Towards an AI-based Early Warning System for Bridge Scour

by   Negin Yousefpour, et al.

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.


page 5

page 6

page 8

page 9

page 11

page 23

page 24

page 34


Feature-weighted Stacking for Nonseasonal Time Series Forecasts: A Case Study of the COVID-19 Epidemic Curves

We investigate ensembling techniques in forecasting and examine their po...

Deep and Confident Prediction for Time Series at Uber

Reliable uncertainty estimation for time series prediction is critical i...

A data filling methodology for time series based on CNN and (Bi)LSTM neural networks

In the process of collecting data from sensors, several circumstances ca...

Real-time Forecasting of Time Series in Financial Markets Using Sequentially Trained Many-to-one LSTMs

Financial markets are highly complex and volatile; thus, learning about ...

Learning via Long Short-Term Memory (LSTM) network for predicting strains in Railway Bridge members under train induced vibration

Bridge health monitoring using machine learning tools has become an effi...

An Exploratory Study of AI System Risk Assessment from the Lens of Data Distribution and Uncertainty

Deep learning (DL) has become a driving force and has been widely adopte...

NeuralHydrology - Interpreting LSTMs in Hydrology

Despite the huge success of Long Short-Term Memory networks, their appli...

Please sign up or login with your details

Forgot password? Click here to reset