Project Summary
This project aims to develop a tool for early warning and real time scour risk assessment of bridges exploiting information from flood forecasting, hydraulic modelling, and low-cost flow sensors.
Project Achievements
Activities WP1: Evaluation of the accuracy of existing scour formulae using available laboratory and field data and quantification of the statistical parameters based on the observed scatter between the predicted and measured pier scour depths. WP2: Use of data-driven machine learning such as Artificial Neural Network for an accurate prediction of the bridge scour depth. WP3: Development of a tool for assessing in real time the risk of exceedance of critical scour levels using low-cost equipment (IP camera).
Conclusions
The tools proposed and tested in this project allow to continuously and remotely monitor some critical hydraulic features of the flow (surface, velocity, depth) that are strongly correlated to those controlling bridge scour. The monitoring data, combined with advanced, time-dependant hydraulic and scour models, permit to achieve near real-time estimates of bridge scour. The developed tools can help Transport Agencies and Operators to make measured- informed decisions concerning bridge scour risk management, thus improving current practice.
Next Steps
• Apply for funding to develop a digital platform based on the proposed tools and models • Submit a larger research proposal on digital twin models for bridge flood risk assessment • Disseminate the obtained results to the scientific community and Transport Agencies and Operators • Engage with Strathclyde Inspire to explore commercialisation of the technology • Test the developed tools under more extreme flood events, start continuous monitoring at the case study bridge.
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