Deng, Penghao, Jidong J. Yang, and Tien Yee. 2024. "Deep Learning-Based Flood Detection for Bridge Monitoring Using Accelerometer Data" Infrastructures 9, no. 9: 140. https://doi.org/10.3390/infrastructures9090140

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Deng, Penghao, Jidong J. Yang, and Tien Yee. 2024. "Deep Learning-Based Flood Detection for Bridge Monitoring Using Accelerometer Data" Infrastructures 9, no. 9: 140. https://doi.org/10.3390/infrastructures9090140

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Deng P, Yang JJ, Yee T. Deep Learning-Based Flood Detection for Bridge Monitoring Using Accelerometer Data. Infrastructures. 2024; 9(9):140. https://doi.org/10.3390/infrastructures9090140

Deng, P.; Yang, J.J.; Yee, T. Deep Learning-Based Flood Detection for Bridge Monitoring Using Accelerometer Data. Infrastructures 2024, 9, 140. https://doi.org/10.3390/infrastructures9090140

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Deng P, Yang JJ, Yee T. Deep Learning-Based Flood Detection for Bridge Monitoring Using Accelerometer Data. Infrastructures. 2024; 9(9):140. https://doi.org/10.3390/infrastructures9090140

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Deng, P.; Yang, J.J.; Yee, T. Deep Learning-Based Flood Detection for Bridge Monitoring Using Accelerometer Data. Infrastructures 2024, 9, 140. https://doi.org/10.3390/infrastructures9090140

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Abstract: Flooding and consequential scouring are the primary causes of bridge failures, making the detection of such events crucial for structural safety. This study investigates the characteristics of accelerometer data from bridge pier vibrations and proposes a flood detection method with deep learning-based models based on ResNet18 and 1D Convolution architectures. These models were comprehensively evaluated for (1) detecting vehicles passing on bridges and (2) detecting flood events based on axis-specific accelerometer data under various traffic conditions. Continuous Wavelet Transform (CWT) was employed to convert the accelerometer data into richer time-frequency representations, enhancing the detection of passing vehicles. Notably, when vehicles are passing over bridges, the vertical direction exhibits a magnified and more sustained energy distribution across a wider frequency range. Additionally, under flooding conditions, time-frequency representations from the bridge direction reveal a significant increase in energy intensity and continuity compared with non-flooding conditions. For detection of vehicles passing, ResNet18 outperformed the 1D Convolution model, achieving an accuracy of 97.2% compared with 91.4%. For flood detection without vehicles passing, the two models performed similarly well, with accuracies of 97.3% and 98.3%, respectively. However, in scenarios with vehicles passing, the 1D Convolution model excelled, achieving an accuracy of 98.6%, significantly higher than that of ResNet18 (81.6%). This suggests that high-frequency signals, such as vertical vibrations induced by passing vehicles, are better captured by more complex representations (CWT) and models (e.g., ResNet18), while relatively low-frequency signals, such as longitudinal vibrations caused by flooding, can be effectively captured by simpler 1D Convolution over the original signals. Consequentially, the two model types are deployed in a pipeline where the ResNet18 model is used for classifying whether vehicles are passing the bridge, followed by two 1D Convolution models: one trained for detecting flood events under vehicles-passing conditions and the other trained for detecting flood events under no-vehicles-passing conditions. This hierarchical approach provides a robust framework for real-time monitoring of bridge response to vehicle passing and timely warning of flood events, enhancing the potential to reduce bridge collapses and improve public safety. Keywords: bridge safety; flood detection; bridge scour monitoring; accelerometer data; continuous wavelet transform; convolutional neural networks