Analyzing the structural integrity of ferritic steel structures subjected to large temperature variations requires the collection of the fracture toughness (K_Jc) of ferritic steels in the ductile-to-brittle transition region. Consequently, predicting K_Jc from minimal testing has been of interest for a long time. In this study, a Windows-ready K_Jc predictor based on tensile properties (specifically, yield stress σ_YSRT and tensile strength σ_BRT at room temperature (RT) and σ_YS at K_Jc prediction temperature) was developed by applying an artificial neural network (ANN) to 531 K_Jc data points. If the σ_YS temperature dependence can be adequately described using the Zerilli–Armstrong σ_YS master curve (MC), the necessary data for K_Jc prediction are reduced to σ_YSRT and σ_BRT. The developed K_Jc predictor successfully predicted K_Jc under arbitrary conditions. Compared with the existing ASTM E1921 K_Jc MC, the developed K_Jc predictor was especially effective in cases where σ_B/σ_YS of the material was larger than that of RPV steel.

The improvement in the fracture toughness J_c of a material in the ductile-to-brittle transition temperature region due to compressive residual stress (CRS) was considered in this study. A straightforward fracture prediction was performed for a specimen with mechanical CRS by using the T-scaling method, which was originally proposed to scale the fracture stress distributions between different temperatures. The method was validated for a 780-MPa-class high-strength steel and 0.45% carbon steel. The results showed that the scaled stress distributions at fracture loads without and with CRS are the same, and that J_c improvement was caused by the loss in the one-to-one correspondence between J and the crack-tip stress distribution. The proposed method is advantageous in possibly predicting fracture loads for specimens with CRS by using only the stress–strain relationship, and by performing elastic-plastic finite element analysis, i.e., without performing fracture toughness testing on specimens without CRS.

In this paper, a new method for scaling the crack tip stress distribution under small scale yielding condition was proposed and named as T-scaling method. This method enables to identify the different stress distributions for materials with different tensile properties but identical load in terms of K or J. Then by assuming that the temperature dependence of a material is represented as the stress-strain relationship temperature dependence, a method to predict the fracture load at an arbitrary temperature from the already known fracture load at a reference temperature was proposed. This method combined the T-scaling method and the knowledge “fracture stress for slip induced cleavage fracture is temperature independent.” Once the fracture load is predicted, fracture toughness Jc at the temperature under consideration can be evaluated by running elastic-plastic finite element analysis. Finally, the above-mentioned framework to predict the Jc temperature dependency of a material in the ductile-to-brittle temperature distribution was validated for 0.55% carbon steel JIS S55C. The proposed framework seems to have a possibility to solve the problem the master curve is facing in the relatively higher temperature region, by requiring only tensile tests