The mathematical relationship between corrosion degree and time is referred to as a corrosion model. Existing corrosion models can only be used to predict the corrosion wastage of a certain material based on its available historical corrosion data, but the corrosion wastage of newer steel grades cannot be obtained if the data are not available. To solve this problem, two advanced algorithms, i.e., generalized regression neural network (GRNN) and optimizing grey model (OGM (1, N)), are introduced, based on which corrosion models can be obtained for steel classes even in the absence of historical corrosion data, as long as the chemical compositions of the material are known. First, the theoretical basis and operational procedures of GRNN and OGM (1, N) are introduced. Grey relational analysis of corrosion wastage influencing factors is subsequently conducted. Last, the time-dependent atmospheric corrosion wastages of Q345 and Q460 steels, two typical structural steel grades but their corrosion models have not been well established, are predicted based on their chemical compositions by these two advanced algorithms. The results show that the main chemical compositions that influence the atmospheric corrosion wastage of steels are C and S. Both GRNN and OGM (1, N) can accurately predict the corrosion wastage of the steels, and the predicted results can be fitted by quadratic function or power function, where the goodness of fit is greater than 0.95, which indicates a high fitting accuracy.
Skip Nav Destination
Article navigation
1 October 2023
Research Article|
July 18 2023
Advanced Algorithms to Predict Time-Dependent Atmospheric Corrosion Wastage of Low-Alloy and High-Strength Steels Based on Chemical Compositions
Yuelin Zhang;
Yuelin Zhang
‡
*College of Civil Engineering, Tongji University, Shanghai, 200092, China.
‡Corresponding author. E-mail: [email protected].
Search for other works by this author on:
Ruyan Zheng
Ruyan Zheng
**First Military Representative Office of Ministry of the Navy Equipment in Shanghai district, Shanghai, 201913, China.
Search for other works by this author on:
CORROSION (2023) 79 (10): 1122–1134.
Citation
Yuelin Zhang, Ruyan Zheng; Advanced Algorithms to Predict Time-Dependent Atmospheric Corrosion Wastage of Low-Alloy and High-Strength Steels Based on Chemical Compositions. CORROSION 1 October 2023; 79 (10): 1122–1134. doi: https://doi.org/10.5006/4363
Download citation file:
Sign in
Don't already have an account? Register
Client Account
You could not be signed in. Please check your email address / username and password and try again.
Could not validate captcha. Please try again.
Sign in via your Institution
Sign in via your InstitutionCiting articles via
Numerical Simulation of Corrosive Flow in the Production End of CCUS-EOR Backflow Wastewater Pipeline
Fangwen Hu, Lili Wang, Haipeng Fu, Shibo Fu, Huajie Wang, Jian Shi, Dong Li
Synthesis, Characterization and Theoretical Calculations of a Novel Organic Corrosion Inhibitor for Mild Steel
Ke Su, Yingchun He, Yang Haijun
Corrosion mechanisms of plasma sprayed porous coatings of Ni80Cr20 and alloy C-276 investigated by numerical and experimental method
Yu Gu, Jiajing Pan, Haitao Lu, Xiaofeng Hu, Miao Zhang, Mingli Lv
Applicability of yeast extract in Postgate culture medium for microbiologically influenced corrosion (MIC) tests
Jakob Lykke Stein, Tanmay Chaturvedi, Torben Lund Skovhus, Mette Hedegaard Thomsen
Quantification of the Adsorption Kinetics of a Model Corrosion Inhibitor on Gold using QCM-D
Kushal Singla, Hubert Perrot, Bruce Brown, Srdjan Nešić