DOI: 10.3390/app13158883 ISSN: 2076-3417

A Convolutional Neural Network-Based Corrosion Damage Determination Method for Localized Random Pitting Steel Columns

Xu Jiang, Hao Qi, Xuhong Qiang, Bosen Zhao, Hao Dong
  • Fluid Flow and Transfer Processes
  • Computer Science Applications
  • Process Chemistry and Technology
  • General Engineering
  • Instrumentation
  • General Materials Science

As one of the most common forms of corrosion in the marine environment, pitting corrosion can have a detrimental impact on the ultimate strength of steel columns. Pitting pits are usually covered by corrosion products, and the detection of pitting is very difficult, so how to effectively identify random pitting corrosion on steel columns has become a very vital issue. In this paper, a deep-learning-based pitting damage determination method for steel columns is investigated by combining numerical simulation and theoretical analysis, which was validated by experimental results. First, a multi-parameter localized pitting corrosion model was proposed that considered the pitting corrosion randomness in time and space distribution. Second, the relationship between the ultimate strength and corrosion rate of steel columns was analyzed. Finally, a steel column damage determination framework was constructed based on the convolutional neural network. Results showed that the ultimate strength and corrosion rate developed different trends in various corrosion regions, and a damage determination accuracy of 90.2% could be achieved by the neural network after training, which satisfied the practical engineering requirements. This study lays the groundwork for further application of deep learning to the research on the pitting damage to steel structures.

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