## 1 Introduction  
Abdominal aortic aneurysm (AAA) is a disease in which the arteries that transport blood from the heart down through the chest and abdomen bulge or swell due to various factors. Usually, AAAs do not manifest any distinct symptoms and are only incidentally discerned during screening or tests performed for another reason. In addition, AAA rupture is frequently caused by different factors, and its prediction is critical because the mortality rate of ruptured AAAs is high. Such a rupture typically occurs when the wall stress exceeds the strength of the aortic tissue due to the pressure exerted on the aortic wall. The wall stress depends on the geometry, location, blood pressure, and characteristics of a patient's aortic tissue. In recent decades, studies on the risk factors of AAA have been conducted based on experimental, theoretical, and computational simulations. In these studies, a method for measuring the wall stress in AAAs using finite element analysis has been established as a more advanced approach than the existing method [1, 2]. In particular, based on computed tomography (CT) image data, computational studies considering the diameter [1], symmetry [3], centerline curvature [4], rupture risk index [5, 6], wall thickness [7, 8, 9], intraluminal thrombus (ILT) [10, 11, 12, 13, 14], calcification [15, 16], and geometry of AAA [17, 18, 19, 20] have been conducted to predict AAA rupture. [PERSON] et al. [2] obtained and analyzed the CT data of 48 patients with AAA to investigate wall stress according to the progression of the disease. Thereafter, they per formed finite element analysis and found that the peak wall stress (PWS) increased as the AAA progressed. In a later work, [PERSON] et al. [1] conducted a similar analysis using the CT data of 159 patients to investigate the risk of AAA rupture over time. They found that the prediction of AAA rupture according to the PWS was more reliable than that based on the aneurysm's diameter. [PERSON] et al. [4] also investigated 39 AAA models and reported that the mean centerline curvature was an important factor in the multiple regression analysis of the correlation between the PWS and geometrical parameters. In addition, [PERSON] et al. [3] determined the relationship between the PWS and geometrical parameters of 15 AAA models and reported that considering AAA asymmetry was important in predicting rupture. [PERSON] et al. [19] performed a statistical analysis of the correlation between the PWS and 47 indicators. They reported that seven indicators (fortuosity, ratio of maximum diameter to proximal neck diameter, outer wall surface area of aneurysm, median Gaussian curvature, maximum compactness, and mean curvature mode) could serve as surrogates for assessing the AAA. [PERSON] et al. [20] conducted a scenario simulation to evaluate the sensitivity of wall stress to geometrical and mechanical properties. They reported that an integration of the geometrical parameter and mechanical properties into clinical decision-making was necessary. The results of these various studies show that the behavior of aortic tissue was examined according to environmental conditions of AAA. However, confirming the behavior of the aortic wall based on the geometric parameters of the AAA was difficult. These parameters may induce stress concentrations on the structure of the aortic wall, and may lead to crack propagation in the aortic tissue. Therefore, to resolve the foregoing, in this study, the biaxial tensile test results of normal abdominal aorta (NAA) tissue and AAA tissue were investigated, and a computational simulation of these tests was conducted. To simulate the experiment, the existing constitutive model is employed to reflect the properties of anisotropic hyperelastic materials, and the material constants of the constitutive model were derived. In addition, a scenario involving the aneurysm's diameter, height, neck angle, and iliac angle was formulated based on the CT data of AAA patients.