Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development, resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend. The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method. The information on the corrosion process was recorded using the galvanic corrosion current monitoring method. The gradient boosting decision tree (GBDT) machine learning method was used to mine the corrosion mechanism, and the importance of the structure factor was investigated. Field exposure tests were conducted to verify the calculated results using the GBDT method. Results indicated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel. Different mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion. When the corrosion reached a stable state, the increase in Mn element content increased the corrosion rate of 3Ni steel, while Cu reduced this rate. In the presence of stress, the increase in Mn element content and Cu addition can inhibit the corrosion process. The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology, verifying the reliability of the big data evaluation method and data prediction model selection.