## 1 Introduction  
Metal corrosion, which is a chemical or electrochemical reaction between materials and severe environments [1], directly leads to the deterioration of material properties [2] and reduces the load-bearing capacity of infrastructures [3]. According to the World Corrosion Organization, the annual cost of corrosion repair is USD 2.5 trillion, accounting for 3% of the global GDP [4]. The high humidity and salinity in the marine environment make materials easily susceptible to corrosion [5]. As the most prevalent type of corrosion [6] in the marine environment, pitting is one of the most destructive forms of corrosion of steel structures [7]. Research on pitting is generally divided into experimental study and numerical simulation [8; 9; 10]. Since pitting experiments require many experimental resources and the relevant test parameters are difficult to control, numerical simulation has become a more common method, and the pitting modeling is crucial to the accuracy of numerical simulation. [PERSON] et al. [11; 12] investigated the effect of corrosion pits on the ultimate strength-carrying capacity of mild steel rectangular plates under uniaxial compression. Comparison between the finite element simulation results and the equations showed a satisfactory fit. [PERSON] et al. [13] conducted a series of tensile tests to investigate the effect of pitting on the tensile strength of members and found that the tensile strength decreases as the thickness loss from pitting increases, and that the decrease in the tensile strength of members due to pitting is greater than the decrease in the tensile strength of members due to uniform thickness loss. Studying pitting modeling is important for an in-depth understanding of the mechanism of the pitting action and for the corresponding preventive detections. [PERSON] et al. [14] used a three-dimensional metacellular automaton model to completely simulate the process of localized etching pits on metal surfaces with time, but their model is too complex to be used in practice. To use pitting models for the study of large-scale samples, batch pitting modeling is needed directly from the perspective of pitting parameters. The common pitting parameters include the shape, distribution, and geometry of pitting. Research on the pitting corrosion on suspension wires found that the pits in the corroted wires had four typical shapes, and the maximum pitting depth fitted a Weibull distribution while the length and the width fitted a lognormal distribution [15]. [PERSON] et al. [16] used a finite element model with a uniform arrangement of pitting pits to investigate the load-carrying capacity of the members after pitting. [PERSON] [17] proposed a mathematical model to qualify the extreme value of pitting corrosion depth according to aging time. Regarding the depth distribution of pitting pits, [PERSON] et al. [18] successfully predicted the depth distribution of pitting pits in oil and gas pipelines by developing a Markov model with an accuracy of more than 90%; [PERSON] [19] collected corrosion data by immersion tests and found that the depth of pitting pits had a bimodal Gaussian distribution; [PERSON] [20] found that the Weibull distribution could better describe the depth distribution of pitting pits on carbon steel surfaces. Uniform and random distributions are two methods commonly used to express the distribution of pitting pit locations. The uniform distribution simplifies the modeling process but it neglects the randomness of pitting, so the random distribution has better accuracy, comparably [21]. Within the existing research, there are few studies related to the random distribution of pitting components, which is a significant drawback to a better understanding of the pitting mechanism. Considering the hazards of pitting damage, the lack of a timely determination of pitting damage will greatly harm economic prospects and resources, which is not in accordance with the Sustainable Development Goal [22]. [PERSON] et al.