Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach

References

• Morcillo M, Chico B, Diaz I, et al. Atmospheric corrosion data of weathering steels. A review. Corros Sci. 2013;77:6–24.
   Web of Science ®Google Scholar
• Nishimura T. Corrosion resistance of Si–Al-bearing ultrafine-grained weathering steel. Sci Technol Adv Mat. 2008;9(1):013005.
   PubMed Web of Science ®Google Scholar
• Natesan M, Selvaraj S, Manickam T, et al. Corrosion behavior of metals and alloys in marine-industrial environment. Sci Technol Adv Mat. 2008;9(4):045002.
   PubMed Web of Science ®Google Scholar
• Morcillo M, Diaz I, Chico B, et al. Weathering steels: from empirical development to scientific design. A review. Corros Sci. 2014;83:6–31.
   Web of Science ®Google Scholar
• Diaz I, Cano H, de la Fuente D, et al. Atmospheric corrosion of Ni-advanced weathering steels in marine atmospheres of moderate salinity. Corros Sci. 2013;76:348–360.
   Web of Science ®Google Scholar
• Wang ZF, Liu JR, Wu LX, et al. Study of the corrosion behavior of weathering steels in atmospheric environments. Corros Sci. 2013;67:1–10.
   Web of Science ®Google Scholar
• Soares CG, Garbatov Y, Zayed A, et al. Influence of environmental factors on corrosion of ship structures in marine atmosphere. Corros Sci. 2009;51(9):2014–2026.
   Web of Science ®Google Scholar
• Diaz I, Cano H, Crespo D, et al. Atmospheric corrosion of ASTM A-242 and ASTM A-588 weathering steels in different types of atmosphere. Corros Eng Sci Techn. 2018;53(6):449–459.
   Web of Science ®Google Scholar
• Dong J, Han E, Ke W. Introduction to atmospheric corrosion research in China. Sci Technol Adv Mat. 2007;8(7–8):559–565.
   Web of Science ®Google Scholar
• Panchenko YM, Marshakov AI. Long-term prediction of metal corrosion losses in atmosphere using a power-linear function. Corros Sci. 2016;109:217–229.
   Web of Science ®Google Scholar
• Panchenko YM, Marshakov AI, Igonin TN, et al. Long-term forecast of corrosion mass losses of technically important metals in various world regions using a power function. Corros Sci. 2014;88:306–316.
   Web of Science ®Google Scholar
• Cai Y, Zhao Y, Ma X, et al. Influence of environmental factors on atmospheric corrosion in dynamic environment. Corros Sci. 2018;137:163–175.
   Web of Science ®Google Scholar
• Hoerle S, Mazaudier F, Dillmann P, et al. Advances in understanding atmospheric corrosion of iron. II. Mechanistic modelling of wet-dry cycles. Corros Sci. 2004;46(6):1431–1465.
   Web of Science ®Google Scholar
• Lien LTH, San PT, Hong HL. Results of studying atmospheric corrosion in Vietnam 1995–2005. Sci Technol Adv Mat. 2007;8(7–8):552–558.
   Web of Science ®Google Scholar
• Alcantara J, de la Fuente D, Chico B, et al. Marine atmospheric corrosion of carbon steel: a review. Materials. 2017;10(4):406.
   Web of Science ®Google Scholar
• Cai YK, Zhao Y, Ma XB, et al. Application of hierarchical linear modelling to corrosion prediction in different atmospheric environments. Corros Eng Sci Techn. 2019;54(3):266–275.
   Web of Science ®Google Scholar
• Chico B, de la Fuente D, Diaz I, et al. Annual atmospheric corrosion of carbon steel worldwide. An integration of ISOCORRAG, ICP/UNECE and MICAT databases. Materials. 2017;10:601.
   Web of Science ®Google Scholar
• Pruksawan S, Lambard G, Samitsu S, et al. Prediction and optimization of epoxy adhesive strength from a small dataset through active learning. Sci Technol Adv Mat. 2019;20(1):1010–1021.
   PubMed Web of Science ®Google Scholar
• Shin D, Lee S, Shyam A, et al. Petascale supercomputing to accelerate the design of high-temperature alloys. Sci Technol Adv Mat. 2017;18(1):828–838.
   PubMed Web of Science ®Google Scholar
• Xue DZ, Xue DQ, Yuan RH, et al. An informatics approach to transformation temperatures of NiTi-based shape memory alloys. Acta Mater. 2017;125:532–541.
   Web of Science ®Google Scholar
• Lee S, Peng J, Shin D, et al. Data analytics approach for melt-pool geometries in metal additive manufacturing. Sci Technol Adv Mat. 2019;20(1):972–978.
   PubMed Web of Science ®Google Scholar
• Shi YN, Fu DM, Zhou XY, et al. Data mining to online galvanic current of zinc/copper internet atmospheric corrosion monitor. Corros Sci. 2018;133:443–450.
   Web of Science ®Google Scholar
• Kamrunnahar M, Urquidi-Macdonald M. Prediction of corrosion behaviour of alloy 22 using neural network as a data mining tool. Corros Sci. 2011;53(3):961–967.
