A novel prediction method of creep rupture life of 9-12% chromium ferritic steel based on abductive network

The creep rupture life of 9-12% chromium ferritic steel is predicted as a function of alloy composition, creep stress, and creep temperature. A database is made up from data in previous publications. A novel abductive network is constructed to predict creep rupture life. With a four-layer architecture, the network shows a precise prediction of creep rupture life of 9-12% chromium ferritic steel. Performance is examined and compared with backpropagation algorithm (BP) neural network. Results indicate that the proposed approach is more accurate than Larson-Miller parametric method and more efficient than that of BP neural network. Automatic relevance determination reveals that the influence of Cr and W on creep rupture life of 9-12% chromium ferritic steel are the greatest amongst the alloying elements. (C) 2013 Elsevier Ltd. All rights reserved.