Cepstral Coefficients for Earthquake Damage Assessment of Bridges Leveraging Deep Learning

TL;DR

This paper introduces a deep learning framework using cepstral coefficients and Mel-scaled filter banks for automated damage detection in bridges, demonstrating improved accuracy in earthquake damage assessment.

Contribution

It presents a novel application of cepstral coefficients and Mel filter banks with GRU-based deep learning for bridge damage detection post-earthquake, enhancing robustness and accuracy.

Findings

Mel filter banks achieved 15.5% higher accuracy than benchmark features.

The framework effectively captures spatio-temporal signal variations.

Validated on real earthquake data from a California bridge.

Abstract

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.