Empirical Learning Investigations of the Stress Corrosion Cracking of Austenitic Stainless Steels in High-Temperature Aqueous Environments

A collection of data documenting the stress corrosion cracking (SCC) behavior of austenitic stainless steels (SS) in high-temperature aqueous environments was investigated using empirical learning techniques. Computer-based empirical learning systems based on classical and nonparametric statistics, connectionist models, machine learning methods, and fuzzy logic are described. An original method for inducing fuzzy rules from input-output data is presented. Performance comparisons of the various approaches are summarized, along with the relative intelligibility of the outputs. In both respects, the decision-tree approach was found to perform very well on the problem investigated.