Analysis of Radiation Embrittlement Trend Curves: Machine Learning and Multilevel Modeling

Gyeong Geun Lee, Min Chul Kim, Joonho Lee, Bong Sang Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Machine learning (ML) has been a widely used tool for recognizing patterns in an extensive database with increasing computing power. This study applied three ML techniques—Cubist, support-vector machine (SVM), and XGBoost (XGB)—to fit the BASELINE surveillance test database. The rule-based Cubist and tree-based XGB models showed a significantly lower root-mean-square deviation than the SVM and ASTM E900-15 nonlinear model. ML showed a good capability for prediction in the interpolation region of the dataset. However, there were significant errors in the extrapolation region in predicting the effect of large fluence on the transition temperature shift (TTS). ML can be very useful in the development of a preliminary model because it can quickly capture the trend of the dataset. To improve the prediction of the TTS trend with fluence in the grouped data with the same initial Charpy impact property, a varying intercept model was introduced into the ASTM E900-15 trend curve, and the model coefficients were estimated by a multilevel modeling procedure. This model considered both the trend of all data and the trend of each group. It then provided more reliable intercepts in each group for TTS prediction with fluence. The multilevel modeling of the grouped datasets can increase the predictive power of the embrittlement trend model for commercial power plants.

Original languageEnglish
Title of host publicationRadiation Embrittlement Trend Curves and Equations and Their Use for RPV Integrity Evaluations
EditorsWilliam L. Server, Milan Brumovsky, Mark Kirk
PublisherASTM International
Pages336-353
Number of pages18
ISBN (Electronic)9780803177413
DOIs
Publication statusPublished - 2023
Event2022 Symposium on Radiation Embrittlement Trend Curves and Equations and Their Use for RPV Integrity Evaluations - Prague, Czech Republic
Duration: 2022 Apr 192022 Apr 22

Publication series

NameASTM Special Technical Publication
VolumeSTP 1647
ISSN (Print)0066-0558

Conference

Conference2022 Symposium on Radiation Embrittlement Trend Curves and Equations and Their Use for RPV Integrity Evaluations
Country/TerritoryCzech Republic
CityPrague
Period22/4/1922/4/22

Bibliographical note

Funding Information:
We would like to thank the ASTM E10.02 committee for providing the dataset for this study. This work was supported by a National Research Foundation of Korea grant funded by the Korean government.

Publisher Copyright:
Copyright © 2023 by ASTM International.

Keywords

  • embrittlement trend curve
  • ETC
  • irradiation embrittlement
  • low-alloy steel
  • machine learning
  • ML
  • multilevel model
  • reactor pressure vessel
  • RPV
  • XGBoost

ASJC Scopus subject areas

  • General Materials Science

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