A Machine Learning‐Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel

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.

Interpretable machine learning for maximum corrosion depth and influence factor analysis

“…The candidate for the number of estimator is set as: [10,20,50,100,150,200,250,300]. The candidates for the loss function, the max_depth, and the learning rate are set as ['linear', 'square', 'exponential'], [3,5,7,9,12,15,18,21,25], and [0.05, 0.1, 0.2, 0.4, 0.6, 0.8, 1], accordingly. The total search space size is 8×3×9×7.…”

“…In recent years, many scholars around the world have been actively pursuing corrosion prediction models, which involve atmospheric corrosion, marine corrosion, microbial corrosion, etc. [9][10][11][12][13] . To predict the corrosion development of pipelines accurately, scientists are committed to constructing corrosion models from multidisciplinary knowledge.…”

Đánh giá độ tin cậy của cột thép tiết diện thay đổi sử dụng kết hợp thuật toán nơ-ron nhân tạo (ANN) và mô phỏng Monte Carlo (MCS)

“…Ý nghĩa của ANN nằm ở khả năng mô phỏng và mô hình hóa khả năng học và xử lý thông tin của con người, mở ra nhiều cơ hội và ứng dụng trong nhiều lĩnh vực khác nhau. Trong lĩnh vực kết cấu công trình, ANN được sử dụng để mô phỏng và dự đoán hiệu suất, độ tin cậy, và ứng xử của các kết cấu, đồng thời giúp cải thiện quy trình thiết kế và tối ưu hóa kết cấu [7][8][9][10][11].…”

Corrosion Behaviour Modelling Using Artificial Neural Networks: A Case Study in Biogas Environment

“…The model was used to determine the most significant factors in corrosion behaviour for the different environments. Tran et al [28] proposed an ANN model to model atmospheric corrosion in tropical climate conditions. Different meteorological and chemical factors were considered as inputs for modelling, whereas the output provided by the model was related to the atmospheric corrosion rate of carbon steel.…”