A novel unsupervised deep learning model for global and local health condition assessment of structures. (1st February 2018) - Rafiei, Mohammad Hossein; Adeli, Hojjat
Highlights: Novel model for vibration-based structural health condition assessment of structures. Incorporates synchrosqueezed wavelet transform, FFT, and deep Boltzmann machine. A new structural health index to assess global and local health conditions. No need for costly experimental results from a scaled version of the structure. Illustrated using the data obtained from a scaled 38-story RC building structure. Abstract: A methodology is described for global and local health condition assessment of structural systems using ambient vibration response of the structure collected by sensors. The model incorporates synchrosqueezed wavelet transform, Fast Fourier Transform, and unsupervised deep Boltzmann machine to extract features from the frequency domain of the recorded signals. A probability density function is used to create a structural health index (SHI). This index can be used to assess both the global and local health conditions of the structure. A beauty of the proposed model is that it does not require costly experimental results to be obtained from a scaled version of the structure to simulate different damage states of the structure. Only ambient vibrations of the healthy structure are needed. In the absence of ambient vibrations, they can be simulated stochastically using structural properties and the probability theory. The effectiveness of the proposed model is illustrated employing experimental data obtained on a shake table in Hong Kong.