论文标题
地震损害评估的桥梁系数利用深度学习
Cepstral Coefficients for Earthquake Damage Assessment of Bridges Leveraging Deep Learning
论文作者
论文摘要
桥梁是弹性社区中必不可少的要素，这是生命线运输系统的重要部分。关于桥梁结构功能的知识至关重要，尤其是在发生重大地震事件之后。在这项研究中，我们提出了用于自动化AI的桥梁损伤检测的信号处理方法。 MEL量表的滤波器库和sepstral系数用于训练配备封闭式复发单元（GRU）层的深度学习体系结构，这些层考虑信号中的时间变化。拟议的框架已在加利福尼亚的RC桥结构上进行了验证。该桥遭受180个双向地面运动记录，带有采样量表因子和六个不同的截距角。与基准累积强度特征相比，MEL过滤库预测临界漂移比的精度为15.5％。为信号时空分析的开发策略增强了利用深度学习来监测生命线结构的损害诊断框架的鲁棒性。
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.