   Web of Science ®Google Scholar
• Pintos S, Queipo NV, de Rincon OT, et al. Artificial neural network modeling of atmospheric corrosion in the MICAT project. Corros Sci. 2000;42(1):35–52.
   Web of Science ®Google Scholar
• Wen YF, Cai CZ, Liu XH, et al. Corrosion rate prediction of 3C steel under different seawater environment by using support vector regression. Corros Sci. 2009;51(2):349–355.
   Web of Science ®Google Scholar
• Shi JB, Wang JH, Macdonald DD. Prediction of primary water stress corrosion crack growth rates in alloy 600 using artificial neural networks. Corros Sci. 2015;92:217–227.
   Web of Science ®Google Scholar
• Ramprasad R, Batra R, Pilania G, et al. Machine learning in materials informatics: recent applications and prospects. Npj Comput Mater. 2017;3(1):54.
   Google Scholar
• MatNavi. [cited 2020 Mar 18]. Available from: https://smds.nims.go.jp/corrosion/index_en.html
   Google Scholar
• Shin D, Yamamoto Y, Brady MP, et al. Modern data analytics approach to predict creep of high-temperature alloys. Acta Mater. 2019;168:321–330.
   Web of Science ®Google Scholar
• Pilania G, Mannodi-Kanakkithodi A, Uberuaga BP, et al. Machine learning bandgaps of double perovskites. Sci Rep. 2016;6(1):19375.
   PubMedGoogle Scholar
• Reshef DN, Reshef YA, Finucane HK, et al. Detecting novel associations in large data sets. Science. 2011;334(6062):1518–1524.
   PubMed Web of Science ®Google Scholar
• Kinney JB, Atwal GS. Equitability, mutual information, and the maximal information coefficient. Proc Natl Acad Sci U S A. 2014;111(9):3354–3359.
   PubMed Web of Science ®Google Scholar
• Ahedo V, Martin O, Santos JI, et al. Independence of EPR and PAP tests performed on resistance spot welding joints. Corros Eng Sci Techn. 2017;52(6):418–424.
   Web of Science ®Google Scholar
• Lundberg SM, Erion GG, Lee SI Consistent individualized feature attribution for tree ensembles. arXiv, 1802.03888.
   Google Scholar
• Stojic A, Stanic N, Vukovic G, et al. Explainable extreme gradient boosting tree-based prediction of toluene, ethylbenzene and xylene wet deposition. Sci Total Environ. 2019;653:140–147.
   PubMed Web of Science ®Google Scholar
• SHAP. [cited 2020 Mar 18]. Available from: https://github.com/slundberg/shap
   Google Scholar
• Agrawal A, Deshpande PD, Cecen A, et al. Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters. Integr Mater Manuf Innovations. 2014;3(1):8.
   Google Scholar
• Sun YT, Bai HY, Li MZ, et al. Machine learning approach for prediction and understanding of glass-forming ability. J Phys Chem Lett. 2017;8(14):3434–3439.
   PubMed Web of Science ®Google Scholar
• Ye ZS, Li JG, Zhang MR. Application of ridge regression and factor analysis in design and production of alloy wheels. J Appl Stat. 2014;41(7):1436–1452.
   Web of Science ®Google Scholar
• Morcillo M, Chico B, Mariaca L, et al. Salinity in marine atmospheric corrosion: its dependence on the wind regime existing in the site. Corros Sci. 2000;42(1):91–104.
   Web of Science ®Google Scholar
• Li D, Fu G, Zhu M, et al. Effect of Ni on the corrosion resistance of bridge steel in a simulated hot and humid coastal-industrial atmosphere. Int J Miner Metall Mater. 2018;25(3):325–338.
   Google Scholar
• Takahashi A, Seko A, Tanaka I. Conceptual and practical bases for the high accuracy of machine learning interatomic potentials: application to elemental titanium. Phy Rev Mater. 2017;1(6):063801.
   Web of Science ®Google Scholar
• Menze BH, Kelm BM, Masuch R, et al. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. Bmc Bioinformatics. 2009;10(1):213.
   PubMedGoogle Scholar
• Fan Y, Liu W, Li S, et al. Evolution of rust layers on carbon steel and weathering steel in high humidity and heat marine atmospheric corrosion. J Mater Sci Technol. 2020;39:190–199.
   Web of Science ®Google Scholar
• Tamura H. The role of rusts in corrosion and corrosion protection of iron and steel. Corros Sci. 2008;50(7):1872–1883.
   Web of Science ®Google Scholar
• Wu W, Cheng XQ, Hou HX, et al. Insight into the product film formed on Ni-advanced weathering steel in a tropical marine atmosphere. Appl Surf Sci. 2018;436:80–89.
   Web of Science ®Google Scholar
• Ma YT, Li Y, Wang FH. Weatherability of 09CuPCrNi steel in a tropical marine environment. Corros Sci. 2009;51(8):1725–1732.
   Web of Science ®Google Scholar
• Butler KT, Davies DW, Cartwright H, et al. Machine learning for molecular and materials science. Nature. 2018;559(7715):547–555.
   PubMed Web of Science ®Google Scholar
• Knotkova D, Kreislova K, Dean SW ISOCORRAG. International atmospheric exposure program: summary of results. West Conshohocken (PA): ASTM International (US); 2010.
   Google Scholar