Abstract
The purpose of this study is to develop a practical artificial neural network (ANN) model for predicting the atmospheric corrosion rate of carbon steel. A set of 240 data samples, which are collected from the experimental results of atmospheric corrosion in tropical climate conditions, are utilized to develop the ANN model. Accordingly, seven meteorological and chemical factors of corrosion, namely, the average temperature, the average relative humidity, the total rainfall, the time of wetness, the hours of sunshine, the average chloride ion concentration, and the average sulfur dioxide deposition rate, are used as input variables for the ANN model. Meanwhile, the atmospheric corrosion rate of carbon steel is considered as the output variable. An optimal ANN model with a high coefficient of determination of 0.999 and a small root mean square error of 0.281 mg/m2.month is retained to predict the corrosion rate. Moreover, the sensitivity analysis shows that the rainfall and hours of sunshine are the most influential parameters on predicting the atmospheric corrosion rate, whereas the average chloride ion concentration, the average temperature, and the time of wetness are less sensitive to the atmospheric corrosion rate. An ANN‐based formula, which accommodates all input parameters, is thereafter proposed to estimate the atmospheric corrosion rate of carbon steel. Finally, a graphical user interface is developed for calculating the atmospheric corrosion rate of carbon steel in tropical climate conditions.
Highlights
Atmospheric corrosion is considered as an electrochemical nonlinear and complex phenomenon, which is mostly depending on external factors and material properties
artificial neural network (ANN) Model Performance. e performance of the proposed ANN model is shown in Figure 5, in which mean square error (MSE) for training, validating, and testing decrease with an increment of the epoch. e best validation performance was selected since MSE was reduced to 1.7814 × 10− 3 at the 4th epoch
A small value of the squared error indicates that the ANN model was well trained
Summary
Atmospheric corrosion is considered as an electrochemical nonlinear and complex phenomenon, which is mostly depending on external factors and material properties. Empirical equations to calculate the atmospheric corrosion rate were proposed by some studies [19, 21, 25] These equations only considered few input parameters, which are sulfur dioxide deposition rate, chloride, and time of wetness. ANN was used for predicting the penetration of corrosion or the corrosion rate of carbon steel considering input parameters such as humidity, temperature, time of wetness, precipitation, sulfur dioxide concentration (SO2), and chloride deposition rate (Cl− ) [30, 35]. Seven external factors, which are the average temperature (T), average relative humidity (RH), total rainfall (Rf ), time of wetness (TOW), hours of sunshine (HoS), Cl− , and SO2 deposition rate, are considered as input variables of the ANN model. An ANN-based equation and a graphical user interface (GUI) tool are established to predict the atmospheric corrosion rate of carbon steel
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