Prediction of material behavior by database and neural network model within Bayesian framework

Tsuji, Hirokazu; Fujii, Hidetoshi*

A neural network model within a Bayesian framework was adopted based on the material database constructed by JAERI for prediction of creep rupture properties of irradiated type 304 stainless steel. Stress level was modeled as a function of 18 variables, including rupture life, creep test temperature, chemical compositions; 10 elements, heat treatment temperature, heat treatment duration, neutron irradiation temperature, fast neutron fluence, thermal neutron fluence, irradiation time, based on JAERI material database in which 347 creep rupture data sets of type 304 stainless steels were stored. The Bayesian method puts error bars on the predicted values of the rupture strength and allows the significance of each individual factor to be estimated.