عنوان مقاله [English]
Damage \ detection \ and \ structural \ health \ monitoring \ (SHM) have become vastly popular within the field of structural engineering over the past few decades. These methods and procedures are utilized in order to characterize the structural integrity of a system and to provide a decision whether or not the system has the appropriate bearing capacity. The S-transform has been developed over the last few years in an attempt to overcome inherent limitations of wavelet transform in the time-frequency representation of signals. S transform combines the characteristics of short Fourier transform and wavelet transform, but the window used in S transform is invariable, so, may be unsuitable for some non-stationary procedures, such as seismic signals. The generalized type of this transform is the S-transform with a complex window and phase modulation that has high potential in better time-frequency localization of similar waveforms on the time series. This paper presents a method for damage detection in shear frames on the basis of signal processing using S-transform with complex window and phase modulation (SCW). In this research, the SCW-transform has been employed due to its favorable performance in determination of damage locations and estimation of damage extent. Determining the extent of damage is of significant importance in priority settings and critical management after
seismic events, in order to enhance the safety of a building and its inhabitants. The efficiency of the proposed algorithm was investigated for different damage scenarios and intensities, and, also, in the presence of measurement noise in multi-story shear frames. The method performance has been verified using two numerical examples. By way of comparison between the location and amount of damage obtained from the proposed method and simulation model, it is demonstrated that the method is sensitive to the location and severity of structural damage. In addition to the detection of damage locations, it also gives good estimation of damage severity in both the absence and presence of measurement noise in the recorded signals.