Structural Health Monitoring for impact localisation via machine learning

Abstract

The focus of this manuscript is Machine learning applied to the Structural Health Monitoring. Two different algorithms have been used to detect impacts on an aluminium plate, one based on the polynomial regression, the other one based on a shallow neural network. Both are supervised algorithms in which some data impacts are used for training, while a complementary subset of data is used for test. The two methods have been preliminarily optimized in terms of training and testing performance and, subsequently, compared in terms of accuracy. By using K-Fold cross validation procedure, with different combinations of training/test sets, the performances of the polynomial models with different degrees were evaluated calculating the Mean Radial Error. For the shallow neural network, three type of learning algorithms were compared: Levenberg-Marquardt, Bayesian Regularization, Scaled Conjugate Gradient. The study confirmed the effectiveness of Machine learning applied to the detection of impacts.