Online nonlinear structural damage detection using Hilbert Huang transform and artificial neural networks

Structural Health Monitoring (SHM) as a process in order to implement a damage detection strategy and assess the condition of structure plays a key role in structural reliability. In this paper, we aim to present a methodology for online detection of damages which may occur during a strong ground excitation. In this regard, Empirical Mode Decomposition (EMD) is superseded by Ensemble Empirical Mode Decomposition (EEMD) in the Hilbert Huang Transformation (HHT). Albeit analogous, EEMD brings about more appropriate Intrinsic Mode Functions (IMFs) than EMD. IMFs are employed to assess the first mode frequency and mode shape. Afterward, Artificial Neural Network (ANN) is applied to predict story acceleration based on previously measured values. Because ANN functions precisely, any congruency between predicted and measured acceleration indicates onset of damage. Then, another ANN method is applied to estimate the stiffness matrix. Though the first mode shape and frequency are calculated in advance, the process essentially requires an inverse problem to be solved in order to find stiffness matrix, which is done by ANN. This algorithm is implemented on moment-resisting steel frames, and the results show that the proposed methodology is reliable for online prediction of structural damage.