LAUSR

Abstract The paper presents a novel structural health monitoring approach based on the wavelet energy of admittance signals to detect, localize and estimate the severity of the damages caused by fatigue in reinforced concrete (RC) frames. Admittance signatures are acquired in user-defined frequency ranges experimentally using six piezo-cement composite disks (embedded at different locations in RC specimens) at various stages during fatigue testing of the frame on a shake table. For purpose of estimating wavelet energies, discrete wavelet transformation (DWT), continuous wavelet transformation (CWT) and power spectral density (PSD) analysis are applied on real-admittance signals (conductance) in frequency domains. Mathematical models based on global dynamic and wavelet energy approach are developed and validated in order to estimate residual life of RC frames. Overall, the experimental results verify the superior performance of the DWT based optimum methodology in enabling full-fledged real-time damage prognosis of RC structures under low-strain and high-cycle fatigue.