Advancing the understanding of material behavior and phenomena related to the size effect in small-scale components is important for settings where limited quantities of material samples can be tested. Through research and collaborative efforts, the scientific community and industry stakeholders have established guidelines for sub-sized specimen testing, encompassing best practices for specimen preparation, testing equipment, test procedures, and data analysis methods. However, prior investigations of the specimen size effect in the literature typically involved a relatively small number of test records. To address this limitation, our team created a large dataset of tensile test records for nuclear structural materials collected from peer-reviewed articles, consisting of 1,070 records with 54 features. In this study, we introduced a machine learning-based approach for predicting tensile properties of sub-sized specimens for stainless steel 316 alloys, and we developed methods for incorporating uncertainty quantification into predicted properties. Furthermore, we leveraged the curated database to investigate the specimen size effect and conducted an empirical validation of the reported critical values for the dimensions and geometry of sub-sized specimens. Additionally, we validated existing analytical models for correlation of the total elongation between sub-sized and standard-sized specimens in tensile testing. Our findings demonstrate the potential of machine learning techniques to enhance understanding of specimen size-related material behavior and highlight the need for coordinated efforts in developing large, open-source databases of mechanical testing data to support further research.
