Negin Yousefpour

Negin Yousefpour, Oscar Correa

Scour is the number one cause of bridge failure in many parts of the world. Considering the lack of reliability in existing empirical equations for scour depth estimation and the complexity and uncertainty of scour as a physical phenomenon, it is essential to develop more reliable solutions for scour risk assessment. This study introduces a novel AI approach for early forecast of scour based on real-time monitoring data obtained from sonar and stage sensors installed at bridge piers. Long-short Term Memory networks (LSTMs), a prominent Deep Learning algorithm successfully used for time-series forecasting in other fields, were developed and trained using river stage and bed elevation readings for more than 11 years obtained from Alaska scour monitoring program. The capability of the AI models in scour prediction is shown for three case-study bridges. Results show that LSTMs can capture the temporal and seasonal patterns of both flow and river bed variations around bridge piers, through cycles of scour and filling and can provide reasonable predictions of upcoming scour depth as early as seven days in advance. It is expected that the proposed solution can be implemented by transportation authorities for development of emerging AI-based early warning systems, enabling superior bridge scour management.

Negin Yousefpour, Bo Wang

This paper introduces scour physics-inspired neural networks (SPINNs), a hybrid physics-data-driven framework for bridge scour prediction using deep learning. SPINNs integrate physics-based, empirical equations into deep neural networks and are trained using site-specific historical scour monitoring data. Long-short Term Memory Network (LSTM) and Convolutional Neural Network (CNN) are considered as the base deep learning (DL) models. We also explore transferable/general models, trained by aggregating datasets from a cluster of bridges, versus the site/bridge-specific models. Despite variation in performance, SPINNs outperformed pure data-driven models in the majority of cases. In some bridge cases, SPINN reduced forecasting errors by up to 50 percent. The pure data-driven models showed better transferability compared to hybrid models. The transferable DL models particularly proved effective for bridges with limited data. In addition, the calibrated time-dependent empirical equations derived from SPINNs showed great potential for maximum scour depth estimation, providing more accurate predictions compared to commonly used HEC-18 model. Comparing SPINNs with traditional empirical models indicates substantial improvements in scour prediction accuracy. This study can pave the way for further exploration of physics-inspired machine learning methods for scour prediction.

Tahrima Hashem, Negin Yousefpour

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.