Dawei Zhang1,2*, Dongmei Fu2,3， Xiaogang Li1,2，Jintao Shu1

1Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China

2National Materials Corrosion and Protection Data Center, Beijing 100083, China

3School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China

EXTENDED ABSTRACT: Materials corrosion is a complex failure process affected by many factors and can seriously jeopardize the service safety of infrastructure and major equipment. The estimated cost of corrosion losses adds up to more than 3 trillion yuan in China every year. The evaluation of corrosion resistance and behaviors takes the longest time and the highest cost in the whole chain of materials R&D and application. As such, corrosion evaluation is considered as one of the most challenging topics to study in the U.S. Materials Genome Initiative and is also a key and featured research direction in the field of material genome engineering in China. Materials corrosion processes are often described as high-dimensional functions as they depend on many parameters including material physical and chemical properties and environmental factors and also their complex interactions. To efficiently understand the relationship between high-dimensional corrosion data and influencing parameters, it is necessary to reduce the dimensionality of corrosion data and select some key characteristic parameters; Then the mapping relationship between corrosion data and key characteristic parameters can be established. Based on the data resource and technological foundation of the National Materials Corrosion and Protection Data Center, this study adopts efficient machine learning methods, with comprehensive consideration of the source, structure and internal logical relationship of the corrosion data and the application of different linear, local linear and nonlinear analysis methods. In views of the statistics, trend and combination characteristics, the influence parameters of materials corrosion are effectively selected and the data dimensionality is reduced via correlation and causality analyses. Subsequently, the nonlinear model between the key characteristic parameters and the corrosion process is constructed by using support vector machine, random forest, graph neural network and their optimized algorithms, in order to realize data-driven intelligent corrosion evaluation and prediction. Several case studies related to atmospheric corrosion evaluation and corrosion inhibitor selection are presented.

Keywords: Corrosion and Protection; Corrosion Big Data; Machine Leaming; Corrosion Prediction