# A GAN-Augmented Corrosion Prediction Model for Uncoated Steel Plates
## 1 Introduction  
Nowadays, pre-1930s steel structures are still in use all over the world, serving the public. According to statistics in 2012, 9% of all bridges in Japan had been in service for more than 50 years [1]. Ten years later, the ratio could reach 28%. In twenty years, the ratio could be higher than 53%. In the past few years, there are more and more reports of aging and damage of steel structures, which becomes a big safety concern [2]. One big reason for the deterioration and damage of steel structures is corrosion [3]. Corrosion will cause the loss of material from the surface of steel members [4], which leads to a thinner cross-sectional area of the steel structure and further reduces the stiffness of the structure and its load-bearing capacity [5]. As a result, the corrosion on the surface of steel members brings huge damage and economic losses to the industry. Based on estimation [6, 7], the loss due to the damage from corrosion of metal surfaces accounts for 3% to 5% of the gross national product of industrialized countries. At present, more and more efforts have been dedicated to the monitoring and control of corrosion, which becomes an important work of infrastructure maintenance. Corrosion is a long-term process. It is time-consuming to determine the degree of corrosion based on observations. Therefore, the simulation and prediction of the corrosion process play a crucial role in the modeling of corrosion damage and the determination of corrosion status promptly. In previous studies, the fractal and spatial correlation have been used to evaluate the surface characteristics of the corroded surface of steel plates, which were taken from actual structural members [8]. However, those methods have limitations on target materials. The relationship between corrosion damage and aging is still unclear. A widely applicable method is in need to effectively predict the corrosion behavior overtime and under different circumstances, which can benefit infrastructure maintenance in the following ways:  
* Predicting the surface corrosion of steel structures can save time for monitoring and observation. * Predicting the surface corrosion of steel structures can facilitate the modeling of corroded surfaces. * Predicting the surface corrosion of steel structures can predict the lifetime of the steel structure, and provide early warning for facility maintenance. Without prompt maintenance, the corrosion of the aging steel structures could lead to major safety hazards. Therefore, the assessment of corrosion loss is very important in the maintenance and management of steel structures. However, traditional assessment methods are time-consuming and lack of accuracy. With the development of numerical simulation technology, developing a more efficient corrosion prediction method has attracted more and more attention. [PERSON] and [PERSON] proposed a stochastic model for corrosion damage evolution [9]. [PERSON] and [PERSON] used extreme value statistics to predict the development of localized corrosion damage and damage function analysis [10]. [PERSON], et al. [11] analyzed the shape of corroded surfaces using the semi-variogram model in the geostatistical analysis. [PERSON] and [PERSON] proposed a method to predict the remained thickness of plates and the period of penetrating based on the localized corrosion of steel members [12]. They used regression trees and variograms to obtain the time-dependent corroded surface models, and referential spatial statistics-based simulations to estimate the corroded surfaces corresponding to the service period. The corrosion rate, remaining life, and accelerated corrosion ratio in corrosive environments were thoroughly studied. The results demonstrated the feasibility of the prediction method. However, the time-based dynamic simulation is not enough and it is only a rough prediction model. Its accuracy is also far from applicable for simulating corroded surfaces. The current issues with the corrosion prediction are listed below:  
* Low prediction accuracy.