Quantitative and Qualitative Analysis of Atmospheric Effects on Carbon Steel Corrosion Using an ANN Model
This study develops an artificial neural network (ANN) model to predict the corrosion rate of carbon steel under a wide range of atmospheric conditions. The model incorporates input variables, including temperature (−3.1–28.2 °C), relative humidity (33.3–91.1%), time of wetness (0.003–0.976), precip...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2075-4701/15/6/607 |
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| author | Pasupuleti L. Narayana Saurabh Tiwari Anoop K. Maurya Muhammad Ishtiaq Nokeun Park Nagireddy Gari Subba Reddy |
| author_facet | Pasupuleti L. Narayana Saurabh Tiwari Anoop K. Maurya Muhammad Ishtiaq Nokeun Park Nagireddy Gari Subba Reddy |
| author_sort | Pasupuleti L. Narayana |
| collection | DOAJ |
| description | This study develops an artificial neural network (ANN) model to predict the corrosion rate of carbon steel under a wide range of atmospheric conditions. The model incorporates input variables, including temperature (−3.1–28.2 °C), relative humidity (33.3–91.1%), time of wetness (0.003–0.976), precipitation (13–4656 mm), sulfur dioxide (0–68.2 mg/m<sup>2</sup>·d), and chloride concentrations (0 to 359.8 mg/m<sup>2</sup>·d). The model demonstrated excellent predictive capability and reliability, with R<sup>2</sup> values of 97.2% and 77.6% for the training and testing datasets, respectively. The model demonstrated a strong predictive performance, with an R<sup>2</sup> of 97.2% for the training set and 77.6% for the test set. It achieved a mean absolute error (MAE) of 5.633 μm/year for training and 18.86 μm/year for testing, along with a root mean square error (RMSE) of 0.000055, indicating reliable generalization despite the limited dataset size. The analysis showed that the relative humidity had the most significant impact on the corrosion rate. The practical applications of the model extend to optimizing material selection and devising effective maintenance strategies. |
| format | Article |
| id | doaj-art-80a05faa3fb74a009c899f8a74e2b65f |
| institution | OA Journals |
| issn | 2075-4701 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Metals |
| spelling | doaj-art-80a05faa3fb74a009c899f8a74e2b65f2025-08-20T02:21:04ZengMDPI AGMetals2075-47012025-05-0115660710.3390/met15060607Quantitative and Qualitative Analysis of Atmospheric Effects on Carbon Steel Corrosion Using an ANN ModelPasupuleti L. Narayana0Saurabh Tiwari1Anoop K. Maurya2Muhammad Ishtiaq3Nokeun Park4Nagireddy Gari Subba Reddy5Titanium Department, Advanced Metal Division, Korea Institute of Materials Science, Changwon 51508, Republic of KoreaSchool of Materials Science and Engineering, Yeungnam University, Gyeongsan 38541, Republic of KoreaTitanium Department, Advanced Metal Division, Korea Institute of Materials Science, Changwon 51508, Republic of KoreaVirtual Materials Laboratory, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju 52828, Republic of KoreaSchool of Materials Science and Engineering, Yeungnam University, Gyeongsan 38541, Republic of KoreaVirtual Materials Laboratory, School of Materials Science and Engineering, Engineering Research Institute, Gyeongsang National University, Jinju 52828, Republic of KoreaThis study develops an artificial neural network (ANN) model to predict the corrosion rate of carbon steel under a wide range of atmospheric conditions. The model incorporates input variables, including temperature (−3.1–28.2 °C), relative humidity (33.3–91.1%), time of wetness (0.003–0.976), precipitation (13–4656 mm), sulfur dioxide (0–68.2 mg/m<sup>2</sup>·d), and chloride concentrations (0 to 359.8 mg/m<sup>2</sup>·d). The model demonstrated excellent predictive capability and reliability, with R<sup>2</sup> values of 97.2% and 77.6% for the training and testing datasets, respectively. The model demonstrated a strong predictive performance, with an R<sup>2</sup> of 97.2% for the training set and 77.6% for the test set. It achieved a mean absolute error (MAE) of 5.633 μm/year for training and 18.86 μm/year for testing, along with a root mean square error (RMSE) of 0.000055, indicating reliable generalization despite the limited dataset size. The analysis showed that the relative humidity had the most significant impact on the corrosion rate. The practical applications of the model extend to optimizing material selection and devising effective maintenance strategies.https://www.mdpi.com/2075-4701/15/6/607atmospheric conditionscorrosion ratecarbon steelartificial neural networkquantitative estimation |
| spellingShingle | Pasupuleti L. Narayana Saurabh Tiwari Anoop K. Maurya Muhammad Ishtiaq Nokeun Park Nagireddy Gari Subba Reddy Quantitative and Qualitative Analysis of Atmospheric Effects on Carbon Steel Corrosion Using an ANN Model Metals atmospheric conditions corrosion rate carbon steel artificial neural network quantitative estimation |
| title | Quantitative and Qualitative Analysis of Atmospheric Effects on Carbon Steel Corrosion Using an ANN Model |
| title_full | Quantitative and Qualitative Analysis of Atmospheric Effects on Carbon Steel Corrosion Using an ANN Model |
| title_fullStr | Quantitative and Qualitative Analysis of Atmospheric Effects on Carbon Steel Corrosion Using an ANN Model |
| title_full_unstemmed | Quantitative and Qualitative Analysis of Atmospheric Effects on Carbon Steel Corrosion Using an ANN Model |
| title_short | Quantitative and Qualitative Analysis of Atmospheric Effects on Carbon Steel Corrosion Using an ANN Model |
| title_sort | quantitative and qualitative analysis of atmospheric effects on carbon steel corrosion using an ann model |
| topic | atmospheric conditions corrosion rate carbon steel artificial neural network quantitative estimation |
| url | https://www.mdpi.com/2075-4701/15/6/607 |
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