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| Predicting Method of Atmospheric Corrosion Rate Based on Manifold Dimension Reduction and Gradient Boosting Decision Trees |
| Received:March 26, 2018 Revised:June 25, 2018 |
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| DOI：10.7643/ issn.1672-9242.2018.06.008 |
| KeyWord:chemical components of steels corrosion rate manifold methods GBDT |
| Author | Institution |
| LIANG Xi-wang |
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing , China |
| FU Dong-mei |
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing , China |
| YANG Tao |
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing , China |
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| Abstract: |
| Objective To analyze quantitatively the relationship between atmospheric corrosion rate and the two factors including outdoor exposure time and chemical components of steel materials, an atmospheric corrosion rate predicting model was proposed in combination with locality preserving projection (LPP) and gradient boosting decision trees (GBDT). Methods First, LPP was applied to have dimension reduction process on chemical components of steels to get low-dimensional features. Then GBDT were used to build a predicting model. Corrosion rate data of marine atmospheric environment within 16 years in Qingdao were used to validate the proposed model. Results Testing MAE and MAPE of LPP-GBDT model were 1.73 μm/a and 6.30% respectively. Testing MAE and MAPE of LPP-GBDT with orthogonalization were 1.21 μm/a and 4.42% respectively. Conclusion Compared with other common predicting methods, the proposed model has preferable prediction accuracy and offers some reference to steels selection in specific environment. |
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