A data-driven approach for seismic damage detection of shear-type building structures using the fractal dimension of time–frequency features

摘要

In this study, a data-driven approach for structural seismic damage detection and localization in a multipledegree-nof-freedomshear-type building structure subjected to strong groundmotion is presented. The proposedmethodnis based on the joint implementation of time–frequency analysis and fractal dimension (FD) characteristics. Thenapproximate analytical wavelet transform is first used to obtain the time–frequency feature (TFF) of the transientnresponse at the measured story. The TFF is defined as the real part of the wavelet coefficients. Next, the box-countingnmethod is used to acquire the FD of the TFF within the fundamental frequency band. It is verified that the proposednFDs at all stories of the linear system are identical, whereas the FDs at the stories with nonlinearities will be differentnfrom those at the stories with linearity. Therefore, the nonlinearity of the structure caused by strong ground motion cannbe detected and localized by comparing the FDs at the measured stories. A numerical simulation on a 10-story sheartypenbuilding was conducted. The simulation results indicate that the aforementioned approach is capable of detectingnand localizing single-location or multiple-location seismic damages in shear-type building structures under variousnseismic excitations and is robust to measurement noise. Finally, the sensor placement for this approach, the effectnof damping ratio, and choice of interstory drifts or accelerations as the measured signals are investigated and discussed.nCopyright © 2012 John Wiley & Sons, Ltd.