## 6 Conclusions  
This paper combined numerical simulation and theoretical analysis to carry out systematic research on the ultimate strength of localized random pitting steel components and established localized random pitting damage determination methods using the CNN. In this paper, first, a numerical simulation of random pitting was realized by establishing a multi-parameter localized random pitting numerical model, combined with Python language and ABAQUS finite element software. Then, using cylindrical steel columns for offshore steel platforms as the study object, the mechanical properties of the axial compression under localized random pitting were investigated to clarify the critical corrosion rate to determine the damage. Finally, the critical pitting rate of the cylindrical steel column was determined, and a convolutional neural network was built and trained to determine the damage by inputting the vibration mode of the steel column as the classification criterion, and the accuracy and effectiveness of the neural network were demonstrated. Conclusions can be drawn as follows:  
1. The accuracy of the numerical model. The multi-parameter localized random pitting numerical model established herein can fully express the randomness of pitting pits in shape and location while ensuring the reasonable shape parameters and location coordinates of pitting pits, which can fully describe the realistic pitting situation of steel columns.
2. Statistical patterns of bearing capacity of localized random pitting corrosion in steel columns. With low dispersion, the ultimate strength distribution of localized random pitting steel columns has good statistical significance. For steel columns with one end  
Figure 19: Loss of training set after modification.  
Figure 20: (**a**) Loss of validation set after modification; (**b**) accuracy of study.  
fixed and one end hinged, the ultimate strength decreases linearly with the increase in the pitting corrosion rate when pitting occurs in regions 1-4; the ultimate strength shows a secondary parabolic downward trend as the pitting corrosion rate increases when pitting occurs in region 5; the bearing capacity of the steel column first remains constant and then shows a linear decrease with the increase in corrosion rate when pitting occurs in regions 6-8.
3. A pitting detection neural network. This study establishes a convolutional neural network to determine whether a steel component is damaged or not by inputting the first six vibration modes. The network has a high detection accuracy, which meets the practical engineering requirements and proves that it is of great theoretical significance and actual application value to determine the damage to a steel component by the convolutional neural network.
4. The detection system of random pitting corrosion. Based on the numerical model of random pitting, the critical corrosion rate is defined by studying the ultimate strength of pitting components, and then vibration modes are input to train the convolutional neural network for damage determination; thus, a localized random pitting damage determination network with reasonable accuracy is worked out.  
Methodology, X.J. and X.Q.; Software, B.Z.; Validation, B.Z. and H.D.; Investigation, H.Q. and H.D.; Data curation, H.Q. and B.Z.; Writing--original draft, H.Q.; Writing--review & editing, B.Z.; Supervision, X.J.; Project administration, X.J. and X.Q.; Funding acquisition, X.Q. All authors have read and agreed to the published version of the manuscript.  
The financial support of the National Natural Science Foundation of China (52278206 and 52278207), the National Key R&D Program of China (2020 YFD1100403), Natural Science Foundation of Shanghai (21 ZR1466100), and Fundamental Research Funds for the Central Universities (02002150114) are highly appreciated.  
Not applicable.  
Not available due to privacy or ethical restrictions.  
The authors declare no conflict of interest.