Skip to main content

Advertisement

Springer Nature Link
Log in

A hybrid intelligent model combining ANN and imperialist competitive algorithm for prediction of corrosion rate in 3C steel under seawater environment

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

There are species of carbon steel in the industry suffering from corrosion phenomena under seawater environment. In this paper, for purposes of the prediction of 3C steel corrosion rate, the proposed methodology here adopts a hybrid model based on neural network (NN) and imperialist competitive algorithm (ICA). Additionally, to validate the suggested method, we have managed to apply a procedure, namely leaving-one-out cross-validation (LOOCV). Thus, the model in this paper is abbreviated as NN–ICA_LOOCV. In the case study, the model is implemented on 46 experimental samples. This dataset is included within five parameters, namely temperature, dissolved oxygen, salinity, PH value and oxidation–reduction potential as inputs and corrosion rate(s) as an output parameter. The dataset was divided into two parts: one for training and the other for testing with 42 and 4 data number, respectively. For an evaluation purpose, the performance of NN–ICA_LOOCV is compared with other models on the basis of indicators such as the coefficient of determination (R 2), root-mean-square error (RMSE) and mean absolute error (MAE). The model was successfully tested, yielding a prediction of corrosion rate with a RMSE of around 0.01, MAE of 0.011 and a correlation factor of 0.99 to the test data. The results demonstrate that the carefully designed hybrid model further succeeded to denote lower modeling error and higher accuracy. Hence, this model is an applicable and reliable offer to engineers in order to online and safe prediction of corrosion rate in 3C steel under seawater environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Wen Y, Cai C, Liu X, Pei J, Zhu X, Xiao T (2009) Corrosion rate prediction of 3C steel under different seawater environment by using support vector regression. Corros Sci 51(2):349–355

    Article  Google Scholar 

  2. Fontana MG (2005) Corrosion engineering. Tata McGraw-Hill Education, New Delhi, Noida

    Google Scholar 

  3. Kim Y, Jeong G, Sohn H (1999) Mathematical modeling on the corrosion of unprotected structure due to stray current resulting from cathodic protection system. Met Mater 5(1):93–99

    Article  Google Scholar 

  4. Jones DA (1992) Principles and prevention of corrosion. Macmillan, New York

    Google Scholar 

  5. Mazario E, Venegas R, Herrasti P, Alonso M, Recio F (2015) Pitting corrosion and stress corrosion cracking study in high strength steels in alkaline media. J Solid State Electrochem 19:1–5. doi:10.1007/s10008-015-2956-y

    Article  Google Scholar 

  6. Chiu C-K, Lin Y-F (2014) Multi-objective decision-making supporting system of maintenance strategies for deteriorating reinforced concrete buildings. Autom Constr 39:15–31

    Article  Google Scholar 

  7. Zhang Z, Tian N, Zhang L, Wu L (2015) Inhibition of the corrosion of carbon steel in HCl solution by methionine and its derivatives. Corros Sci 98:438–449

    Article  Google Scholar 

  8. Paik JK, Thayamballi AK, Park YI, Hwang JS (2004) A time-dependent corrosion wastage model for seawater ballast tank structures of ships. Corros Sci 46(2):471–486

    Article  Google Scholar 

  9. Melchers R (2006) Modelling immersion corrosion of structural steels in natural fresh and brackish waters. Corros Sci 48(12):4174–4201

    Article  Google Scholar 

  10. Jingjun L, Yuzhen L, Xiaoyu L (2008) Numerical simulation for carbon steel flow-induced corrosion in high-velocity flow seawater. Anti-Corros Method Mater 55(2):66–72

    Article  Google Scholar 

  11. Y-x Liu, X-c Gao, G-y Zhang, H-h Guo (2008) BP neural networks used in prediction and analyses of 3C steel corrosion function. Mater Sci Eng Hangzhou 26(1):94

    Google Scholar 

  12. Liu X, Tang X, Wang J (2009) Correlation between seawater environmental factors and marine corrosion rate using artificial neural network analysis. J Chin Soc Corros Prot 25(1):11–14

    Google Scholar 

  13. Kong D-Y, Song S-Z (1998) Analysis of corrosion data for carbon steel and low-alloy steels in seawater by artificial neural network. J Chin Soc Corro Prot 18:289–296

