Creep test material rupture prediction by neural networks

This work focuses on acoustic emission analysis of mechanisms damage in fiber composite materials, subjected to heavy loads during a creep test. The goal of the present study was to develop and evaluate machine learning algorithms for the prediction of material rupture with creep test by traction method. This study aimed to predict if a tensile specimen will break in 30 seconds or not. Multilayer Perceptrons were trained retrospectively in a group of 80 samples moments and tested prospectively in a group of 16 tensile specimens. During the 5-cross validations we reached a sensitivity of 88% and a specificity of 88% in the prospective group. The mean area under the ROC (Receiver Operating Curves) was equal to 0.86. Those results are very interesting because they are a first important step in the lifetime prediction of material rupture before significant damages can occur.