Seismic damage assessment using improved wavelet-based damage-sensitive features

Abstract

In this paper, acceleration responses of steel MRFs under different ground motions using incremental dynamic analysis (IDA) are used for nonlinear damage diagnosis. In the first step, auto-regressive moving-average with exogenous input (ARX) model, along with stabilization diagram, is utilized to assess the natural frequencies of MRFs. In the second step, complex Morlet (cmorfb-fc) wavelet-based refined damage-sensitive features (rDSFs), as new DSFs considering higher mode contributions and end-effect modifications, are proposed. Improved efficiency and accuracy of the proposed rDSFs are presented through benchmark steel MRFs. Results indicate that the damage pattern (DP) causing first plastic hinge formation and also the damaged story can be accurately recognized using the proposed wavelet-based rDSFs. In addition, the damage extent (severity) for each DP at each story can be assessed using the cumulative sum of wavelet energy at time-shift b (or sum of scalogram for each time) over the refined effective time of vibration (refined ETV) for each output acceleration when compared with that over the refined ETV for the input ground acceleration. Finally, results also indicate improvement over the DSFs existing in the technical literature.