    Google Scholar 

  14. Paul S (2011) Model to study the effect of composition of seawater on the corrosion rate of mild steel and stainless steel. J Mater Eng Perform 20(3):325–334

    Article  Google Scholar 

  15. Kharchenko U, Beleneva I, Kovalchuk YL, Karpov V (2011) Estimation of aggressiveness of seawater corrosion using indicators of microbiological activity of fouling communities formed on metals. Prot Met Phys Chem Surf 47(7):907–910

    Article  Google Scholar 

  16. Jaśniok T, Jaśniok M (2015) Influence of rapid changes of moisture content in concrete and temperature on corrosion rate of reinforcing steel. Procedia Eng 108:316–323

    Article  Google Scholar 

  17. Ling W, Dong-Mei F (2009) A novel approach using SVR ensembles for minor prototypes prediction of seawater corrosion rate. In: Computer science and engineering, 2009. WCSE’09. Second international workshop on, 2009. IEEE, pp 39–43

  18. You W, Liu Y (2008) Predicting the corrosion rates of steels in sea water using artificial neural network. In: Natural computation, 2008. ICNC’08. Fourth international conference on, 2008. IEEE, pp 101–105

  19. Kamrunnahar M, Urquidi-Macdonald M (2010) Prediction of corrosion behavior using neural network as a data mining tool. Corros Sci 52(3):669–677

    Article  Google Scholar 

  20. Hodhod O, Ahmed H (2014) Modeling the corrosion initiation time of slag concrete using the artificial neural network. HBRC J 10(3):231–234

    Article  Google Scholar 

  21. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: Evolutionary computation, 2007. CEC 2007. IEEE Congress on, 2007. IEEE, pp 4661–4667

  22. Ahmadi MA, Ebadi M, Shokrollahi A, Majidi SMJ (2013) Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl Soft Comput 13(2):1085–1098

    Article  Google Scholar 

  23. Rodger JA (2014) A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings. Expert Syst Appl 41(4):1813–1829

    Article  Google Scholar 

  24. Ahmadi MA, Shadizadeh SR, Goudarzi A (2012) Combining artificial neural network and unified particle swarm optimization for oil flow rate prediction: case study. Neural Comput Appl 23:1–8. doi:10.1007/s00521-012-0955-9

    Google Scholar 

  25. Ahmadi MA, Shadizadeh SR, Ebadi M, Khalighi Sheshdeh R (2012) Prediction of condensate-to-gas ratio by using stochastic particle swarm optimization and neural network. Neural Comput Appl 23:1–9. doi:10.1007/s00521-012-0986-2

    Google Scholar 

  26. Ong BT, Sugiura K, Zettsu K (2015) Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM2. 5. Neural Comput Appl 26:1–14. doi:10.1007/s00521-015-1955-3

    Article  Google Scholar 

  27. Sadowski L, Nikoo M (2014) Corrosion current density prediction in reinforced concrete by imperialist competitive algorithm. Neural Comput Appl 25(7–8):1627–1638

    Article  Google Scholar 

  28. Mohebi J, Zadeh Shirazi A, Tabatabaeec H (2015) Adaptive-neuro fuzzy inference system (Anfis) model for prediction of blast-induced ground vibration. Sci Int 27(3):2079–2091

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information and Communication Technology (ICT), Khorasan Razavi Gas Company, National Iranian Gas Company (NIGC), Mashhad, Iran

    Amin Zadeh Shirazi

  2. Department of Computer Engineering, Imam Reza International University, Mashhad, Iran

    Zahra Mohammadi

Authors
  1. Amin Zadeh Shirazi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Zahra Mohammadi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Zahra Mohammadi.

Appendix

Appendix

See Table 4.

Table 4 Dataset of 3C steel corrosion rates under different seawater environment factors
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zadeh Shirazi, A., Mohammadi, Z. A hybrid intelligent model combining ANN and imperialist competitive algorithm for prediction of corrosion rate in 3C steel under seawater environment. Neural Comput & Applic 28, 3455–3464 (2017). https://doi.org/10.1007/s00521-016-2251-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2251-6

Keywords