## 1 Introduction
[23] were the first to propose a damage identification method using intrinsic frequency. [PERSON] et al. [24] presented various methods of intrinsic frequency damage detection and pointed out that since the stress is minimum at the point where the modal displacement is zero, the site of damage is in the vicinity of the point where this modal displacement is zero. However, the intrinsic frequency is less sensitive to pitting damage and cannot be accurately determined [25; 26]. [PERSON] et al. [27] developed a damage identification method based on the vibration mode and found that the vibration mode change occurred at the damaged area, which was positively correlated with the damage degree. [PERSON] et al. [28] developed a new method for damage localization and severity estimation based on the application of modal strain energy. This method requires information on the variation of only a few lower intrinsic frequencies to locate the damage and estimate its severity. Related studies have shown that the vibration mode has a good mapping for corrosion damage identification [29; 30], which can be used in pitting corrosion determination. According to the current literature, there are still large gaps in the relevant research. Machine learning, as an efficient analytical tool with proven powerful learning and nonlinear fitting capabilities, has played an important role in the research on the pitting corrosion of steel components in recent years. Six machine learning models with eight different combinations of input variables were assessed using eleven statistic indicators to predict the maximum pitting corrosion depth in oil and gas pipelines [31]; as a result, the Kriging model had the best performance and accuracy. [PERSON] [32] proposed a Gradient Boosting Machine with Principal Component Analysis to predict the corrosion defect depth growth of oil pipelines with high accuracy. The author utilized the parameters of the pipelines to predict the corrosion conditions in the future using the proposed Principal Component Analysis and Gradient Boosting Machine (PCA-GBM) method. Deep learning, as a further development, has taken pitting research to a new level. A deep-learning framework was developed to find the contributions of alloying elements to local pitting resistance, which benefited the investigation of corrosion-resistant alloys for alloy design [33]. Researchers have conducted many studies related to pitting using deep learning. [PERSON] et al. [34] used the artificial neural network (ANN) method to estimate the load-carrying capacity of deteriorated steel beam models with different levels of pitting under patch loading. [PERSON] [35] developed a subspace-clustered neural network model to search for the relationship between pipeline corrosion and related input parameters using particle swarm optimization in a feed-forward network. [PERSON] [36] et al. used truss bridge node displacements as a damage indicator and also used ANNs to predict the damage to truss bridge unit stiffness, still based on a finite element model, with no foot-size test to confirm the practicability of the method. As for the stress-strain properties of pitting steel components, related studies found that the yield platform of the load-displacement curve disappeared when pitting corrosion occurred in steel components, and ultimate strength, yield strength, as well as elongation were correlated with the pitting conditions [37]. The convolutional neural network (CNN) is a kind of matrix feature extraction using convolutional kernels that has a powerful learning ability. The Faster R-CNN technique has already been used for computer visual inspection of the steel corrosion and bolt corrosion and determined them to be 91.8% and 86.1% AP, respectively [38]. [PERSON] et al. [39] used a deep-learning method to calculate the effective thickness of the shell models to equivalently substitute the solid models, which were utilized to evaluate the tensile performance of the pitting components.