Corrosion rate prediction of 3C steel under different seawater environment by using support vector regression

By Y. Wen, C. Cai, X. H. Liu, J. Pei, X. J. Zhu, T. T. Xiao. 2009

175 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Materials Chemistry, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/f5bb82dc97952d98295f9a7a43c2de3cf716785b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Xiaogang Li
2. Herman Terryn

In Materials Chemistry

1. Dawei Zhang
2. Xuequn Cheng
3. Xiaogang Li
4. Herman Terryn

In Mechanical Engineering

1. Xiaogang Li

Citations in the corpus

• Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning (2020, methodology): “The accuracy of the SVR prediction is substantially higher than that of the ANN prediction, which is consistent with previous studies [26,28].”