  Bridges are indispensable elements in resilient communities as essential
parts of the lifeline transportation systems. Knowledge about the functionality
of bridge structures is crucial, especially after a major earthquake event. In
this study, we propose signal processing approaches for automated AI-equipped
damage detection of bridges. Mel-scaled filter banks and cepstral coefficients
are utilized for training a deep learning architecture equipped with Gated
Recurrent Unit (GRU) layers that consider the temporal variations in a signal.
The proposed framework has been validated on an RC bridge structure in
California. The bridge is subjected to 180 bi-directional ground motion records
with sampled scale factors and six different intercept angles. Compared with
the benchmark cumulative intensity features, the Mel filter banks resulted in
15.5% accuracy in predicting critical drift ratios. The developed strategy for
spatio-temporal analysis of signals enhances the robustness of damage diagnosis
frameworks that utilize deep learning for monitoring lifeline structures.
