Structural Damage Detection Using Ensemble Empirical Mode Decomposition, Hilbert Transform and Artificial Neural Networks

Abstract: Civil structures are on the verge of changing which leads energy dissipation capacity to decline. 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. Earthquake is a recognized factor in variation of structures condition, inasmuch as inelastic behavior of a building subjected to design level earthquakes is plausible. In this study Hilbert Huang Transformation (HHT) is superseded by Ensemble Empirical Mode decomposition (EEMD) and Hilbert Transform (HT) together. Albeit analogous, EEMD brings more appropriate Intrinsic Mode Functions (IMFs) than Empirical Mode Decomposition (EMD). IMFs are employed to assess first mode frequency and mode shape. Afterward, Artificial Neural Networks (ANN) is applied to predict story acceleration based on acceleration of structure during previous moments. ANN functions precisely. Therefore, any congruency between predicted and measured acceleration provides onset of damage. Then another ANN method is applied to estimate stiffness matrix. Though first mode shape and frequency is calculated in advance, it essentially requires an inverse problem to be solved in order to find stiffness matrix. This task is done by ANN. In other words, these two ANN methods are exercised to forecast location and measure severity of damage respectively. This algorithm is implemented on one nonlinear moment-resisting steel frame and the results are acceptable.