Machine learning was applied to classify tension-strain curves harvested from inflation tests on ascending thoracic aneurysm samples. The curves were classified into rupture and nonrupture groups using prerupture response features. Two groups of features were used as the basis for classification. The first was the constitutive parameters fitted from the tension-strain data, and the second was geometric parameters extracted from the tension-strain curve. Based on the importance scores provided by the machine learning, implications of some features were interrogated. It was found that (1) the value of a constitutive parameter is nearly the same for all members in the rupture group and (2) the strength correlates strongly with a tension in the early phase of response as well as with the end stiffness. The study suggests that the strength, which is not available without rupturing the tissue, may be indirectly inferred from prerupture response features.
Keywords: ATAA; machine learning; random forest; rupture; strength.
Copyright © 2018 John Wiley & Sons, Ltd.