Impact load identification and localization method on thin-walled cylinders using machine learning

Abstract

Abstract In this paper, a novel impact load identification and localization method on actual engineering structures using machine learning is proposed. Three machine learning models, including a gradient boosting decision tree (GBDT) model based on ensemble learning, a convolutional neural network (CNN) model and a bidirectional long short-term memory (BLSTM) model based on deep learning, are trained to directly identify and locate impact loads according to dynamic response. The GBDT model and the CNN model can reversely identify force peak and location of impact loads. The BLSTM model can reconstruct the time history of impact loads. The method is verified on a thin-walled cylinder with obvious nonlinearity. The result shows that the method can accurately identify impact loads and its location. The characteristics of the three models are compared and the influence of structural boundary conditions on the accuracy of identification is discussed. The proposed method has the potential to be applied to various engineering structures and multiple load types.