A Machine Learning-Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel
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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6967550 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832558894127251456 |
|---|---|
| author | Ngoc-Long Tran Trong-Ha Nguyen Van-Tien Phan Duy-Duan Nguyen |
| author_facet | Ngoc-Long Tran Trong-Ha Nguyen Van-Tien Phan Duy-Duan Nguyen |
| author_sort | Ngoc-Long Tran |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c9fc5ef98e3f46a08fc14df68e7a072c |
| institution | Kabale University |
| issn | 1687-8442 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-c9fc5ef98e3f46a08fc14df68e7a072c2025-02-03T01:31:26ZengWileyAdvances in Materials Science and Engineering1687-84422021-01-01202110.1155/2021/6967550A Machine Learning-Based Model for Predicting Atmospheric Corrosion Rate of Carbon SteelNgoc-Long Tran0Trong-Ha Nguyen1Van-Tien Phan2Duy-Duan Nguyen3Department of Civil EngineeringDepartment of Civil EngineeringDepartment of Civil EngineeringDepartment of Civil EngineeringThe 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.http://dx.doi.org/10.1155/2021/6967550 |
| spellingShingle | Ngoc-Long Tran Trong-Ha Nguyen Van-Tien Phan Duy-Duan Nguyen A Machine Learning-Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel Advances in Materials Science and Engineering |
| title | A Machine Learning-Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel |
| title_full | A Machine Learning-Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel |
| title_fullStr | A Machine Learning-Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel |
| title_full_unstemmed | A Machine Learning-Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel |
| title_short | A Machine Learning-Based Model for Predicting Atmospheric Corrosion Rate of Carbon Steel |
| title_sort | machine learning based model for predicting atmospheric corrosion rate of carbon steel |
| url | http://dx.doi.org/10.1155/2021/6967550 |
| work_keys_str_mv | AT ngoclongtran amachinelearningbasedmodelforpredictingatmosphericcorrosionrateofcarbonsteel AT tronghanguyen amachinelearningbasedmodelforpredictingatmosphericcorrosionrateofcarbonsteel AT vantienphan amachinelearningbasedmodelforpredictingatmosphericcorrosionrateofcarbonsteel AT duyduannguyen amachinelearningbasedmodelforpredictingatmosphericcorrosionrateofcarbonsteel AT ngoclongtran machinelearningbasedmodelforpredictingatmosphericcorrosionrateofcarbonsteel AT tronghanguyen machinelearningbasedmodelforpredictingatmosphericcorrosionrateofcarbonsteel AT vantienphan machinelearningbasedmodelforpredictingatmosphericcorrosionrateofcarbonsteel AT duyduannguyen machinelearningbasedmodelforpredictingatmosphericcorrosionrateofcarbonsteel |