  Scour around bridge piers is a critical challenge for infrastructures around
the world. In the absence of analytical models and due to the complexity of the
scour process, it is difficult for current empirical methods to achieve
accurate predictions. In this paper, we exploit the power of deep learning
algorithms to forecast the scour depth variations around bridge piers based on
historical sensor monitoring data, including riverbed elevation, flow
elevation, and flow velocity. We investigated the performance of Long
Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models for
real-time scour forecasting using data collected from bridges in Alaska and
Oregon from 2006 to 2021. The LSTM models achieved mean absolute error (MAE)
ranging from 0.1m to 0.5m for predicting bed level variations a week in
advance, showing a reasonable performance. The Fully Convolutional Network
(FCN) variant of CNN outperformed other CNN configurations, showing a
comparable performance to LSTMs with significantly lower computational costs.
We explored various innovative random-search heuristics for hyperparameter
tuning and model optimisation which resulted in reduced computational cost
compared to grid-search method. The impact of different combinations of sensor
features on scour prediction showed the significance of the historical time
series of scour for predicting upcoming events. Overall, this study provides a
greater understanding of the potential of Deep Learning algorithms for
real-time scour prediction and early warning for bridges with distinct geology,
geomorphology and flow characteristics.
