Affiliation:
1. Army Engineering University of PLA, Nanjing, China
Abstract
In this study, the authors introduced energy entropy as a reference feature into the field of blast vibration recognition classification and achieved good results. On the basis of the previous experimental database, 4 kinds of typical vibration signals were selected to form the sample group (building collapse vibration, surface rock blast vibration, underground tunnel blast vibration, and natural gas pipeline explosion vibration). EEMD (ensemble empirical mode decomposition) algorithm was used to calculate the energy entropy of each signal. Taking eigenvector composed of CEE (components of energy entropy) as input, multiclassification SVM algorithm was used for training and prediction. Prediction accuracy was more than 80%. Compared with BP (backpropagation) neural network algorithm, SVM (support vector machine) algorithm has higher training efficiency. The research results can be used in urban vibration monitoring, identify the nature of vibration source in time, and provide technical support for rapid response of emergency rescue.
Funder
National Natural Science Foundation of China
Subject
Mechanical Engineering,Mechanics of Materials,Geotechnical Engineering and Engineering Geology,Condensed Matter Physics,Civil and Structural Engineering
Cited by
6 articles.
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