It is investigated for the seismic consequences in the nuclear power plant (NPP) where the radiological hazard could be one of critical issues when the safety system is in failure. The artificial learning is done during the calculations of each time step. There are the simulations for the artificial neural networking (ANN) as the precision, sensitivity (recall value), specificity, and accuracy which are 21.48%, 50.53%, 25.47%, and 32.68% respectively. Likewise, the recurrent neural network (RNN) modeling has 23.64%, 54.53%, 25.56%, and 34.17% respectively. In the comparisons for ANN and RNN, the values of ANN’s parameters are lower than those of RNN in all values of precision, recall, specificity, and accuracy. As the designed factors for the nuclear matters increase, the estimations could be better in considering the conditional situations.
Funding source: The Cyber University of Korea
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: This study was supported by a grant of the Cyber University of Korea.
Conflict of interest statement: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Asim, K.M., Martínez-Álvarez, F., Basit, A., and Iqbal, T. (2017). Earthquake magnitude prediction in Hindukush region using machine learning techniques. Nat. Hazards 85: 471–486, https://doi.org/10.1007/s11069-016-2579-3.Search in Google Scholar
Ayo-Imoru, R.M. and Cilliers, A.C. (2018). Continuous machine learning for abnormality identification to aid condition-based maintenance in nuclear power plant. Ann. Nucl. Energy 118: 61–70, https://doi.org/10.1016/j.anucene.2018.04.002.Search in Google Scholar
Bagriacik, A., Davidson, R.A., Hughes, M.W., Bradley, B.A., and Cubrinovski, M. (2018). Comparison of statistical and machine learning approaches to modeling earthquake damage to water pipelines. Soil Dynam. Earthq. Eng. 112: 76–88, https://doi.org/10.1016/j.soildyn.2018.05.010.Search in Google Scholar
Buisson, B. and Lakeha, D. (2019). Towards an integrated machine-learning framework for model evaluation and uncertainty quantification. Nucl. Eng. Des. 3541: 110197, https://doi.org/10.1016/j.nucengdes.2019.110197.Search in Google Scholar
Ge, F., Li, Y., Yuan, M., Zhang, J., and Zhang, W. (2020). Identifying predictors of probable posttraumatic stress disorder in children and adolescents with earthquake exposure: a longitudinal study using a machine learning approach. J. Affect. Disord. 2641: 483–493, https://doi.org/10.1016/j.jad.2019.11.079.Search in Google Scholar PubMed
Hanna, B.N., Dinh, N.T., Youngblood, R.W., and Bolotnov, I.A. (2020). Machine-learning based error prediction approach for coarse-grid Computational Fluid Dynamics (CG-CFD). Prog. Nucl. Energy 118: 103140, https://doi.org/10.1016/j.pnucene.2019.103140.Search in Google Scholar
Jiao, P. and Alavi, A.H. (2020). Artificial intelligence in seismology: advent, performance and future trends. Geosci. Front. 11: 739–744, https://doi.org/10.1016/j.gsf.2019.10.004.Search in Google Scholar
Kim, T.K., Park, J.K., Lee, B.H., and Seong, S.H. (2019). Deep-learning-based alarm system for accident diagnosis and reactor state classification with probability value. Ann. Nucl. Energy 133: 723–731, https://doi.org/10.1016/j.anucene.2019.07.022.Search in Google Scholar
Lamarsh, J.R. and Baratta, A.J. (2017). Introduction to nuclear engineering, 4th ed. London, United Kingdom: Pearson.Search in Google Scholar
Olson, D.L. and Delen, D. (2008). Advanced data mining techniques, 1st ed. New York, USA: Springer, p. 138.Search in Google Scholar
Paltrinieri, N., Comfort, L., and Reniers, G. (2019). Learning about risk: machine learning for risk assessment. Saf. Sci. 118: 475–486, https://doi.org/10.1016/j.ssci.2019.06.001.Search in Google Scholar
Park, H.M., Lee, J.H., and Kim, K.D. (2020). Wall temperature prediction at critical heat flux using a machine learning model. Ann. Nucl. Energy 141: 107334, https://doi.org/10.1016/j.anucene.2020.107334.Search in Google Scholar
Rundle, J.B. (1989). Derivation of the complete Gutenberg–Richter magnitude-frequency relation using the principle of scale invariance. J. Geophys. Res. Solid Earth 94: 12337–12342, https://doi.org/10.1029/JB094iB09p12337.Search in Google Scholar
SDS (2022). What is System Dynamics; System Dynamics Society (SDS), Littleton, USA, https://systemdynamics.org/what-is-system-dynamics/.Search in Google Scholar
Vantana (2015). Vensim code system. Ventana Systems, Inc., Harvard, USA.Search in Google Scholar
Zeng, Y., Liu, J., Sun, K., and Hu, L. (2020). Machine learning based system performance prediction model for reactor control. Ann. Nucl. Energy 113: 270–278, https://doi.org/10.1016/j.anucene.2017.11.014.Search in Google Scholar
Zhang, Y., Burton, H.V., Sun, H., and Shokrabadi, M. (2018). A machine learning framework for assessing post-earthquake structural safety. Struct. Saf. 72: 1–16, https://doi.org/10.1016/j.strusafe.2017.12.001.Search in Google Scholar
© 2022 Walter de Gruyter GmbH, Berlin/Boston