Zhang, Y. and Yang, J. (2013). Pipeline corrosion rate prediction based on BP neural network. Total Corros. Control 9: 67–71.
44. Zhang, Y., Zhou, X., Shi, H., Zheng, Z., and Li, S. (2015). Corrosion pitting damage detection of rolling bearings using data mining techniques. Int. J. Model. Identif. 24: 235–243. https://doi.org/10.1504/ijmic.2015.072614.10.1504/IJMIC.2015.072614 ↗
45. Zhao, X., Zhou, Y., Zhao, J., Zhan, G., and Yang, P. (2016). Safety prediction of soleplate corrosion state in petroleum storage tank based on grey theory model. Chem. Eng. Trans. 51: 271–276.
46. Zhi, Y., Fu, D., Li, Z., and Qing, X. (2015). Reasoning of atmospheric corrosion level under missing data based on CMAC and Bayesian network. In: 34th Chinese Control Conference. IEEE, Hangzhou, China, pp. 3447–3451.10.1109/ChiCC.2015.7260170 ↗
47. Zhi, Y., Fu, D., Zhang, D., Yang, T., and Li, X. (2019). Prediction and knowledge mining of outdoor atmospheric corrosion rates of low alloy steels based on the random forests approach. Metals 9: 383.https://doi.org/10.3390/met903038310.3390/met9030383 ↗
48. Zhi, Y., Yang, T., and Fu, D. (2020). An improved deep forest model for forecast the outdoor atmospheric corrosion rate of low-alloy steels. J. Mater. Sci. Technol. 49: 202–210. https://doi.org/10.1016/j.jmst.2020.01.044.10.1016/j.jmst.2020.01.044 ↗
49. Zounemat-Kermani, M., Stephan, D., and Barjenbruch, M. (2020). Ensemble data mining modeling in corrosion of concrete sewer: a comparative study of network-based (MLPNN and RBFNN) and tree-based (RF, CHAID, and CART) models. Adv. Eng. Inform. 43: 101030. https://doi.org/10.1016/j.aei.2019.101030.10.1016/j.aei.2019.101030 ↗