  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Hilbert Huang Transformation (HHT) is superseded by Ensemble Empirical Mode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.
