Data-driven identification of structural damage under unknown seismic excitations using the energy integrals of strain signals transformed from transmissibility functions

Abstract

In recent years, the methodology based on the energy variations of structural dynamic strain responses has been developed. Although it is an efficient data-driven structural damage identification approach, it can only be used for structures under impulse or known excitations. To overcome this limitation, an improved method is proposed in this paper for the rapid damage identification of beam-like structures using structural dynamic strain responses under unknown seismic excitations. The influence of different excitations to an intact and damaged structure, in which the intact structure is under ambient excitations and structural damage is caused by unknown seismic excitations, is eliminated via the transmissibility functions (TFs) of measured structural strain responses. Contrary to the previous TFs of structural acceleration or displacement responses, TFs of structural strains is more sensitive to local damage in beam-like structures. Moreover, power spectral density transmissibility functions (PSDTFs) are used instead of the traditional Fourier transform TFs. Then, through the inverse Fourier transformation in the frequency domain, the PSDTFs are returned to the time-domain signals to avoid the problem of selecting the proper frequency band in previous studies on identifying structural damage based on TFs in the frequency domain. Finally, structural damage can be identified based on the energy variations of the time-domain strain signals from the intact and damaged structures. Numerical examples of structural damage identification of a seven-storey planar frame and a cable-stayed bridge girder under unknown seismic excitations are provided to demonstrate the effectiveness and robustness of the improved method. Moreover, structural damage identification of a laboratory cable-stayed bridge tower model subjected to shaking table tests is performed to validate the good performance of the proposed method.