# A GAN-Augmented Corrosion Prediction Model for Uncoated Steel Plates
## 5 Conclusions  
The deterioration and damage of aging steel structures have led to huge economic loss and safety hazards. A big reason for the damage is the corrosion of steel members, which can cause the loss of material from the surface of steel members and reduce the thickness, stiffness, and load-bearing capacity of the steel structure. It is worth a thorough investigation to simulate and predict the corrosion on steel structures and to further achieve more effective and efficient maintenance management. Traditional corrosion assessment takes a long time and lacks clear criteria. However, the studies in this field have limitations on the target material or environment, lack of generality. In this research, we propose a GAN-based machine learning model to augment the dataset and simulate and predict the corrosion of SM400A, SM490A carbon steel, and SMA400 AW, SMA490 AW weathering steel in four environments: ISO16539, PWRI, Atmospheric exposure I in Choshi, Atmospheric exposure II in Miyakojima. Our proposed method can effectively predict the corrosion behavior of uncoated steel structures over time and under different circumstances. It can achieve faster and more accurate predictions of corrosion. We use Unet to simulate the corrosion surface. The structure of Unet determines that the distribution of the generated data is very close to the original data. MobileNet-V2 requires a small number of parameters and has a relatively high score among other image classification algorithms. Meanwhile, the GAN structure is better controlled compared to cGAN and InfoGAN. The GAN structure composed of Unet and MobileNet-V2 provides outstanding performance to address the research problems in this paper. Through comparative experiments, our model achieves high prediction accuracy and is verified to be reliable and generally applicable.  
Meanwhile, there are some limitations in this research, which can be addressed in future work. First, the steel plates in our experiments are uncoated. In reality, many steel structures such as bridges have their bare steels generally treated with a coating film. This method still requires more analysis and Tests on the corrosion prediction of the coated steel structure. Furthermore, our system does not consider severe localized corrosion on steel plates, which could be further studied as future work. Thirdly, this method does not quantify the change of the corroded surface and lacks the analysis of the material itself. Finally, the prediction accuracy could be further improved. There are outliers that occasionally appear in the current corrosion simulation. How to dynamically monitor and suppress outliers is the future direction to improve the simulation.  
Conceptualization, F.J. and M.H.; methodology, F.J.; software, F.J.; validation, F.J.; writing--original draft preparation, F.J.; writing--review and editing, F.J. and M.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by JST SPRING, Grang Number JPMJSP2138.  
Not applicable.  
The source code supporting the conclusions of this article is available at [[https://github.com/glachehail/Corrosion_Prediction_Model](https://github.com/glachehail/Corrosion_Prediction_Model)]([https://github.com/glachehail/Corrosion_Prediction_Model](https://github.com/glachehail/Corrosion_Prediction_Model)) (accessed on 4 April 2022). Interested readers can reach out to the authors at [EMAIL_ADDRESS] (accessed on 4 April 2022) to request a copy of the dataset.  
A part of this research was supported by Sho-bond corporation.  
The authors declare no conflict of interest.