Markov chain modelling of pitting corrosion in underground pipelines

By F. Caleyo, J. C. Velazquez, A. Valor, J. Hallen. 2009

171 citations recorded by the source corpus.

Subfields: Materials Chemistry, Mechanical Engineering, Metals and Alloys

View Paper: https://www.semanticscholar.org/paper/a01738b1d6b7a1e2c447e9c3e608071a2813471d

How researchers see this paper

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

In Materials Chemistry

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

In Mechanical Engineering

1. Xiaogang Li

In Metals and Alloys

1. Xuequn Cheng
2. Xiaogang Li

Citations in the corpus

• Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning (2020, methodology): “Other methods that were less commonly used but are also suitable for mining time-series corrosion data include Markov chains [29,30], grey analyses [31,32] and Monte-Carlo simulations [33,34].”