Modeling the Impact of Hydrogen Embrittlement on the Fracture Toughness of Low-Carbon Steel Using a Machine Learning Approach

This study aims to advance the understanding of hydrogen embrittlement (HE) in low-carbon and low-alloy steels by developing a predictive framework for assessing fracture toughness (FT), a critical parameter for mitigating HE in hydrogen infrastructure. A machine learning (ML) model was constructed by analyzing data from relevant literature to evaluate the fracture toughness of steels exposed to hydrogen environments. Seven ML modeling techniques were initially considered, with four selected for detailed evaluation based on predictive accuracy. The chosen modeling techniques were k-nearest neighbors (KNN), random forest (RF), gradient boosting (GB), and decision tree regression (DT). The selected models were further evaluated for their predictive accuracy and reliability, and the best model was used to perform parametric studies to investigate the impact of relevant parameters on FT. According to the results, the KNN model demonstrated reliable predictive performance, supported by high R-squared values and low error metrics. Among the variables considered, hydrogen pressure and yield strength emerged as the most influential, with hydrogen pressure alone accounting for 32% of the variation in FT. The model revealed a distinct trend in FT behavior, showing a significant decline at low hydrogen pressures (0–6.9 MPa) and a plateau at higher pressures (>8 MPa), indicating a saturation point. Alloying element contents, specifically those of carbon and phosphorus, also played a notable role in FT prediction. Additionally, the study confirmed that low concentrations of oxygen (<200 ppm) mitigate HE in X70 steel, likely by limiting hydrogen uptake. FT predictions do not show noticeable variations with lower displacement rates (<0.1 mm/min), indicating the need for low-rate measurements for accurate ML model training.