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Joint damage identification for a steel frame using compression sensing-based EMI signatures

An experimental study of damage identification for a three-layered steel frame is implemented. Damages are introduced by completely loosening bolts over several conditions on the structure and then the piezoelectric admittance signatures are obtained under various damage locations. EMI signatures are pre-processed using the theory of compression sensing and the compressed signatures still keep the original damage information invariable. Finally, the principal components of the compressed EMI signatures are obtained as the input parameters of BP neural network for quantitative damage identification. Research shows that the transmission bandwidth and storage space of the processed data are reduced greatly using the compression perception technique, and the present hybrid method can detect the damage locations quantitatively.