## 1 Introduction  
Structural elements are often subjected to cyclic loading while in operation. For this reason, many researchers pay attention to the fatigue behaviour of structures, even nowadays, when it seems that all essential fatigue problems have been solved--including fatigue crack growth rates. Besides the experimental papers concerning fatigue crack growth rates, there are also some numerical analyses devoted to this problem, such as [1], and papers which examine machine learning methods to predict fatigue strength using existing databases, such as [2]. Knowledge of the fatigue crack growth rate is essential from the viewpoint of the residual fatigue life of a component. In order to determine the residual life, the critical size of the crack is needed. This depends primarily on the fracture toughness of the component, which can be determined using the J-R resistance curve. The J-based R-curve is currently used for the determination of the fracture toughness of ductile materials. The curve represents the dependence between the J integral and the crack extension \(\Delta\)a in fracture specimens. Knowing the effective yield stress or the flow stress of a material (usually taken as the mean of the tensile strength and the yield), the fracture toughness parameters \(J_{\rm in}\) and \(J_{0.2}\) can be determined. If the J-R curve is known for one type of specimen, then the J-R curve can also be determined for another type of specimen using the so-called Gurson method, providing the stress state conditions at the crack tip are similar. The Gurson method was first proposed in 1977 [3], and later modified by [PERSON] [4] and [PERSON] [5]. This method obtained recognition as a continuous damage model suitable for describing the ductile failure of metals. The GTN model has become very popular in the academic world as well as in industry for its use in structural integrity assessments and simulations of ductile fractures. It is still widely used in several state-of-the-art papers, e.g., ([6; 7; 8; 9; 10; 11]). We used this method for the determination of the J-based R-curve for SENT specimens. Depending on the crack/component configuration, the fracture toughness (the magnitude of a controlling fracture parameter at the instant of fracture) of the same material can vary. This is caused, to a great extent, by differences in the degree of the crack tip strain constraint. For a high constraint, the fracture toughness is low. When predicting fracture conditions of structural components, it is required that the prediction be conservative, meaning that the fracture toughness should be tested on specimens with a high constraint (even higher than the actual component is exposed to in operation). Therefore, special specimens with a high constraint are used in testing materials for their fracture toughness. Among them, the most widely used are CT and SENB specimens. These specimens are characterized by a high crack tip constraint, in contrast to SENT specimens, which are attributed to a lower constraint. The use of the GTN model for R-curve determination can significantly reduce the number of physical tests required. Our investigation, concerned with SENT specimens, was based on the standard experimental determination of the J-R curve (J-\(\Delta\)a dependence, where \(\Delta\)a stands for crack extension) using CT specimens. The R-curve for the CT specimens was determined using the conventional multi-specimen method. This means that a series of identical tests were performed on seven specimens in such a way that each specimen was loaded up to a certain force level. During loading (Testatron loading frame) the force (F) and the force-point displacement (f) were recorded by Peekel equipment. The areas below the F-f curves were evaluated and further used to determine the J integrals. The appropriate crack extensions were determined after the specimens were cooled down in liquid Nitrogen and broken open. Fracture toughness was characterized by two parameters, namely \(J_{\rm in}\) and \(J_{0.2}\).