LBE corrosion fatigue life prediction of T91 steel and 316 SS using machine learning method assisted by symbol regression

This study employs machine learning models assisted by symbol regression to achieve satisfactory corrosion fatigue life prediction for T91 steel and 316L stainless steel (SS) used in 4th generation advanced nuclear power plants. Symbol regression features improve the performance of all considered machine learning models. The artificial neural network (ANN) model shows the most significant improvement of 22 % decrease in RMSE regarding to the model without symbol regression features. Additionally, the well-trained ANN model is transferred to predict 316L SS with a 50 % reduction in training samples, highlighting its potential for more efficient model training and deployment.

Automated summary

This study utilizes machine learning models with symbol regression to accurately predict the corrosion fatigue life of T91 steel and 316L stainless steel in advanced nuclear power plants. Symbol regression features enhance the performance of all machine learning models, with the artificial neural network model showing the most significant improvement.